pax_global_header00006660000000000000000000000064141324740260014515gustar00rootroot0000000000000052 comment=04d377f321972801888381356cb6259766eb63b6 geopandas-0.10.2/000077500000000000000000000000001413247402600135365ustar00rootroot00000000000000geopandas-0.10.2/.coveragerc000066400000000000000000000004011413247402600156520ustar00rootroot00000000000000[report] exclude_lines = pragma: no cover def __repr__ raise AssertionError raise NotImplementedError if __name__ == .__main__.: omit = geopandas/tests/* geopandas/io/tests/* geopandas/tools/tests/* geopandas/_version.py geopandas-0.10.2/.gitattributes000066400000000000000000000000431413247402600164260ustar00rootroot00000000000000geopandas/_version.py export-subst geopandas-0.10.2/.github/000077500000000000000000000000001413247402600150765ustar00rootroot00000000000000geopandas-0.10.2/.github/ISSUE_TEMPLATE/000077500000000000000000000000001413247402600172615ustar00rootroot00000000000000geopandas-0.10.2/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000016741413247402600217630ustar00rootroot00000000000000--- name: Bug Report about: Create a bug report to help us improve geopandas title: "BUG:" labels: "bug, needs triage" --- - [ ] I have checked that this issue has not already been reported. - [ ] I have confirmed this bug exists on the latest version of geopandas. - [ ] (optional) I have confirmed this bug exists on the master branch of geopandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Your code here ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] #### Expected Output #### Output of ``geopandas.show_versions()``
[paste the output of ``geopandas.show_versions()`` here leaving a blank line after the details tag]
geopandas-0.10.2/.github/ISSUE_TEMPLATE/feature_request.md000066400000000000000000000016351413247402600230130ustar00rootroot00000000000000--- name: Feature Request about: Suggest an idea for geopandas title: "ENH:" labels: "enhancement" --- #### Is your feature request related to a problem? [this should provide a description of what the problem is, e.g. "I wish I could use geopandas to do [...]"] #### Describe the solution you'd like [this should provide a description of the feature request, e.g. "`GeoDataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature] #### API breaking implications [this should provide a description of how this feature will affect the API] #### Describe alternatives you've considered [this should provide a description of any alternative solutions or features you've considered] #### Additional context [add any other context, code examples, or references to existing implementations about the feature request here] ```python # Your code here, if applicable ``` geopandas-0.10.2/.github/ISSUE_TEMPLATE/installation_issue.md000066400000000000000000000015071413247402600235170ustar00rootroot00000000000000--- name: Installation Issue about: Ask about installing geopandas title: "" labels: "installation" --- - [ ] I have read the [documentation on installation](https://geopandas.org/install.html) and followed the instructions provided. - [ ] I have looked through [issues labeled "installation"](https://github.com/geopandas/geopandas/labels/installation) in the geopandas repo. - If your issue is related to installation using the `conda-forge` channel, please open an issue in [geopandas-feedstock repository](https://github.com/conda-forge/geopandas-feedstock) instead. --- #### System information [what operating system do you have and what package management system are you using] #### Environment details
[if using conda, paste the output of `conda info` and `conda list`; if using pip, `pip freeze`]
geopandas-0.10.2/.github/ISSUE_TEMPLATE/submit_question.md000066400000000000000000000014341413247402600230370ustar00rootroot00000000000000--- name: Submit Question about: Ask a general question about geopandas title: "QST:" labels: "question" --- - [ ] I have searched the [geopandas] tag on [StackOverflow](https://stackoverflow.com/questions/tagged/geopandas) and [GIS StackExchange](https://gis.stackexchange.com/questions/tagged/geopandas) for similar questions. - [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com) or [GIS StackExhange](https://gis.stackexchange.com). --- #### Question about geopandas **Note**: If you'd still like to submit a question, please read [this guide]( https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question. ```python # Your code here, if applicable ``` geopandas-0.10.2/.github/workflows/000077500000000000000000000000001413247402600171335ustar00rootroot00000000000000geopandas-0.10.2/.github/workflows/release_to_pypi.yml000066400000000000000000000032451413247402600230450ustar00rootroot00000000000000name: Publish geopandas to PyPI / GitHub on: push: tags: - "v*" jobs: build-n-publish: name: Build and publish geopandas to PyPI runs-on: ubuntu-latest steps: - name: Checkout source uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: "3.x" - name: Build a binary wheel and a source tarball run: | python -m pip install --upgrade pip pip install setuptools wheel python setup.py sdist bdist_wheel - name: Publish distribution to PyPI uses: pypa/gh-action-pypi-publish@master with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} - name: Create GitHub Release id: create_release uses: actions/create-release@v1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token with: tag_name: ${{ github.ref }} release_name: ${{ github.ref }} draft: false prerelease: false - name: Get Asset name run: | export PKG=$(ls dist/ | grep tar) set -- $PKG echo "name=$1" >> $GITHUB_ENV - name: Upload Release Asset (sdist) to GitHub id: upload-release-asset uses: actions/upload-release-asset@v1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: upload_url: ${{ steps.create_release.outputs.upload_url }} asset_path: dist/${{ env.name }} asset_name: ${{ env.name }} asset_content_type: application/zip geopandas-0.10.2/.github/workflows/tests.yaml000066400000000000000000000072451413247402600211710ustar00rootroot00000000000000name: Tests on: push: branches: [master] pull_request: branches: [master] schedule: - cron: "0 0 * * *" concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true jobs: Linting: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 - uses: pre-commit/action@v2.0.0 Test: needs: Linting name: ${{ matrix.os }}, ${{ matrix.env }} runs-on: ${{ matrix.os }} defaults: run: shell: bash -l {0} strategy: fail-fast: false matrix: os: [ubuntu-latest] postgis: [false] dev: [false] env: - ci/envs/37-minimal.yaml - ci/envs/38-no-optional-deps.yaml - ci/envs/37-pd10.yaml - ci/envs/37-latest-defaults.yaml - ci/envs/37-latest-conda-forge.yaml - ci/envs/38-latest-conda-forge.yaml - ci/envs/39-latest-conda-forge.yaml include: - env: ci/envs/37-latest-conda-forge.yaml os: macos-latest postgis: false dev: false - env: ci/envs/38-latest-conda-forge.yaml os: macos-latest postgis: false dev: false - env: ci/envs/37-latest-conda-forge.yaml os: windows-latest postgis: false dev: false - env: ci/envs/38-latest-conda-forge.yaml os: windows-latest postgis: false dev: false - env: ci/envs/38-dev.yaml os: ubuntu-latest dev: true steps: - uses: actions/checkout@v2 - name: Setup Conda uses: conda-incubator/setup-miniconda@v2 with: environment-file: ${{ matrix.env }} - name: Check and Log Environment run: | python -V python -c "import geopandas; geopandas.show_versions();" conda info # save conda list to file and print out # so that we can do the HAS_PYGEOS check without calling conda again conda list 2>&1 | tee conda.txt if ( cat conda.txt | grep -q pygeos ) then echo "Setting HAS_PYGEOS=1" echo "HAS_PYGEOS=1" >> $GITHUB_ENV else echo "Setting HAS_PYGEOS=0" echo "HAS_PYGEOS=0" >> $GITHUB_ENV fi - name: Test without PyGEOS env: USE_PYGEOS: 0 run: | pytest -v -r s -n auto --color=yes --cov=geopandas --cov-append --cov-report term-missing --cov-report xml geopandas/ - name: Test with PyGEOS if: env.HAS_PYGEOS == 1 env: USE_PYGEOS: 1 run: | pytest -v -r s -n auto --color=yes --cov=geopandas --cov-append --cov-report term-missing --cov-report xml geopandas/ - name: Test with PostGIS if: contains(matrix.env, '38-latest-conda-forge.yaml') && contains(matrix.os, 'ubuntu') env: PGUSER: postgres PGPASSWORD: postgres PGHOST: "127.0.0.1" PGPORT: 5432 run: | conda install postgis -c conda-forge sh ci/scripts/setup_postgres.sh pytest -v -r s --color=yes --cov=geopandas --cov-append --cov-report term-missing --cov-report xml geopandas/io/tests/test_sql.py | tee /dev/stderr | if grep SKIPPED >/dev/null;then echo "TESTS SKIPPED, FAILING" && exit 1;fi - name: Test docstrings if: contains(matrix.env, '38-latest-conda-forge.yaml') && contains(matrix.os, 'ubuntu') env: USE_PYGEOS: 1 run: | pytest -v --color=yes --doctest-only geopandas --ignore=geopandas/datasets - uses: codecov/codecov-action@v1 geopandas-0.10.2/.gitignore000066400000000000000000000014051413247402600155260ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ venv*/ build/ dist/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ result_images # Sphinx documentation doc/_build/ .buildinfo # Jupyter Notebook .ipynb_checkpoints # pytest .pytest_cache .mypy_cache # projects .spyderproject .spyproject .idea .ropeproject .vscode *.py~ .DS_Store examples/nybb_*.zip doc/source/savefig doc/source/reference doc/source/docs/reference/api/*.rst geopandas.egg-info geopandas/version.py .asv doc/source/getting_started/my_file.geojson geopandas-0.10.2/.pre-commit-config.yaml000066400000000000000000000004121413247402600200140ustar00rootroot00000000000000files: 'geopandas\/' repos: - repo: https://github.com/python/black rev: 20.8b1 hooks: - id: black language_version: python3 - repo: https://gitlab.com/pycqa/flake8 rev: 3.8.3 hooks: - id: flake8 language: python_venv geopandas-0.10.2/CHANGELOG.md000066400000000000000000000752351413247402600153630ustar00rootroot00000000000000Changelog ========= Version 0.10.2 (October 16, 2021) --------------------------------- Small bug-fix release: - Fix regression in `overlay()` in case no geometries are intersecting (but have overlapping total bounds) (#2172). - Fix regression in `overlay()` with `keep_geom_type=True` in case the overlay of two geometries in a GeometryCollection with other geometry types (#2177). - Fix `overlay()` to honor the `keep_geom_type` keyword for the `op="differnce"` case (#2164). - Fix regression in `plot()` with a mapclassify `scheme` in case the formatted legend labels have duplicates (#2166). - Fix a bug in the `explore()` method ignoring the `vmin` and `vmax` keywords in case they are set to 0 (#2175). - Fix `unary_union` to correctly handle a GeoSeries with missing values (#2181). - Avoid internal deprecation warning in `clip()` (#2179). Version 0.10.1 (October 8, 2021) -------------------------------- Small bug-fix release: - Fix regression in `overlay()` with non-overlapping geometries and a non-default `how` (i.e. not "intersection") (#2157). Version 0.10.0 (October 3, 2021) -------------------------------- Highlights of this release: - A new `sjoin_nearest()` method to join based on proximity, with the ability to set a maximum search radius (#1865). In addition, the `sindex` attribute gained a new method for a "nearest" spatial index query (#1865, #2053). - A new `explore()` method on GeoDataFrame and GeoSeries with native support for interactive visualization based on folium / leaflet.js (#1953) - The `geopandas.sjoin()`/`overlay()`/`clip()` functions are now also available as methods on the GeoDataFrame (#2141, #1984, #2150). New features and improvements: - Add support for pandas' `value_counts()` method for geometry dtype (#2047). - The `explode()` method has a new `ignore_index` keyword (consistent with pandas' explode method) to reset the index in the result, and a new `index_parts` keywords to control whether a cumulative count indexing the parts of the exploded multi-geometries should be added (#1871). - `points_from_xy()` is now available as a GeoSeries method `from_xy` (#1936). - The `to_file()` method will now attempt to detect the driver (if not specified) based on the extension of the provided filename, instead of defaulting to ESRI Shapefile (#1609). - Support for the `storage_options` keyword in `read_parquet()` for specifying filesystem-specific options (e.g. for S3) based on fsspec (#2107). - The read/write functions now support `~` (user home directory) expansion (#1876). - Support the `convert_dtypes()` method from pandas to preserve the GeoDataFrame class (#2115). - Support WKB values in the hex format in `GeoSeries.from_wkb()` (#2106). - Update the `estimate_utm_crs()` method to handle crossing the antimeridian with pyproj 3.1+ (#2049). - Improved heuristic to decide how many decimals to show in the repr based on whether the CRS is projected or geographic (#1895). - Switched the default for `geocode()` from GeoCode.Farm to the Photon geocoding API (https://photon.komoot.io) (#2007). Deprecations and compatibility notes: - The `op=` keyword of `sjoin()` to indicate which spatial predicate to use for joining is being deprecated and renamed in favor of a new `predicate=` keyword (#1626). - The `cascaded_union` attribute is deprecated, use `unary_union` instead (#2074). - Constructing a GeoDataFrame with a duplicated "geometry" column is now disallowed. This can also raise an error in the `pd.concat(.., axis=1)` function if this results in duplicated active geometry columns (#2046). - The `explode()` method currently returns a GeoSeries/GeoDataFrame with a MultiIndex, with an additional level with indices of the parts of the exploded multi-geometries. For consistency with pandas, this will change in the future and the new `index_parts` keyword is added to control this. Bug fixes: - Fix in the `clip()` function to correctly clip MultiPoints instead of leaving them intact when partly outside of the clip bounds (#2148). - Fix `GeoSeries.isna()` to correctly return a boolean Series in case of an empty GeoSeries (#2073). - Fix the GeoDataFrame constructor to preserve the geometry name when the argument is already a GeoDataFrame object (i.e. `GeoDataFrame(gdf)`) (#2138). - Fix loss of the values' CRS when setting those values as a column (`GeoDataFrame.__setitem__`) (#1963) - Fix in `GeoDataFrame.apply()` to preserve the active geometry column name (#1955). - Fix in `sjoin()` to not ignore the suffixes in case of a right-join (`how="right`) (#2065). - Fix `GeoDataFrame.explode()` with a MultiIndex (#1945). - Fix the handling of missing values in `to/from_wkb` and `to_from_wkt` (#1891). - Fix `to_file()` and `to_json()` when DataFrame has duplicate columns to raise an error (#1900). - Fix bug in the colors shown with user-defined classification scheme (#2019). - Fix handling of the `path_effects` keyword in `plot()` (#2127). - Fix `GeoDataFrame.explode()` to preserve `attrs` (#1935) Notes on (optional) dependencies: - GeoPandas 0.10.0 dropped support for Python 3.6 and pandas 0.24. Further, the minimum required versions are numpy 1.18, shapely 1.6, fiona 1.8, matplotlib 3.1 and pyproj 2.2. - Plotting with a classification schema now requires mapclassify version >= 2.4 (#1737). - Compatibility fixes for the latest numpy in combination with Shapely 1.7 (#2072) - Compatibility fixes for the upcoming Shapely 1.8 (#2087). - Compatibility fixes for the latest PyGEOS (#1872, #2014) and matplotlib (colorbar issue, #2066). Version 0.9.0 (February 28, 2021) --------------------------------- Many documentation improvements and a restyled and restructured website with a new logo (#1564, #1579, #1617, #1668, #1731, #1750, #1757, #1759). New features and improvements: - The `geopandas.read_file` function now accepts more general file-like objects (e.g. `fsspec` open file objects). It will now also automatically recognize zipped files (#1535). - The `GeoDataFrame.plot()` method now provides access to the pandas plotting functionality for the non-geometry columns, either using the `kind` keyword or the accessor method (e.g. `gdf.plot(kind="bar")` or `gdf.plot.bar()`) (#1465). - New `from_wkt()`, `from_wkb()`, `to_wkt()`, `to_wkb()` methods for GeoSeries to construct a GeoSeries from geometries in WKT or WKB representation, or to convert a GeoSeries to a pandas Seriew with WKT or WKB values (#1710). - New `GeoSeries.z` attribute to access the z-coordinates of Point geometries (similar to the existing `.x` and `.y` attributes) (#1773). - The `to_crs()` method now handles missing values (#1618). - Support for pandas' new `.attrs` functionality (#1658). - The `dissolve()` method now allows dissolving by no column (`by=None`) to create a union of all geometries (single-row GeoDataFrame) (#1568). - New `estimate_utm_crs()` method on GeoSeries/GeoDataFrame to determine the UTM CRS based on the bounds (#1646). - `GeoDataFrame.from_dict()` now accepts `geometry` and `crs` keywords (#1619). - `GeoDataFrame.to_postgis()` and `geopandas.read_postgis()` now supports both sqlalchemy engine and connection objects (#1638). - The `GeoDataFrame.explode()` method now allows exploding based on a non-geometry column, using the pandas implementation (#1720). - Performance improvement in `GeoDataFrame/GeoSeries.explode()` when using the PyGEOS backend (#1693). - The binary operation and predicate methods (eg `intersection()`, `intersects()`) have a new `align` keyword which allows optionally not aligning on the index before performing the operation with `align=False` (#1668). - The `GeoDataFrame.dissolve()` method now supports all relevant keywords of `groupby()`, i.e. the `level`, `sort`, `observed` and `dropna` keywords (#1845). - The `geopandas.overlay()` function now accepts `make_valid=False` to skip the step to ensure the input geometries are valid using `buffer(0)` (#1802). - The `GeoDataFrame.to_json()` method gained a `drop_id` keyword to optionally not write the GeoDataFrame's index as the "id" field in the resulting JSON (#1637). - A new `aspect` keyword in the plotting methods to optionally allow retaining the original aspect (#1512) - A new `interval` keyword in the `legend_kwds` group of the `plot()` method to control the appearance of the legend labels when using a classification scheme (#1605). - The spatial index of a GeoSeries (accessed with the `sindex` attribute) is now stored on the underlying array. This ensures that the spatial index is preserved in more operations where possible, and that multiple geometry columns of a GeoDataFrame can each have a spatial index (#1444). - Addition of a `has_sindex` attribute on the GeoSeries/GeoDataFrame to check if a spatial index has already been initialized (#1627). - The `geopandas.testing.assert_geoseries_equal()` and `assert_geodataframe_equal()` testing utilities now have a `normalize` keyword (False by default) to normalize geometries before comparing for equality (#1826). Those functions now also give a more informative error message when failing (#1808). Deprecations and compatibility notes: - The `is_ring` attribute currently returns True for Polygons. In the future, this will be False (#1631). In addition, start to check it for LineStrings and LinearRings (instead of always returning False). - The deprecated `objects` keyword in the `intersection()` method of the `GeoDataFrame/GeoSeries.sindex` spatial index object has been removed (#1444). Bug fixes: - Fix regression in the `plot()` method raising an error with empty geometries (#1702, #1828). - Fix `geopandas.overlay()` to preserve geometries of the correct type which are nested within a GeometryCollection as a result of the overlay operation (#1582). In addition, a warning will now be raised if geometries of different type are dropped from the result (#1554). - Fix the repr of an empty GeoSeries to not show spurious warnings (#1673). - Fix the `.crs` for empty GeoDataFrames (#1560). - Fix `geopandas.clip` to preserve the correct geometry column name (#1566). - Fix bug in `plot()` method when using `legend_kwds` with multiple subplots (#1583) - Fix spurious warning with `missing_kwds` keyword of the `plot()` method when there are no areas with missing data (#1600). - Fix the `plot()` method to correctly align values passed to the `column` keyword as a pandas Series (#1670). - Fix bug in plotting MultiPoints when passing values to determine the color (#1694) - The `rename_geometry()` method now raises a more informative error message when a duplicate column name is used (#1602). - Fix `explode()` method to preserve the CRS (#1655) - Fix the `GeoSeries.apply()` method to again accept the `convert_dtype` keyword to be consistent with pandas (#1636). - Fix `GeoDataFrame.apply()` to preserve the CRS when possible (#1848). - Fix bug in containment test as `geom in geoseries` (#1753). - The `shift()` method of a GeoSeries/GeoDataFrame now preserves the CRS (#1744). - The PostGIS IO functionality now quotes table names to ensure it works with case-sensitive names (#1825). - Fix the `GeoSeries` constructor without passing data but only an index (#1798). Notes on (optional) dependencies: - GeoPandas 0.9.0 dropped support for Python 3.5. Further, the minimum required versions are pandas 0.24, numpy 1.15 and shapely 1.6 and fiona 1.8. - The `descartes` package is no longer required for plotting polygons. This functionality is now included by default in GeoPandas itself, when matplotlib is available (#1677). - Fiona is now only imported when used in `read_file`/`to_file`. This means you can now force geopandas to install without fiona installed (although it is still a default requirement) (#1775). - Compatibility with the upcoming Shapely 1.8 (#1659, #1662, #1819). Version 0.8.2 (January 25, 2021) -------------------------------- Small bug-fix release for compatibility with PyGEOS 0.9. Version 0.8.1 (July 15, 2020) ----------------------------- Small bug-fix release: - Fix a regression in the `plot()` method when visualizing with a JenksCaspallSampled or FisherJenksSampled scheme (#1486). - Fix spurious warning in `GeoDataFrame.to_postgis` (#1497). - Fix the un-pickling with `pd.read_pickle` of files written with older GeoPandas versions (#1511). Version 0.8.0 (June 24, 2020) ----------------------------- **Experimental**: optional use of PyGEOS to speed up spatial operations (#1155). PyGEOS is a faster alternative for Shapely (being contributed back to a future version of Shapely), and is used in element-wise spatial operations and for spatial index in e.g. `sjoin` (#1343, #1401, #1421, #1427, #1428). See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html#using-the-optional-pygeos-dependency) for more info and how to enable it. New features and improvements: - IO enhancements: - New `GeoDataFrame.to_postgis()` method to write to PostGIS database (#1248). - New Apache Parquet and Feather file format support (#1180, #1435) - Allow appending to files with `GeoDataFrame.to_file` (#1229). - Add support for the `ignore_geometry` keyword in `read_file` to only read the attribute data. If set to True, a pandas DataFrame without geometry is returned (#1383). - `geopandas.read_file` now supports reading from file-like objects (#1329). - `GeoDataFrame.to_file` now supports specifying the CRS to write to the file (#802). By default it still uses the CRS of the GeoDataFrame. - New `chunksize` keyword in `geopandas.read_postgis` to read a query in chunks (#1123). - Improvements related to geometry columns and CRS: - Any column of the GeoDataFrame that has a "geometry" dtype is now returned as a GeoSeries. This means that when having multiple geometry columns, not only the "active" geometry column is returned as a GeoSeries, but also accessing another geometry column (`gdf["other_geom_column"]`) gives a GeoSeries (#1336). - Multiple geometry columns in a GeoDataFrame can now each have a different CRS. The global `gdf.crs` attribute continues to returns the CRS of the "active" geometry column. The CRS of other geometry columns can be accessed from the column itself (eg `gdf["other_geom_column"].crs`) (#1339). - New `set_crs()` method on GeoDataFrame/GeoSeries to set the CRS of naive geometries (#747). - Improvements related to plotting: - The y-axis is now scaled depending on the center of the plot when using a geographic CRS, instead of using an equal aspect ratio (#1290). - When passing a column of categorical dtype to the `column=` keyword of the GeoDataFrame `plot()`, we now honor all categories and its order (#1483). In addition, a new `categories` keyword allows to specify all categories and their order otherwise (#1173). - For choropleths using a classification scheme (using `scheme=`), the `legend_kwds` accept two new keywords to control the formatting of the legend: `fmt` with a format string for the bin edges (#1253), and `labels` to pass fully custom class labels (#1302). - New `covers()` and `covered_by()` methods on GeoSeries/GeoDataframe for the equivalent spatial predicates (#1460, #1462). - GeoPandas now warns when using distance-based methods with data in a geographic projection (#1378). Deprecations: - When constructing a GeoSeries or GeoDataFrame from data that already has a CRS, a deprecation warning is raised when both CRS don't match, and in the future an error will be raised in such a case. You can use the new `set_crs` method to override an existing CRS. See [the docs](https://geopandas.readthedocs.io/en/latest/projections.html#projection-for-multiple-geometry-columns). - The helper functions in the `geopandas.plotting` module are deprecated for public usage (#656). - The `geopandas.io` functions are deprecated, use the top-level `read_file` and `to_file` instead (#1407). - The set operators (`&`, `|`, `^`, `-`) are deprecated, use the `intersection()`, `union()`, `symmetric_difference()`, `difference()` methods instead (#1255). - The `sindex` for empty dataframe will in the future return an empty spatial index instead of `None` (#1438). - The `objects` keyword in the `intersection` method of the spatial index returned by the `sindex` attribute is deprecated and will be removed in the future (#1440). Bug fixes: - Fix the `total_bounds()` method to ignore missing and empty geometries (#1312). - Fix `geopandas.clip` when masking with non-overlapping area resulting in an empty GeoDataFrame (#1309, #1365). - Fix error in `geopandas.sjoin` when joining on an empty geometry column (#1318). - CRS related fixes: `pandas.concat` preserves CRS when concatenating GeoSeries objects (#1340), preserve the CRS in `geopandas.clip` (#1362) and in `GeoDataFrame.astype` (#1366). - Fix bug in `GeoDataFrame.explode()` when 'level_1' is one of the column names (#1445). - Better error message when rtree is not installed (#1425). - Fix bug in `GeoSeries.equals()` (#1451). - Fix plotting of multi-part geometries with additional style keywords (#1385). And we now have a [Code of Conduct](https://github.com/geopandas/geopandas/blob/master/CODE_OF_CONDUCT.md)! GeoPandas 0.8.0 is the last release to support Python 3.5. The next release will require Python 3.6, pandas 0.24, numpy 1.15 and shapely 1.6 or higher. Version 0.7.0 (February 16, 2020) --------------------------------- Support for Python 2.7 has been dropped. GeoPandas now works with Python >= 3.5. The important API change of this release is that GeoPandas now requires PROJ > 6 and pyproj > 2.2, and that the `.crs` attribute of a GeoSeries and GeoDataFrame no longer stores the CRS information as a proj4 string or dict, but as a ``pyproj.CRS`` object (#1101). This gives a better user interface and integrates improvements from pyproj and PROJ 6, but might also require some changes in your code. Check the [migration guide](https://geopandas.readthedocs.io/en/latest/projections.html#upgrading-to-geopandas-0-7-with-pyproj-2-2-and-proj-6) in the documentation. Other API changes; - The `GeoDataFrame.to_file` method will now also write the GeoDataFrame index to the file, if the index is named and/or non-integer. You can use the `index=True/False` keyword to overwrite this default inference (#1059). New features and improvements: - A new `geopandas.clip` function to clip a GeoDataFrame to the spatial extent of another shape (#1128). - The `geopandas.overlay` function now works for all geometry types, including points and linestrings in addition to polygons (#1110). - The `plot()` method gained support for missing values (in the column that determines the colors). By default it doesn't plot the corresponding geometries, but using the new `missing_kwds` argument you can specify how to style those geometries (#1156). - The `plot()` method now also supports plotting GeometryCollection and LinearRing objects (#1225). - Added support for filtering with a geometry or reading a subset of the rows in `geopandas.read_file` (#1160). - Added support for the new nullable integer data type of pandas in `GeoDataFrame.to_file` (#1220). Bug fixes: - `GeoSeries.reset_index()` now correctly results in a GeoDataFrame instead of DataFrame (#1252). - Fixed the `geopandas.sjoin` function to handle MultiIndex correctly (#1159). - Fixed the `geopandas.sjoin` function to preserve the index name of the left GeoDataFrame (#1150). Version 0.6.3 (February 6, 2020) --------------------------------- Small bug-fix release: - Compatibility with Shapely 1.7 and pandas 1.0 (#1244). - Fix `GeoDataFrame.fillna` to accept non-geometry values again when there are no missing values in the geometry column. This should make it easier to fill the numerical columns of the GeoDataFrame (#1279). Version 0.6.2 (November 18, 2019) --------------------------------- Small bug-fix release fixing a few regressions: - Fix a regression in passing an array of RRB(A) tuples to the ``.plot()`` method (#1178, #1211). - Fix the ``bounds`` and ``total_bounds`` attributes for empty GeoSeries, which also fixes the repr of an empty or all-NA GeoSeries (#1184, #1195). - Fix filtering of a GeoDataFrame to preserve the index type when ending up with an empty result (#1190). Version 0.6.1 (October 12, 2019) -------------------------------- Small bug-fix release fixing a few regressions: - Fix `astype` when converting to string with Multi geometries (#1145) or when converting a dataframe without geometries (#1144). - Fix `GeoSeries.fillna` to accept `np.nan` again (#1149). Version 0.6.0 (September 27, 2019) ---------------------------------- Important note! This will be the last release to support Python 2.7 (#1031) API changes: - A refactor of the internals based on the pandas ExtensionArray interface (#1000). The main user visible changes are: - The `.dtype` of a GeoSeries is now a `'geometry'` dtype (and no longer a numpy `object` dtype). - The `.values` of a GeoSeries now returns a custom `GeometryArray`, and no longer a numpy array. To get back a numpy array of Shapely scalars, you can convert explicitly using `np.asarray(..)`. - The `GeoSeries` constructor now raises a warning when passed non-geometry data. Currently the constructor falls back to return a pandas `Series`, but in the future this will raise an error (#1085). - The missing value handling has been changed to now separate the concepts of missing geometries and empty geometries (#601, 1062). In practice this means that (see [the docs](https://geopandas.readthedocs.io/en/v0.6.0/missing_empty.html) for more details): - `GeoSeries.isna` now considers only missing values, and if you want to check for empty geometries, you can use `GeoSeries.is_empty` (`GeoDataFrame.isna` already only looked at missing values). - `GeoSeries.dropna` now actually drops missing values (before it didn't drop either missing or empty geometries) - `GeoSeries.fillna` only fills missing values (behaviour unchanged). - `GeoSeries.align` uses missing values instead of empty geometries by default to fill non-matching index entries. New features and improvements: - Addition of a `GeoSeries.affine_transform` method, equivalent of Shapely's function (#1008). - Addition of a `GeoDataFrame.rename_geometry` method to easily rename the active geometry column (#1053). - Addition of `geopandas.show_versions()` function, which can be used to give an overview of the installed libraries in bug reports (#899). - The `legend_kwds` keyword of the `plot()` method can now also be used to specify keywords for the color bar (#1102). - Performance improvement in the `sjoin()` operation by re-using existing spatial index of the input dataframes, if available (#789). - Updated documentation to work with latest version of geoplot and contextily (#1044, #1088). - A new ``geopandas.options`` configuration, with currently a single option to control the display precision of the coordinates (``options.display_precision``). The default is now to show less coordinates (3 for projected and 5 for geographic coordinates), but the default can be overridden with the option. Bug fixes: - Also try to use `pysal` instead of `mapclassify` if available (#1082). - The `GeoDataFrame.astype()` method now correctly returns a `GeoDataFrame` if the geometry column is preserved (#1009). - The `to_crs` method now uses `always_xy=True` to ensure correct lon/lat order handling for pyproj>=2.2.0 (#1122). - Fixed passing list-like colors in the `plot()` method in case of "multi" geometries (#1119). - Fixed the coloring of shapes and colorbar when passing a custom `norm` in the `plot()` method (#1091, #1089). - Fixed `GeoDataFrame.to_file` to preserve VFS file paths (e.g. when a "s3://" path is specified) (#1124). - Fixed failing case in ``geopandas.sjoin`` with empty geometries (#1138). In addition, the minimum required versions of some dependencies have been increased: GeoPandas now requirs pandas >=0.23.4 and matplotlib >=2.0.1 (#1002). Version 0.5.1 (July 11, 2019) ----------------------------- - Compatibility with latest mapclassify version 2.1.0 (#1025). Version 0.5.0 (April 25, 2019) ------------------------------ Improvements: * Significant performance improvement (around 10x) for `GeoDataFrame.iterfeatures`, which also improves `GeoDataFrame.to_file` (#864). * File IO enhancements based on Fiona 1.8: * Support for writing bool dtype (#855) and datetime dtype, if the file format supports it (#728). * Support for writing dataframes with multiple geometry types, if the file format allows it (e.g. GeoJSON for all types, or ESRI Shapefile for Polygon+MultiPolygon) (#827, #867, #870). * Compatibility with pyproj >= 2 (#962). * A new `geopandas.points_from_xy()` helper function to convert x and y coordinates to Point objects (#896). * The `buffer` and `interpolate` methods now accept an array-like to specify a variable distance for each geometry (#781). * Addition of a `relate` method, corresponding to the shapely method that returns the DE-9IM matrix (#853). * Plotting improvements: * Performance improvement in plotting by only flattening the geometries if there are actually 'Multi' geometries (#785). * Choropleths: access to all `mapclassify` classification schemes and addition of the `classification_kwds` keyword in the `plot` method to specify options for the scheme (#876). * Ability to specify a matplotlib axes object on which to plot the color bar with the `cax` keyword, in order to have more control over the color bar placement (#894). * Changed the default provider in ``geopandas.tools.geocode`` from Google (now requires an API key) to Geocode.Farm (#907, #975). Bug fixes: - Remove the edge in the legend marker (#807). - Fix the `align` method to preserve the CRS (#829). - Fix `geopandas.testing.assert_geodataframe_equal` to correctly compare left and right dataframes (#810). - Fix in choropleth mapping when the values contain missing values (#877). - Better error message in `sjoin` if the input is not a GeoDataFrame (#842). - Fix in `read_postgis` to handle nullable (missing) geometries (#856). - Correctly passing through the `parse_dates` keyword in `read_postgis` to the underlying pandas method (#860). - Fixed the shape of Antarctica in the included demo dataset 'naturalearth_lowres' (by updating to the latest version) (#804). Version 0.4.1 (March 5, 2019) ----------------------------- Small bug-fix release for compatibility with the latest Fiona and PySAL releases: * Compatibility with Fiona 1.8: fix deprecation warning (#854). * Compatibility with PySAL 2.0: switched to `mapclassify` instead of `PySAL` as dependency for choropleth mapping with the `scheme` keyword (#872). * Fix for new `overlay` implementation in case the intersection is empty (#800). Version 0.4.0 (July 15, 2018) ----------------------------- Improvements: * Improved `overlay` function (better performance, several incorrect behaviours fixed) (#429) * Pass keywords to control legend behavior (`legend_kwds`) to `plot` (#434) * Add basic support for reading remote datasets in `read_file` (#531) * Pass kwargs for `buffer` operation on GeoSeries (#535) * Expose all geopy services as options in geocoding (#550) * Faster write speeds to GeoPackage (#605) * Permit `read_file` filtering with a bounding box from a GeoDataFrame (#613) * Set CRS on GeoDataFrame returned by `read_postgis` (#627) * Permit setting markersize for Point GeoSeries plots with column values (#633) * Started an example gallery (#463, #690, #717) * Support for plotting MultiPoints (#683) * Testing functionality (e.g. `assert_geodataframe_equal`) is now publicly exposed (#707) * Add `explode` method to GeoDataFrame (similar to the GeoSeries method) (#671) * Set equal aspect on active axis on multi-axis figures (#718) * Pass array of values to column argument in `plot` (#770) Bug fixes: * Ensure that colorbars are plotted on the correct axis (#523) * Handle plotting empty GeoDataFrame (#571) * Save z-dimension when writing files (#652) * Handle reading empty shapefiles (#653) * Correct dtype for empty result of spatial operations (#685) * Fix empty `sjoin` handling for pandas>=0.23 (#762) Version 0.3.0 (August 29, 2017) ------------------------------- Improvements: * Improve plotting performance using ``matplotlib.collections`` (#267) * Improve default plotting appearance. The defaults now follow the new matplotlib defaults (#318, #502, #510) * Provide access to x/y coordinates as attributes for Point GeoSeries (#383) * Make the NYBB dataset available through ``geopandas.datasets`` (#384) * Enable ``sjoin`` on non-integer-index GeoDataFrames (#422) * Add ``cx`` indexer to GeoDataFrame (#482) * ``GeoDataFrame.from_features`` now also accepts a Feature Collection (#225, #507) * Use index label instead of integer id in output of ``iterfeatures`` and ``to_json`` (#421) * Return empty data frame rather than raising an error when performing a spatial join with non overlapping geodataframes (#335) Bug fixes: * Compatibility with shapely 1.6.0 (#512) * Fix ``fiona.filter`` results when bbox is not None (#372) * Fix ``dissolve`` to retain CRS (#389) * Fix ``cx`` behavior when using index of 0 (#478) * Fix display of lower bin in legend label of choropleth plots using a PySAL scheme (#450) Version 0.2.0 ------------- Improvements: * Complete overhaul of the documentation * Addition of ``overlay`` to perform spatial overlays with polygons (#142) * Addition of ``sjoin`` to perform spatial joins (#115, #145, #188) * Addition of ``__geo_interface__`` that returns a python data structure to represent the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection`` (#116) and ``iterfeatures`` method (#178) * Addition of the ``explode`` (#146) and ``dissolve`` (#310, #311) methods. * Addition of the ``sindex`` attribute, a Spatial Index using the optional dependency ``rtree`` (``libspatialindex``) that can be used to speed up certain operations such as overlays (#140, #141). * Addition of the ``GeoSeries.cx`` coordinate indexer to slice a GeoSeries based on a bounding box of the coordinates (#55). * Improvements to plotting: ability to specify edge colors (#173), support for the ``vmin``, ``vmax``, ``figsize``, ``linewidth`` keywords (#207), legends for chloropleth plots (#210), color points by specifying a colormap (#186) or a single color (#238). * Larger flexibility of ``to_crs``, accepting both dicts and proj strings (#289) * Addition of embedded example data, accessible through ``geopandas.datasets.get_path``. API changes: * In the ``plot`` method, the ``axes`` keyword is renamed to ``ax`` for consistency with pandas, and the ``colormap`` keyword is renamed to ``cmap`` for consistency with matplotlib (#208, #228, #240). Bug fixes: * Properly handle rows with missing geometries (#139, #193). * Fix ``GeoSeries.to_json`` (#263). * Correctly serialize metadata when pickling (#199, #206). * Fix ``merge`` and ``concat`` to return correct GeoDataFrame (#247, #320, #322). geopandas-0.10.2/CODE_OF_CONDUCT.md000066400000000000000000000164031413247402600163410ustar00rootroot00000000000000# GeoPandas Project Code of Conduct Behind the GeoPandas Project is an engaged and respectful community made up of people from all over the world and with a wide range of backgrounds. Naturally, this implies diversity of ideas and perspectives on often complex problems. Disagreement and healthy discussion of conflicting viewpoints is welcome: the best solutions to hard problems rarely come from a single angle. But disagreement is not an excuse for aggression: humans tend to take disagreement personally and easily drift into behavior that ultimately degrades a community. This is particularly acute with online communication across language and cultural gaps, where many cues of human behavior are unavailable. We are outlining here a set of principles and processes to support a healthy community in the face of these challenges. Fundamentally, we are committed to fostering a productive, harassment-free environment for everyone. Rather than considering this code an exhaustive list of things that you can’t do, take it in the spirit it is intended - a guide to make it easier to enrich all of us and the communities in which we participate. Importantly: as a member of our community, *you are also a steward of these values*. Not all problems need to be resolved via formal processes, and often a quick, friendly but clear word on an online forum or in person can help resolve a misunderstanding and de-escalate things. However, sometimes these informal processes may be inadequate: they fail to work, there is urgency or risk to someone, nobody is intervening publicly and you don't feel comfortable speaking in public, etc. For these or other reasons, structured follow-up may be necessary and here we provide the means for that: we welcome reports by emailing [*geopandas-conduct@googlegroups.com*](mailto:geopandas-conduct@googlegroups.com) or by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSd8Tbi2zNl1i2N9COX0yavHEqTGFIPQ1_cLcy1A3JgVc1OrAQ/viewform). This code applies equally to founders, developers, mentors and new community members, in all spaces managed by the GeoPandas Project. This includes the mailing lists, our GitHub organization, our chat room, in-person events, and any other forums created by the project team. In addition, violations of this code outside these spaces may affect a person's ability to participate within them. By embracing the following principles, guidelines and actions to follow or avoid, you will help us make the GeoPandas Project a welcoming and productive community. Feel free to contact the Code of Conduct Committee at [*geopandas-conduct@googlegroups.com*](mailto:geopandas-conduct@googlegroups.com) with any questions. 1. **Be friendly and patient**. 2. **Be welcoming**. We strive to be a community that welcomes and supports people of all backgrounds and identities. This includes, but is not limited to, members of any race, ethnicity, culture, national origin, color, immigration status, social and economic class, educational level, sex, sexual orientation, gender identity and expression, age, physical appearance, family status, technological or professional choices, academic discipline, religion, mental ability, and physical ability. 3. **Be considerate**. Your work will be used by other people, and you in turn will depend on the work of others. Any decision you take will affect users and colleagues, and you should take those consequences into account when making decisions. Remember that we're a world-wide community. You may be communicating with someone with a different primary language or cultural background. 4. **Be respectful**. Not all of us will agree all the time, but disagreement is no excuse for poor behavior or poor manners. We might all experience some frustration now and then, but we cannot allow that frustration to turn into a personal attack. It’s important to remember that a community where people feel uncomfortable or threatened is not a productive one. 5. **Be careful in the words that you choose**. Be kind to others. Do not insult or put down other community members. Harassment and other exclusionary behavior are not acceptable. This includes, but is not limited to: * Violent threats or violent language directed against another person * Discriminatory jokes and language * Posting sexually explicit or violent material * Posting (or threatening to post) other people's personally identifying information ("doxing") * Personal insults, especially those using racist, sexist, and xenophobic terms * Unwelcome sexual attention * Advocating for, or encouraging, any of the above behavior * Repeated harassment of others. In general, if someone asks you to stop, then stop 6. **Moderate your expectations**. Please respect that community members choose how they spend their time in the project. A thoughtful question about your expectations is preferable to demands for another person's time. 7. **When we disagree, try to understand why**. Disagreements, both social and technical, happen all the time and the GeoPandas Project is no exception. Try to understand where others are coming from, as seeing a question from their viewpoint may help find a new path forward. And don’t forget that it is human to err: blaming each other doesn’t get us anywhere, while we can learn from mistakes to find better solutions. 8. **A simple apology can go a long way**. It can often de-escalate a situation, and telling someone that you are sorry is an act of empathy that doesn’t automatically imply an admission of guilt. ## Reporting If you believe someone is violating the code of conduct, please report this in a timely manner. Code of conduct violations reduce the value of the community for everyone and we take them seriously. You can file a report by emailing [*geopandas-conduct@googlegroups.com*](mailto:geopandas-conduct@googlegroups.com) or by filing out [this form](https://docs.google.com/forms/d/e/1FAIpQLSd8Tbi2zNl1i2N9COX0yavHEqTGFIPQ1_cLcy1A3JgVc1OrAQ/viewform). The online form gives you the option to keep your report anonymous or request that we follow up with you directly. While we cannot follow up on an anonymous report, we will take appropriate action. Messages sent to the e-mail address or through the form will be sent only to the Code of Conduct Committee, which currently consists of: * Hannah Aizenman * Joris Van den Bossche * Martin Fleischmann ## Enforcement Enforcement procedures within the GeoPandas Project follow Project Jupyter's [*Enforcement Manual*](https://github.com/jupyter/governance/blob/master/conduct/enforcement.md). For information on enforcement, please view the [original manual](https://github.com/jupyter/governance/blob/master/conduct/enforcement.md). Original text courtesy of the [*Speak Up!*](http://web.archive.org/web/20141109123859/http://speakup.io/coc.html), [*Django*](https://www.djangoproject.com/conduct) and [*Jupyter*](https://github.com/jupyter/governance/blob/master/conduct/code_of_conduct.md) Projects, modified by the GeoPandas Project. We are grateful to those projects for contributing these materials under open licensing terms for us to easily reuse. All content on this page is licensed under a [*Creative Commons Attribution*](http://creativecommons.org/licenses/by/3.0/) license. geopandas-0.10.2/CONTRIBUTING.md000066400000000000000000000053431413247402600157740ustar00rootroot00000000000000Guidelines ========== Contributions to GeoPandas are very welcome. They are likely to be accepted more quickly if they follow these guidelines. At this stage of GeoPandas development, the priorities are to define a simple, usable, and stable API and to have clean, maintainable, readable code. Performance matters, but not at the expense of those goals. In general, GeoPandas follows the conventions of the pandas project where applicable. Please read the [contributing guidelines](https://geopandas.readthedocs.io/en/latest/community/contributing.html). In particular, when submitting a pull request: - Install the requirements for the development environment (one can do this with either conda, and the environment.yml file, or pip, and the requirements-dev.txt file, and can use the pandas contributing guidelines as a guide). - All existing tests should pass. Please make sure that the test suite passes, both locally and on [GitHub Actions](https://github.com/geopandas/geopandas/actions). Status on GHA will be visible on a pull request. GHA are automatically enabled on your own fork as well. To trigger a check, make a PR to your own fork. - New functionality should include tests. Please write reasonable tests for your code and make sure that they pass on your pull request. - Classes, methods, functions, etc. should have docstrings. The first line of a docstring should be a standalone summary. Parameters and return values should be documented explicitly. Improving the documentation and testing for code already in GeoPandas is a great way to get started if you'd like to make a contribution. Style ----- - GeoPandas supports Python 3.7+ only. The last version of GeoPandas supporting Python 2 is 0.6. - GeoPandas follows [the PEP 8 standard](http://www.python.org/dev/peps/pep-0008/) and uses [Black](https://black.readthedocs.io/en/stable/) and [Flake8](http://flake8.pycqa.org/en/latest/) to ensure a consistent code format throughout the project. - Imports should be grouped with standard library imports first, third-party libraries next, and GeoPandas imports third. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests. - You can set up [pre-commit hooks](https://pre-commit.com/) to automatically run `black` and `flake8` when you make a git commit. This can be done by installing `pre-commit`: $ python -m pip install pre-commit From the root of the geopandas repository, you should then install `pre-commit`: $ pre-commit install Then `black` and `flake8` will be run automatically each time you commit changes. You can skip these checks with `git commit --no-verify`. geopandas-0.10.2/LICENSE.txt000066400000000000000000000027401413247402600153640ustar00rootroot00000000000000Copyright (c) 2013-2016, GeoPandas developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Enthought, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. geopandas-0.10.2/MANIFEST.in000066400000000000000000000001101413247402600152640ustar00rootroot00000000000000include versioneer.py include geopandas/_version.py include LICENSE.txt geopandas-0.10.2/README.md000066400000000000000000000142301413247402600150150ustar00rootroot00000000000000GeoPandas [![Actions Status](https://github.com/geopandas/geopandas/workflows/Tests/badge.svg)](https://github.com/geopandas/geopandas/actions?query=workflow%3ATests) [![Coverage Status](https://codecov.io/gh/geopandas/geopandas/branch/master/graph/badge.svg)](https://codecov.io/gh/geopandas/geopandas) [![Join the chat at https://gitter.im/geopandas/geopandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/geopandas/geopandas/master) [![DOI](https://zenodo.org/badge/11002815.svg)](https://zenodo.org/badge/latestdoi/11002815) ========= Python tools for geographic data Introduction ------------ GeoPandas is a project to add support for geographic data to [pandas](http://pandas.pydata.org) objects. It currently implements `GeoSeries` and `GeoDataFrame` types which are subclasses of `pandas.Series` and `pandas.DataFrame` respectively. GeoPandas objects can act on [shapely](http://shapely.readthedocs.io/en/latest/) geometry objects and perform geometric operations. GeoPandas geometry operations are cartesian. The coordinate reference system (crs) can be stored as an attribute on an object, and is automatically set when loading from a file. Objects may be transformed to new coordinate systems with the `to_crs()` method. There is currently no enforcement of like coordinates for operations, but that may change in the future. Documentation is available at [geopandas.org](http://geopandas.org) (current release) and [Read the Docs](http://geopandas.readthedocs.io/en/latest/) (release and development versions). Install -------- See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html) for all details. GeoPandas depends on the following packages: - ``pandas`` - ``shapely`` - ``fiona`` - ``pyproj`` Further, ``matplotlib`` is an optional dependency, required for plotting, and [``rtree``](https://github.com/Toblerity/rtree) is an optional dependency, required for spatial joins. ``rtree`` requires the C library [``libspatialindex``](https://github.com/libspatialindex/libspatialindex). Those packages depend on several low-level libraries for geospatial analysis, which can be a challenge to install. Therefore, we recommend to install GeoPandas using the [conda package manager](https://conda.io/en/latest/). See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html) for more details. Get in touch ------------ - Ask usage questions ("How do I?") on [StackOverflow](https://stackoverflow.com/questions/tagged/geopandas) or [GIS StackExchange](https://gis.stackexchange.com/questions/tagged/geopandas). - Report bugs, suggest features or view the source code [on GitHub](https://github.com/geopandas/geopandas). - For a quick question about a bug report or feature request, or Pull Request, head over to the [gitter channel](https://gitter.im/geopandas/geopandas). - For less well defined questions or ideas, or to announce other projects of interest to GeoPandas users, ... use the [mailing list](https://groups.google.com/forum/#!forum/geopandas). Examples -------- >>> import geopandas >>> from shapely.geometry import Polygon >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = geopandas.GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0)) dtype: geometry ![Example 1](doc/source/gallery/test.png) Some geographic operations return normal pandas object. The `area` property of a `GeoSeries` will return a `pandas.Series` containing the area of each item in the `GeoSeries`: >>> print(g.area) 0 0.5 1 1.0 2 1.0 dtype: float64 Other operations return GeoPandas objects: >>> g.buffer(0.5) 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ... 2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04... dtype: geometry ![Example 2](doc/source/gallery/test_buffer.png) GeoPandas objects also know how to plot themselves. GeoPandas uses [matplotlib](http://matplotlib.org) for plotting. To generate a plot of our GeoSeries, use: >>> g.plot() GeoPandas also implements alternate constructors that can read any data format recognized by [fiona](http://fiona.readthedocs.io/en/latest/). To read a zip file containing an ESRI shapefile with the [boroughs boundaries of New York City](https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm) (GeoPandas includes this as an example dataset): >>> nybb_path = geopandas.datasets.get_path('nybb') >>> boros = geopandas.read_file(nybb_path) >>> boros.set_index('BoroCode', inplace=True) >>> boros.sort_index(inplace=True) >>> boros BoroName Shape_Leng Shape_Area \ BoroCode 1 Manhattan 359299.096471 6.364715e+08 2 Bronx 464392.991824 1.186925e+09 3 Brooklyn 741080.523166 1.937479e+09 4 Queens 896344.047763 3.045213e+09 5 Staten Island 330470.010332 1.623820e+09 geometry BoroCode 1 MULTIPOLYGON (((981219.0557861328 188655.31579... 2 MULTIPOLYGON (((1012821.805786133 229228.26458... 3 MULTIPOLYGON (((1021176.479003906 151374.79699... 4 MULTIPOLYGON (((1029606.076599121 156073.81420... 5 MULTIPOLYGON (((970217.0223999023 145643.33221... ![New York City boroughs](doc/source/gallery/nyc.png) >>> boros['geometry'].convex_hull BoroCode 1 POLYGON ((977855.4451904297 188082.3223876953,... 2 POLYGON ((1017949.977600098 225426.8845825195,... 3 POLYGON ((988872.8212280273 146772.0317993164,... 4 POLYGON ((1000721.531799316 136681.776184082, ... 5 POLYGON ((915517.6877458114 120121.8812543372,... dtype: geometry ![Convex hulls of New York City boroughs](doc/source/gallery/nyc_hull.png) geopandas-0.10.2/asv.conf.json000066400000000000000000000141301413247402600161450ustar00rootroot00000000000000{ // The version of the config file format. Do not change, unless // you know what you are doing. "version": 1, // The name of the project being benchmarked "project": "geopandas", // The project's homepage "project_url": "http://geopandas.org/", // The URL or local path of the source code repository for the // project being benchmarked "repo": ".", // List of branches to benchmark. If not provided, defaults to "master" // (for git) or "default" (for mercurial). // "branches": ["master"], // for git // "branches": ["default"], // for mercurial // The DVCS being used. If not set, it will be automatically // determined from "repo" by looking at the protocol in the URL // (if remote), or by looking for special directories, such as // ".git" (if local). // "dvcs": "git", // The tool to use to create environments. May be "conda", // "virtualenv" or other value depending on the plugins in use. // If missing or the empty string, the tool will be automatically // determined by looking for tools on the PATH environment // variable. "environment_type": "conda", "conda_channels": ["conda-forge", "defaults"], // timeout in seconds for installing any dependencies in environment // defaults to 10 min //"install_timeout": 600, // the base URL to show a commit for the project. "show_commit_url": "http://github.com/geopandas/geopandas/commit/", // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. // "pythons": ["2.7", "3.3"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty // list or empty string indicates to just test against the default // (latest) version. null indicates that the package is to not be // installed. If the package to be tested is only available from // PyPi, and the 'environment_type' is conda, then you can preface // the package name by 'pip+', and the package will be installed via // pip (with all the conda available packages installed first, // followed by the pip installed packages). // "matrix": { "pandas": [], "shapely": [], "pygeos": [], "fiona": [], "pyproj": [], "rtree": [], "matplotlib": [], "descartes": [] }, // "numpy": ["1.6", "1.7"], // "six": ["", null], // test with and without six installed // "pip+emcee": [""], // emcee is only available for install with pip. // }, // Combinations of libraries/python versions can be excluded/included // from the set to test. Each entry is a dictionary containing additional // key-value pairs to include/exclude. // // An exclude entry excludes entries where all values match. The // values are regexps that should match the whole string. // // An include entry adds an environment. Only the packages listed // are installed. The 'python' key is required. The exclude rules // do not apply to includes. // // In addition to package names, the following keys are available: // // - python // Python version, as in the *pythons* variable above. // - environment_type // Environment type, as above. // - sys_platform // Platform, as in sys.platform. Possible values for the common // cases: 'linux2', 'win32', 'cygwin', 'darwin'. // // "exclude": [ // {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows // {"environment_type": "conda", "six": null}, // don't run without six on conda // ], // // "include": [ // // additional env for python2.7 // {"python": "2.7", "numpy": "1.8"}, // // additional env if run on windows+conda // {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""}, // ], // The directory (relative to the current directory) that benchmarks are // stored in. If not provided, defaults to "benchmarks" // "benchmark_dir": "benchmarks", // The directory (relative to the current directory) to cache the Python // environments in. If not provided, defaults to "env" "env_dir": ".asv/env", // The directory (relative to the current directory) that raw benchmark // results are stored in. If not provided, defaults to "results". "results_dir": ".asv/results", // The directory (relative to the current directory) that the html tree // should be written to. If not provided, defaults to "html". "html_dir": ".asv/html", // The number of characters to retain in the commit hashes. // "hash_length": 8, // `asv` will cache wheels of the recent builds in each // environment, making them faster to install next time. This is // number of builds to keep, per environment. // "wheel_cache_size": 0 // The commits after which the regression search in `asv publish` // should start looking for regressions. Dictionary whose keys are // regexps matching to benchmark names, and values corresponding to // the commit (exclusive) after which to start looking for // regressions. The default is to start from the first commit // with results. If the commit is `null`, regression detection is // skipped for the matching benchmark. // // "regressions_first_commits": { // "some_benchmark": "352cdf", // Consider regressions only after this commit // "another_benchmark": null, // Skip regression detection altogether // } // The thresholds for relative change in results, after which `asv // publish` starts reporting regressions. Dictionary of the same // form as in ``regressions_first_commits``, with values // indicating the thresholds. If multiple entries match, the // maximum is taken. If no entry matches, the default is 5%. // // "regressions_thresholds": { // "some_benchmark": 0.01, // Threshold of 1% // "another_benchmark": 0.5, // Threshold of 50% // } } geopandas-0.10.2/benchmarks/000077500000000000000000000000001413247402600156535ustar00rootroot00000000000000geopandas-0.10.2/benchmarks/__init__.py000066400000000000000000000000011413247402600177530ustar00rootroot00000000000000 geopandas-0.10.2/benchmarks/clip.py000066400000000000000000000007141413247402600171560ustar00rootroot00000000000000from geopandas import read_file, datasets, clip from shapely.geometry import box class Bench: def setup(self, *args): world = read_file(datasets.get_path("naturalearth_lowres")) capitals = read_file(datasets.get_path("naturalearth_cities")) self.bounds = [box(*geom.bounds) for geom in world.geometry] self.points = capitals def time_clip(self): for bound in self.bounds: clip(self.points, bound) geopandas-0.10.2/benchmarks/geom_methods.py000066400000000000000000000100551413247402600207000ustar00rootroot00000000000000import random import numpy as np from geopandas import GeoSeries from shapely.geometry import Point, Polygon, MultiPolygon def with_attributes(**attrs): def decorator(func): for key, value in attrs.items(): setattr(func, key, value) return func return decorator class Bench: def setup(self, *args): self.points = GeoSeries([Point(i, i) for i in range(100000)]) triangles = GeoSeries([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(1000)]) triangles2 = triangles.copy().iloc[np.random.choice(1000, 1000)] triangles3 = GeoSeries([Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(10000)]) triangles4 = GeoSeries([ MultiPolygon([ Polygon([(random.random(), random.random()) for _ in range(3)]) ]) for _ in range(10000)]) triangle = Polygon([(random.random(), random.random()) for _ in range(3)]) self.triangles, self.triangles2 = triangles, triangles2 self.triangles_big = triangles3 self.multi_triangles = triangles4 self.triangle = triangle @with_attributes(param_names=['op'], params=[('contains', 'crosses', 'disjoint', 'intersects', 'overlaps', 'touches', 'within', 'geom_equals', 'geom_almost_equals', 'geom_equals_exact')]) def time_binary_predicate(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=['op'], params=[('contains', 'crosses', 'disjoint', 'intersects', 'overlaps', 'touches', 'within', 'geom_equals', 'geom_almost_equals')]) # 'geom_equals_exact')]) def time_binary_predicate_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=['op'], params=[('distance')]) def time_binary_float(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=['op'], params=[('distance')]) def time_binary_float_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=['op'], params=[('difference', 'symmetric_difference', 'union', 'intersection')]) def time_binary_geo(self, op): getattr(self.triangles, op)(self.triangle) @with_attributes(param_names=['op'], params=[('difference', 'symmetric_difference', 'union', 'intersection')]) def time_binary_geo_vector(self, op): getattr(self.triangles, op)(self.triangles2) @with_attributes(param_names=['op'], params=[('is_valid', 'is_empty', 'is_simple', 'is_ring')]) def time_unary_predicate(self, op): getattr(self.triangles, op) @with_attributes(param_names=['op'], params=[('area', 'length')]) def time_unary_float(self, op): getattr(self.triangles_big, op) @with_attributes(param_names=['op'], params=[('boundary', 'centroid', 'convex_hull', 'envelope', 'exterior', 'interiors')]) def time_unary_geo(self, op): getattr(self.triangles, op) def time_unary_geo_representative_point(self, *args): getattr(self.triangles, 'representative_point')() def time_geom_type(self, *args): self.triangles_big.geom_type def time_bounds(self, *args): self.triangles.bounds def time_unary_union(self, *args): self.triangles.unary_union def time_buffer(self, *args): self.points.buffer(2) def time_explode(self, *args): self.multi_triangles.explode() # TODO # project, interpolate, affine_transform, translate, rotate, scale, skew # cx indexer geopandas-0.10.2/benchmarks/io.py000066400000000000000000000061721413247402600166420ustar00rootroot00000000000000import os import shutil import tempfile import warnings import numpy as np from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries, read_file, read_parquet, read_feather # TEMP: hide warning from to_parquet warnings.filterwarnings("ignore", message=".*initial implementation of Parquet.*") format_dict = { "ESRI Shapefile": ( ".shp", lambda gdf, filename: gdf.to_file(filename, driver="ESRI Shapefile"), lambda filename: read_file(filename, driver="ESRI Shapefile"), ), "GeoJSON": ( ".json", lambda gdf, filename: gdf.to_file(filename, driver="GeoJSON"), lambda filename: read_file(filename, driver="GeoJSON"), ), "GPKG": ( ".gpkg", lambda gdf, filename: gdf.to_file(filename, driver="GeoJSON"), lambda filename: read_file(filename, driver="GeoJSON"), ), "Parquet": ( ".parquet", lambda gdf, filename: gdf.to_parquet(filename), lambda filename: read_parquet(filename), ), "Feather": ( ".feather", lambda gdf, filename: gdf.to_feather(filename), lambda filename: read_feather(filename), ), } class Bench: params = ["ESRI Shapefile", "GeoJSON", "GPKG", "Parquet", "Feather"] param_names = ["file_format"] def setup(self, file_format): self.ext, self.writer, self.reader = format_dict[file_format] num_points = 20000 xs = np.random.rand(num_points) ys = np.random.rand(num_points) self.points = GeoSeries([Point(x, y) for (x, y) in zip(xs, ys)]) self.df = GeoDataFrame( { "geometry": self.points, "x": xs, "y": ys, "s": np.zeros(num_points, dtype="object"), } ) self.tmpdir = tempfile.mkdtemp() self.filename = os.path.join(self.tmpdir, "frame" + self.ext) self.writer(self.df, self.filename) def teardown(self, file_format): shutil.rmtree(self.tmpdir) class BenchFrame(Bench): params = ["ESRI Shapefile", "GeoJSON", "GPKG", "Parquet", "Feather"] param_names = ["file_format"] def time_write(self, file_format): with tempfile.TemporaryDirectory() as tmpdir: out_filename = os.path.join(tmpdir, "frame" + self.ext) self.writer(self.df, out_filename) def time_read(self, file_format): self.reader(self.filename) class BenchSeries(Bench): params = ["ESRI Shapefile", "GeoJSON", "GPKG"] param_names = ["file_format"] def setup(self, file_format): super().setup(file_format) self.filename_series = os.path.join(self.tmpdir, "series" + self.ext) self.writer(self.points, self.filename_series) def time_write_series(self, file_format): with tempfile.TemporaryDirectory() as tmpdir: out_filename = os.path.join(tmpdir, "series" + self.ext) self.writer(self.points, out_filename) def time_read_series(self, file_format): GeoSeries.from_file(self.filename_series) def time_read_series_from_frame(self, file_format): GeoSeries.from_file(self.filename) geopandas-0.10.2/benchmarks/overlay.py000066400000000000000000000041221413247402600177050ustar00rootroot00000000000000from geopandas import GeoDataFrame, GeoSeries, read_file, datasets, overlay import numpy as np from shapely.geometry import Point, Polygon class Countries: param_names = ['how'] params = [('intersection', 'union', 'identity', 'symmetric_difference', 'difference')] def setup(self, *args): world = read_file(datasets.get_path('naturalearth_lowres')) capitals = read_file(datasets.get_path('naturalearth_cities')) countries = world[['geometry', 'name']] countries = countries.to_crs('+init=epsg:3395')[ countries.name != "Antarctica"] capitals = capitals.to_crs('+init=epsg:3395') capitals['geometry'] = capitals.buffer(500000) self.countries = countries self.capitals = capitals def time_overlay(self, how): overlay(self.countries, self.capitals, how=how) class Small: param_names = ['how'] params = [('intersection', 'union', 'identity', 'symmetric_difference', 'difference')] def setup(self, *args): polys1 = GeoSeries([Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)])]) polys2 = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])]) df1 = GeoDataFrame({'geometry': polys1, 'df1': [1, 2]}) df2 = GeoDataFrame({'geometry': polys2, 'df2': [1, 2]}) self.df1, self.df2 = df1, df2 def time_overlay(self, how): overlay(self.df1, self.df2, how=how) class ManyPoints: param_names = ['how'] params = [('intersection', 'union', 'identity', 'symmetric_difference', 'difference')] def setup(self, *args): points = GeoDataFrame(geometry=[Point(i, i) for i in range(1000)]) base = np.array([[0, 0], [0, 100], [100, 100], [100, 0]]) polys = GeoDataFrame( geometry=[Polygon(base + i * 100) for i in range(10)]) self.df1, self.df2 = points, polys def time_overlay(self, how): overlay(self.df1, self.df2, how=how) geopandas-0.10.2/benchmarks/plotting.py000066400000000000000000000044731413247402600200750ustar00rootroot00000000000000import random from geopandas import GeoDataFrame, GeoSeries from shapely.geometry import Point, LineString, Polygon, MultiPolygon import numpy as np class Bench: param_names = ["geom_type"] params = [("Point", "LineString", "Polygon", "MultiPolygon", "mixed")] def setup(self, geom_type): if geom_type == "Point": geoms = GeoSeries([Point(i, i) for i in range(1000)]) elif geom_type == "LineString": geoms = GeoSeries( [ LineString([(random.random(), random.random()) for _ in range(5)]) for _ in range(100) ] ) elif geom_type == "Polygon": geoms = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(100) ] ) elif geom_type == "MultiPolygon": geoms = GeoSeries( [ MultiPolygon( [ Polygon( [(random.random(), random.random()) for _ in range(3)] ) for _ in range(3) ] ) for _ in range(20) ] ) elif geom_type == "mixed": g1 = GeoSeries([Point(i, i) for i in range(100)]) g2 = GeoSeries( [ LineString([(random.random(), random.random()) for _ in range(5)]) for _ in range(100) ] ) g3 = GeoSeries( [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(100) ] ) geoms = g1 geoms.iloc[np.random.randint(0, 100, 50)] = g2.iloc[:50] geoms.iloc[np.random.randint(0, 100, 33)] = g3.iloc[:33] print(geoms.geom_type.value_counts()) df = GeoDataFrame({"geometry": geoms, "values": np.random.randn(len(geoms))}) self.geoms = geoms self.df = df def time_plot_series(self, *args): self.geoms.plot() def time_plot_values(self, *args): self.df.plot(column="values") geopandas-0.10.2/benchmarks/sindex.py000066400000000000000000000066401413247402600175250ustar00rootroot00000000000000from shapely.geometry import Point from geopandas import read_file, datasets, GeoSeries # Derive list of valid query predicates based on underlying index backend; # we have to create a non-empty instance of the index to get these index = GeoSeries([Point(0, 0)]).sindex predicates = sorted(p for p in index.valid_query_predicates if p is not None) geom_types = ("mixed", "points", "polygons") def generate_test_df(): world = read_file(datasets.get_path("naturalearth_lowres")) capitals = read_file(datasets.get_path("naturalearth_cities")) countries = world.to_crs("epsg:3395")[["geometry"]] capitals = capitals.to_crs("epsg:3395")[["geometry"]] mixed = capitals.append(countries) # get a mix of geometries points = capitals polygons = countries # filter out invalid geometries data = { "mixed": mixed[mixed.is_valid], "points": points[points.is_valid], "polygons": polygons[polygons.is_valid], } # ensure index is pre-generated for data_type in data.keys(): data[data_type].sindex.query(data[data_type].geometry.values.data[0]) return data class BenchIntersection: param_names = ["input_geom_type", "tree_geom_type"] params = [ geom_types, geom_types, ] def setup(self, *args): self.data = generate_test_df() # cache bounds so that bound creation is not counted in benchmarks self.bounds = { data_type: [g.bounds for g in self.data[data_type].geometry] for data_type in self.data.keys() } def time_intersects(self, input_geom_type, tree_geom_type): tree = self.data[tree_geom_type].sindex for bounds in self.bounds[input_geom_type]: tree.intersection(bounds) class BenchIndexCreation: param_names = ["tree_geom_type"] params = [ geom_types, ] def setup(self, *args): self.data = generate_test_df() def time_index_creation(self, tree_geom_type): """Time creation of spatial index. Note: requires running a single query to ensure that lazy-building indexes are actually built. """ # Note: the GeoDataFram._sindex_generated attribute will # be removed by GH#1444 but is kept here (in the benchmarks # so that we can compare pre GH#1444 to post GH#1444 if needed self.data[tree_geom_type]._sindex_generated = None self.data[tree_geom_type].geometry.values._sindex = None tree = self.data[tree_geom_type].sindex # also do a single query to ensure the index is actually # generated and used tree.query( self.data[tree_geom_type].geometry.values.data[0] ) class BenchQuery: param_names = ["predicate", "input_geom_type", "tree_geom_type"] params = [ predicates, geom_types, geom_types, ] def setup(self, *args): self.data = generate_test_df() def time_query_bulk(self, predicate, input_geom_type, tree_geom_type): self.data[tree_geom_type].sindex.query_bulk( self.data[input_geom_type].geometry.values.data, predicate=predicate, ) def time_query(self, predicate, input_geom_type, tree_geom_type): tree = self.data[tree_geom_type].sindex for geom in self.data[input_geom_type].geometry.values.data: tree.query( geom, predicate=predicate ) geopandas-0.10.2/benchmarks/sjoin.py000066400000000000000000000016301413247402600173470ustar00rootroot00000000000000import random from geopandas import GeoDataFrame, GeoSeries, sjoin from shapely.geometry import Point, LineString, Polygon import numpy as np class Bench: param_names = ['op'] params = [('intersects', 'contains', 'within')] def setup(self, *args): triangles = GeoSeries( [Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(1000)]) points = GeoSeries( [Point(x, y) for x, y in zip(np.random.random(10000), np.random.random(10000))]) df1 = GeoDataFrame({'val1': np.random.randn(len(triangles)), 'geometry': triangles}) df2 = GeoDataFrame({'val1': np.random.randn(len(points)), 'geometry': points}) self.df1, self.df2 = df1, df2 def time_sjoin(self, op): sjoin(self.df1, self.df2, op=op) geopandas-0.10.2/benchmarks/transform.py000066400000000000000000000015041413247402600202400ustar00rootroot00000000000000from geopandas import GeoDataFrame, GeoSeries, read_file, datasets import numpy as np import pandas as pd from shapely.geometry import Point class CRS: def setup(self): nybb = read_file(datasets.get_path('nybb')) self.long_nybb = GeoDataFrame(pd.concat(10 * [nybb]), crs=nybb.crs) num_points = 20000 longitudes = np.random.rand(num_points) - 120 latitudes = np.random.rand(num_points) + 38 self.point_df = GeoSeries([Point(x, y) for (x, y) in zip(longitudes, latitudes)]) self.point_df.crs = {"init": "epsg:4326"} def time_transform_wgs84(self): self.long_nybb.to_crs({"init": "epsg:4326"}) def time_transform_many_points(self): self.point_df.to_crs({"init": "epsg:32610"}) geopandas-0.10.2/ci/000077500000000000000000000000001413247402600141315ustar00rootroot00000000000000geopandas-0.10.2/ci/envs/000077500000000000000000000000001413247402600151045ustar00rootroot00000000000000geopandas-0.10.2/ci/envs/37-latest-conda-forge.yaml000066400000000000000000000005331413247402600216760ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.7 # required - pandas - shapely - fiona - pyproj - pygeos # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree - matplotlib - mapclassify - folium - xyzservices - scipy - geopy - SQLalchemy - libspatialite - pyarrow geopandas-0.10.2/ci/envs/37-latest-defaults.yaml000066400000000000000000000005631413247402600213240ustar00rootroot00000000000000name: test channels: - defaults dependencies: - python=3.7 # required - pandas - shapely - fiona - pyproj - geos # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree - matplotlib #- geopy - SQLalchemy - libspatialite - pip: - geopy - mapclassify - pyarrow - folium - xyzservices geopandas-0.10.2/ci/envs/37-minimal.yaml000066400000000000000000000006631413247402600176520ustar00rootroot00000000000000name: test channels: - defaults - conda-forge dependencies: - python=3.7 # required - numpy=1.18 - pandas==0.25 - shapely=1.6 - fiona=1.8.13 #- pyproj # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree - matplotlib - matplotlib=3.1 # - mapclassify=2.4.0 - doesn't build due to conflicts - geopy - SQLalchemy - libspatialite - pyarrow - pip: - pyproj==2.2.2 geopandas-0.10.2/ci/envs/37-pd10.yaml000066400000000000000000000006121413247402600167620ustar00rootroot00000000000000name: test channels: - defaults dependencies: - python=3.7 # required - pandas=1.0 - shapely - fiona - numpy=<1.19 #- pyproj - geos # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree - matplotlib #- geopy - SQLalchemy - libspatialite - pip - pip: - pyproj==3.0.1 - geopy - mapclassify==2.4.0 - pyarrow geopandas-0.10.2/ci/envs/38-dev.yaml000066400000000000000000000013551413247402600170020ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.8 - cython # required - fiona - pyproj - geos # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree #- geopy - SQLalchemy - libspatialite - pyarrow - pip: - geopy - mapclassify>=2.4.0 # dev versions of packages - git+https://github.com/numpy/numpy.git@main - git+https://github.com/pydata/pandas.git@master - git+https://github.com/matplotlib/matplotlib.git@master - git+https://github.com/Toblerity/Shapely.git@master - git+https://github.com/pygeos/pygeos.git@master - git+https://github.com/python-visualization/folium.git@master - git+https://github.com/geopandas/xyzservices.git@main geopandas-0.10.2/ci/envs/38-latest-conda-forge.yaml000066400000000000000000000010661413247402600217010ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.8 # required - pandas=1.3.2 # temporary pin because 1.3.3 has regression for overlay (GH2101) - shapely - fiona - pyproj - pygeos # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree - matplotlib - mapclassify - folium - xyzservices - scipy - geopy # installed in tests.yaml, because not available on windows # - postgis - SQLalchemy - psycopg2 - libspatialite - geoalchemy2 - pyarrow # doctest testing - pytest-doctestplusgeopandas-0.10.2/ci/envs/38-no-optional-deps.yaml000066400000000000000000000002621413247402600214100ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.8 # required - pandas - shapely - fiona - pyproj # testing - pytest - pytest-cov - pytest-xdist geopandas-0.10.2/ci/envs/39-latest-conda-forge.yaml000066400000000000000000000007571413247402600217100ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.9 # required - pandas - shapely - fiona - pyproj - pygeos # testing - pytest - pytest-cov - pytest-xdist - fsspec # optional - rtree - matplotlib - mapclassify - folium - xyzservices - scipy - geopy # installed in tests.yaml, because not available on windows # - postgis - SQLalchemy - psycopg2 - libspatialite - geoalchemy2 - pyarrow # doctest testing - pytest-doctestplus geopandas-0.10.2/ci/scripts/000077500000000000000000000000001413247402600156205ustar00rootroot00000000000000geopandas-0.10.2/ci/scripts/setup_postgres.sh000066400000000000000000000013621413247402600212440ustar00rootroot00000000000000#!/bin/sh set -e if [ -z "${PGUSER}" ] || [ -z "${PGPORT}" ]; then echo "Environment variables PGUSER and PGPORT must be set" exit 1 fi PGDATA=$(mktemp -d /tmp/postgres.XXXXXX) echo "Setting up PostgreSQL in ${PGDATA} on port ${PGPORT}" pg_ctl -D ${PGDATA} initdb pg_ctl -D ${PGDATA} start SOCKETPATH="/tmp/.s.PGSQL.${PGPORT}" echo -n 'waiting for postgres' while [ ! -e ${SOCKETPATH} ]; do sleep 1 echo -n '.' done echo echo "Done setting up PostgreSQL. When finished, stop and cleanup using:" echo echo " pg_ctl -D ${PGDATA} stop" echo " rm -rf ${PGDATA}" echo createuser -U ${USER} -s ${PGUSER} createdb --owner=${PGUSER} test_geopandas psql -d test_geopandas -q -c "CREATE EXTENSION postgis" echo "PostGIS server ready." geopandas-0.10.2/codecov.yml000066400000000000000000000002431413247402600157020ustar00rootroot00000000000000coverage: status: project: default: target: 95% # the required coverage value threshold: 0.2% # the leniency in hitting the target geopandas-0.10.2/constraints.txt000066400000000000000000000000131413247402600166400ustar00rootroot00000000000000rtree>=0.8 geopandas-0.10.2/doc/000077500000000000000000000000001413247402600143035ustar00rootroot00000000000000geopandas-0.10.2/doc/Makefile000066400000000000000000000130721413247402600157460ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end geopandas-0.10.2/doc/nyc_boros.py000066400000000000000000000044721413247402600166610ustar00rootroot00000000000000""" Visualizing NYC Boroughs ------------------------ Visualize the Boroughs of New York City with Geopandas. This example generates many images that are used in the documentation. See the `Geometric Manipulations ` example for more details. First we'll import a dataset containing each borough in New York City. We'll use the ``datasets`` module to handle this quickly. """ import numpy as np import matplotlib.pyplot as plt from shapely.geometry import Point from geopandas import GeoSeries, GeoDataFrame import geopandas as gpd np.random.seed(1) DPI = 100 path_nybb = gpd.datasets.get_path('nybb') boros = GeoDataFrame.from_file(path_nybb) boros = boros.set_index('BoroCode') boros ############################################################################## # Next, we'll plot the raw data ax = boros.plot() plt.xticks(rotation=90) plt.savefig('nyc.png', dpi=DPI, bbox_inches='tight') ############################################################################## # We can easily retrieve the convex hull of each shape. This corresponds to # the outer edge of the shapes. boros.geometry.convex_hull.plot() plt.xticks(rotation=90) # Grab the limits which we'll use later xmin, xmax = plt.gca().get_xlim() ymin, ymax = plt.gca().get_ylim() plt.savefig('nyc_hull.png', dpi=DPI, bbox_inches='tight') ############################################################################## # We'll generate some random dots scattered throughout our data, and will # use them to perform some set operations with our boroughs. 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geopandas-0.10.2/doc/source/about.md000066400000000000000000000035201413247402600172370ustar00rootroot00000000000000# About GeoPandas ```{toctree} :maxdepth: 2 :caption: About :hidden: Team Citing Logo ``` GeoPandas is an open source project to add support for geographic data to pandas objects. It currently implements `GeoSeries` and `GeoDataFrame` types which are subclasses of `pandas.Series` and `pandas.DataFrame` respectively. GeoPandas objects can act on `shapely` geometry objects and perform geometric operations. GeoPandas is a community-led project written, used and supported by a wide range of people from all around of world of a large variety of backgrounds. Want to get involved in the community? See our [community guidelines](community). GeoPandas will always be 100% open source software, free for all to use and released under the liberal terms of the BSD-3-Clause license. ```{container} button {doc}`Team ` {doc}`Citing ` {doc}`Logo ` ``` ## Project history Kelsey Jordahl founded GeoPandas project in 2013 during the Scipy Conference and released a version 0.1.0 in July 2014. In 2016, Joris Van den Bossche took the lead and became the maintainer of the project. Since the beginning, GeoPandas is a BSD-licensed open-source project supported by a [community of contributors](https://github.com/geopandas/geopandas/graphs/contributors) from around the world and is now maintained by a [team](about/team) of core developers. In 2020 GeoPandas became [NumFOCUS Affiliated Project](https://numfocus.org/sponsored-projects/affiliated-projects) and received two [Small Development Grants](https://numfocus.org/programs/sustainability) to support its development. ## Timeline - **2013**: Beginning of the development - **2014**: GeoPandas 0.1.0 released - **2020**: GeoPandas became [NumFOCUS Affiliated Project](https://numfocus.org/sponsored-projects/affiliated-projects) geopandas-0.10.2/doc/source/about/000077500000000000000000000000001413247402600167155ustar00rootroot00000000000000geopandas-0.10.2/doc/source/about/citing.md000066400000000000000000000035341413247402600205210ustar00rootroot00000000000000# Citing When citing GeoPandas, you can use [Zenodo DOI](https://zenodo.org/record/3946761#.Xy24LC2ZPOQ) for each release. Below is the example of resulting BiBTeX record for GeoPandas 0.8.1 and a reference using APA 6th ed. _Kelsey Jordahl, Joris Van den Bossche, Martin Fleischmann, Jacob Wasserman, James McBride, Jeffrey Gerard, … François Leblanc. (2020, July 15). geopandas/geopandas: v0.8.1 (Version v0.8.1). Zenodo. http://doi.org/10.5281/zenodo.3946761_ ``` @software{kelsey_jordahl_2020_3946761, author = {Kelsey Jordahl and Joris Van den Bossche and Martin Fleischmann and Jacob Wasserman and James McBride and Jeffrey Gerard and Jeff Tratner and Matthew Perry and Adrian Garcia Badaracco and Carson Farmer and Geir Arne Hjelle and Alan D. Snow and Micah Cochran and Sean Gillies and Lucas Culbertson and Matt Bartos and Nick Eubank and maxalbert and Aleksey Bilogur and Sergio Rey and Christopher Ren and Dani Arribas-Bel and Leah Wasser and Levi John Wolf and Martin Journois and Joshua Wilson and Adam Greenhall and Chris Holdgraf and Filipe and François Leblanc}, title = {geopandas/geopandas: v0.8.1}, month = jul, year = 2020, publisher = {Zenodo}, version = {v0.8.1}, doi = {10.5281/zenodo.3946761}, url = {https://doi.org/10.5281/zenodo.3946761} } ``` geopandas-0.10.2/doc/source/about/logo.md000066400000000000000000000035641413247402600202070ustar00rootroot00000000000000# GeoPandas logo GeoPandas project uses a logo derived from [`pandas` logo](https://pandas.pydata.org/about/citing.html), enclosing it in a globe illustrating the geographic nature of our data. ## Versions We have four versions of our logo: ### Primary logo The primary logo should be used in a majority of cases. Inverted logo or icon should be used only when necessary. ```{image} ../_static/logo/geopandas_logo.png :alt: geopandas-logo :align: center ``` ### Inverted colors If you want to place the GeoPandas logo on a dark background, use the inverted version. ```{image} ../_static/logo/geopandas_logo_green.png :alt: geopandas-logo-green :align: center ``` ### Icon Although it is possible to use icon independently, we would prefer using the complete variant above. ```{image} ../_static/logo/geopandas_icon.png :alt: geopandas-icon :width: 25% :align: center ``` ### Inverted icon ```{image} ../_static/logo/geopandas_icon_green.png :alt: geopandas-icon-green :width: 25% :align: center ``` ## Download You can download all version in SVG and PNG from [GitHub repository](https://github.com/geopandas/geopandas/tree/master/doc/source/_static/logo). ## Colors Pink and yellow accent colors are shared with `pandas`. ### Green ```{raw} html ``` **HEX:** #139C5A **RGB:** (19, 156, 90) ### Yellow ```{raw} html ``` **HEX:** #FFCA00 **RGB:** (255, 202, 0) ### Pink ```{raw} html ``` **HEX:** #E70488 **RGB:** ((31, 4, 136) geopandas-0.10.2/doc/source/about/roadmap.md000066400000000000000000000000551413247402600206620ustar00rootroot00000000000000# Roadmap ## Roadmap for GeoPandas 1.0 WIP geopandas-0.10.2/doc/source/about/team.md000066400000000000000000000012411413247402600201630ustar00rootroot00000000000000# Team ## Contributors GeoPandas is developed by more than [100 volunteer contributors](https://github.com/geopandas/geopandas/graphs/contributors). ## Core developers - Joris Van den Bossche - **lead maintainer** | [@jorisvandenbossche](https://github.com/jorisvandenbossche) - Martin Fleischmann | [@martinfleis](https://github.com/martinfleis) - James McBride | [@jdmcbr](https://github.com/jdmcbr) - Brendan Ward | [@brendan-ward](https://github.com/brendan-ward) - Levi Wolf | [@ljwolf](https://github.com/ljwolf) ## Founder - Kelsey Jordahl | [@kjordahl](https://github.com/kjordahl) ## Alumni developers - Jacob Wasserman | [@jwass](https://github.com/jwass) geopandas-0.10.2/doc/source/community.rst000066400000000000000000000023631413247402600203650ustar00rootroot00000000000000Community --------- GeoPandas is a community-led project written, used and supported by a wide range of people from all around of world of a large variety of backgrounds. Everyone is welcome, each small contribution, no matter if it is a fix of a typo in the documentation, bug report, an idea, or a question, is valuable. As a member of our community, you should adhere to the principles presented in the :doc:`Code of Conduct `. If you'd like to contribute, please read the :doc:`Contributing guide `. It will help you to understand the way GeoPandas development works and us to review your contribution. GeoPandas is a part of the broader Python :doc:`ecosystem `. It depends on a range of great tools, and various packages are built on top of GeoPandas addressing specific needs in geospatial data processing, analysis and visualization. .. container:: button :doc:`Contributing ` :doc:`Code of Conduct ` :doc:`Ecosystem ` .. toctree:: :maxdepth: 2 :caption: Community :hidden: Contributing Code of Conduct Ecosystem geopandas-0.10.2/doc/source/community/000077500000000000000000000000001413247402600176275ustar00rootroot00000000000000geopandas-0.10.2/doc/source/community/code_of_conduct.rst000066400000000000000000000164551413247402600235110ustar00rootroot00000000000000GeoPandas Project Code of Conduct ================================= Behind the GeoPandas Project is an engaged and respectful community made up of people from all over the world and with a wide range of backgrounds. Naturally, this implies diversity of ideas and perspectives on often complex problems. Disagreement and healthy discussion of conflicting viewpoints is welcome: the best solutions to hard problems rarely come from a single angle. But disagreement is not an excuse for aggression: humans tend to take disagreement personally and easily drift into behavior that ultimately degrades a community. This is particularly acute with online communication across language and cultural gaps, where many cues of human behavior are unavailable. We are outlining here a set of principles and processes to support a healthy community in the face of these challenges. Fundamentally, we are committed to fostering a productive, harassment-free environment for everyone. Rather than considering this code an exhaustive list of things that you can’t do, take it in the spirit it is intended - a guide to make it easier to enrich all of us and the communities in which we participate. Importantly: as a member of our community, *you are also a steward of these values*. Not all problems need to be resolved via formal processes, and often a quick, friendly but clear word on an online forum or in person can help resolve a misunderstanding and de-escalate things. However, sometimes these informal processes may be inadequate: they fail to work, there is urgency or risk to someone, nobody is intervening publicly and you don't feel comfortable speaking in public, etc. For these or other reasons, structured follow-up may be necessary and here we provide the means for that: we welcome reports by emailing `geopandas-conduct@googlegroups.com `__ or by filling out `this form `__. This code applies equally to founders, developers, mentors and new community members, in all spaces managed by the GeoPandas Project. This includes the mailing lists, our GitHub organization, our chat room, in-person events, and any other forums created by the project team. In addition, violations of this code outside these spaces may affect a person's ability to participate within them. By embracing the following principles, guidelines and actions to follow or avoid, you will help us make Jupyter a welcoming and productive community. Feel free to contact the Code of Conduct Committee at `geopandas-conduct@googlegroups.com `__ with any questions. 1. **Be friendly and patient**. 2. **Be welcoming**. We strive to be a community that welcomes and supports people of all backgrounds and identities. This includes, but is not limited to, members of any race, ethnicity, culture, national origin, color, immigration status, social and economic class, educational level, sex, sexual orientation, gender identity and expression, age, physical appearance, family status, technological or professional choices, academic discipline, religion, mental ability, and physical ability. 3. **Be considerate**. Your work will be used by other people, and you in turn will depend on the work of others. Any decision you take will affect users and colleagues, and you should take those consequences into account when making decisions. Remember that we're a world-wide community. You may be communicating with someone with a different primary language or cultural background. 4. **Be respectful**. Not all of us will agree all the time, but disagreement is no excuse for poor behavior or poor manners. We might all experience some frustration now and then, but we cannot allow that frustration to turn into a personal attack. It’s important to remember that a community where people feel uncomfortable or threatened is not a productive one. 5. **Be careful in the words that you choose**. Be kind to others. Do not insult or put down other community members. Harassment and other exclusionary behavior are not acceptable. This includes, but is not limited to: - Violent threats or violent language directed against another person - Discriminatory jokes and language - Posting sexually explicit or violent material - Posting (or threatening to post) other people's personally identifying information ("doxing") - Personal insults, especially those using racist, sexist, and xenophobic terms - Unwelcome sexual attention - Advocating for, or encouraging, any of the above behavior - Repeated harassment of others. In general, if someone asks you to stop, then stop 6. **Moderate your expectations**. Please respect that community members choose how they spend their time in the project. A thoughtful question about your expectations is preferable to demands for another person's time. 7. **When we disagree, try to understand why**. Disagreements, both social and technical, happen all the time and the GeoPandas Project is no exception. Try to understand where others are coming from, as seeing a question from their viewpoint may help find a new path forward. And don’t forget that it is human to err: blaming each other doesn’t get us anywhere, while we can learn from mistakes to find better solutions. 8. **A simple apology can go a long way**. It can often de-escalate a situation, and telling someone that you are sorry is an act of empathy that doesn’t automatically imply an admission of guilt. Reporting --------- If you believe someone is violating the code of conduct, please report this in a timely manner. Code of conduct violations reduce the value of the community for everyone and we take them seriously. You can file a report by emailing `geopandas-conduct@googlegroups.com `__ or by filing out `this form `__. The online form gives you the option to keep your report anonymous or request that we follow up with you directly. While we cannot follow up on an anonymous report, we will take appropriate action. Messages sent to the e-mail address or through the form will be sent only to the Code of Conduct Committee, which currently consists of: - Hannah Aizenman - Joris Van den Bossche - Martin Fleischmann Enforcement ----------- Enforcement procedures within the GeoPandas Project follow Project Jupyter's `Enforcement Manual `__. For information on enforcement, please view the `original manual `__. Original text courtesy of the `Speak Up! `__, `Django `__ and `Jupyter `__ Projects, modified by the GeoPandas Project. We are grateful to those projects for contributing these materials under open licensing terms for us to easily reuse. All content on this page is licensed under a `Creative Commons Attribution `__ license. geopandas-0.10.2/doc/source/community/contributing.rst000066400000000000000000000331261413247402600230750ustar00rootroot00000000000000Contributing to GeoPandas ========================= (Contribution guidelines largely copied from `pandas `_) Overview -------- Contributions to GeoPandas are very welcome. They are likely to be accepted more quickly if they follow these guidelines. At this stage of GeoPandas development, the priorities are to define a simple, usable, and stable API and to have clean, maintainable, readable code. Performance matters, but not at the expense of those goals. In general, GeoPandas follows the conventions of the pandas project where applicable. In particular, when submitting a pull request: - All existing tests should pass. Please make sure that the test suite passes, both locally and on `GitHub Actions `_. Status on GHA will be visible on a pull request. GHA are automatically enabled on your own fork as well. To trigger a check, make a PR to your own fork. - New functionality should include tests. Please write reasonable tests for your code and make sure that they pass on your pull request. - Classes, methods, functions, etc. should have docstrings. The first line of a docstring should be a standalone summary. Parameters and return values should be documented explicitly. - Follow PEP 8 when possible. We use `Black `_ and `Flake8 `_ to ensure a consistent code format throughout the project. For more details see :ref:`below `. - Imports should be grouped with standard library imports first, 3rd-party libraries next, and GeoPandas imports third. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests. - GeoPandas supports Python 3.7+ only. The last version of GeoPandas supporting Python 2 is 0.6. Seven Steps for Contributing ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ There are seven basic steps to contributing to *GeoPandas*: 1) Fork the *GeoPandas* git repository 2) Create a development environment 3) Install *GeoPandas* dependencies 4) Make a ``development`` build of *GeoPandas* 5) Make changes to code and add tests 6) Update the documentation 7) Submit a Pull Request Each of these 7 steps is detailed below. 1) Forking the *GeoPandas* repository using Git ------------------------------------------------ To the new user, working with Git is one of the more daunting aspects of contributing to *GeoPandas**. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help. The code is hosted on `GitHub `_. To contribute you will need to sign up for a `free GitHub account `_. We use `Git `_ for version control to allow many people to work together on the project. Some great resources for learning Git: * Software Carpentry's `Git Tutorial `_ * `Atlassian `_ * the `GitHub help pages `_. * Matthew Brett's `Pydagogue `_. Getting started with Git ~~~~~~~~~~~~~~~~~~~~~~~~ `GitHub has instructions `__ for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub. .. _contributing.forking: Forking ~~~~~~~ You will need your own fork to work on the code. Go to the `GeoPandas project page `_ and hit the ``Fork`` button. You will want to clone your fork to your machine:: git clone git@github.com:your-user-name/geopandas.git geopandas-yourname cd geopandas-yourname git remote add upstream git://github.com/geopandas/geopandas.git This creates the directory `geopandas-yourname` and connects your repository to the upstream (main project) *GeoPandas* repository. The testing suite will run automatically on GitHub Actions once your pull request is submitted. The test suite will also automatically run on your branch so you can check it prior to submitting the pull request. Creating a branch ~~~~~~~~~~~~~~~~~~ You want your master branch to reflect only production-ready code, so create a feature branch for making your changes. For example:: git branch shiny-new-feature git checkout shiny-new-feature The above can be simplified to:: git checkout -b shiny-new-feature This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to *GeoPandas*. You can have many shiny-new-features and switch in between them using the git checkout command. To update this branch, you need to retrieve the changes from the master branch:: git fetch upstream git rebase upstream/master This will replay your commits on top of the latest GeoPandas git master. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to ``stash`` them prior to updating. This will effectively store your changes and they can be reapplied after updating. .. _contributing.dev_env: 2) Creating a development environment --------------------------------------- A development environment is a virtual space where you can keep an independent installation of *GeoPandas*. This makes it easy to keep both a stable version of python in one place you use for work, and a development version (which you may break while playing with code) in another. An easy way to create a *GeoPandas* development environment is as follows: - Install either `Anaconda `_ or `miniconda `_ - Make sure that you have :ref:`cloned the repository ` - ``cd`` to the *geopandas** source directory Tell conda to create a new environment, named ``geopandas_dev``, or any other name you would like for this environment, by running:: conda create -n geopandas_dev python This will create the new environment, and not touch any of your existing environments, nor any existing python installation. To work in this environment, you need to ``activate`` it. The instructions below should work for both Windows, Mac and Linux:: conda activate geopandas_dev Once your environment is activated, you will see a confirmation message to indicate you are in the new development environment. To view your environments:: conda info -e To return to you home root environment:: conda deactivate See the full conda docs `here `__. At this point you can easily do a *development* install, as detailed in the next sections. 3) Installing Dependencies -------------------------- To run *GeoPandas* in an development environment, you must first install *GeoPandas*'s dependencies. We suggest doing so using the following commands (executed after your development environment has been activated):: conda install -c conda-forge pandas fiona shapely pyproj rtree pytest This should install all necessary dependencies. 4) Making a development build ----------------------------- Once dependencies are in place, make an in-place build by navigating to the git clone of the *GeoPandas* repository and running:: python setup.py develop 5) Making changes and writing tests ------------------------------------- *GeoPandas* is serious about testing and strongly encourages contributors to embrace `test-driven development (TDD) `_. This development process "relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test." So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests. Adding tests is one of the most common requests after code is pushed to *GeoPandas*. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue. *GeoPandas* uses the `pytest testing system `_ and the convenient extensions in `numpy.testing `_. Writing tests ~~~~~~~~~~~~~ All tests should go into the ``tests`` directory. This folder contains many current examples of tests, and we suggest looking to these for inspiration. The ``.util`` module has some special ``assert`` functions that make it easier to make statements about whether GeoSeries or GeoDataFrame objects are equivalent. The easiest way to verify that your code is correct is to explicitly construct the result you expect, then compare the actual result to the expected correct result, using eg the function ``assert_geoseries_equal``. Running the test suite ~~~~~~~~~~~~~~~~~~~~~~ The tests can then be run directly inside your Git clone (without having to install *GeoPandas*) by typing:: pytest 6) Updating the Documentation ----------------------------- *GeoPandas* documentation resides in the ``doc`` folder. Changes to the docs are made by modifying the appropriate file in the ``source`` folder within ``doc``. *GeoPandas* docs use mixture of reStructuredText syntax for ``rst`` files, `which is explained here `_ and MyST syntax for ``md`` files `explained here `_. The docstrings follow the `Numpy Docstring standard `_. Some pages and examples are Jupyter notebooks converted to docs using `nbsphinx `_. Jupyter notebooks should be stored without the output. We highly encourage you to follow the `Google developer documentation style guide `_ when updating or creating new documentation. Once you have made your changes, you may try if they render correctly by building the docs using sphinx. To do so, you can navigate to the `doc` folder:: cd doc and type:: make html The resulting html pages will be located in ``doc/build/html``. In case of any errors, you can try to use ``make html`` within a new environment based on environment.yml specification in the ``doc`` folder. You may need to register Jupyter kernel as ``geopandas_docs``. Using conda:: cd doc conda env create -f environment.yml conda activate geopandas_docs python -m ipykernel install --user --name geopandas_docs make html For minor updates, you can skip the ``make html`` part as reStructuredText and MyST syntax are usually quite straightforward. 7) Submitting a Pull Request ------------------------------ Once you've made changes and pushed them to your forked repository, you then submit a pull request to have them integrated into the *GeoPandas* code base. You can find a pull request (or PR) tutorial in the `GitHub's Help Docs `_. .. _contributing_style: Style Guide & Linting --------------------- GeoPandas follows the `PEP8 `_ standard and uses `Black `_ and `Flake8 `_ to ensure a consistent code format throughout the project. Continuous Integration (GitHub Actions) will run those tools and report any stylistic errors in your code. Therefore, it is helpful before submitting code to run the check yourself:: black geopandas git diff upstream/master -u -- "*.py" | flake8 --diff to auto-format your code. Additionally, many editors have plugins that will apply ``black`` as you edit files. Optionally (but recommended), you can setup `pre-commit hooks `_ to automatically run ``black`` and ``flake8`` when you make a git commit. This can be done by installing ``pre-commit``:: $ python -m pip install pre-commit From the root of the geopandas repository, you should then install the ``pre-commit`` included in *GeoPandas*:: $ pre-commit install Then ``black`` and ``flake8`` will be run automatically each time you commit changes. You can skip these checks with ``git commit --no-verify``. Commit message conventions -------------------------- Commit your changes to your local repository with an explanatory message. GeoPandas uses the pandas convention for commit message prefixes and layout. Here are some common prefixes along with general guidelines for when to use them: * ENH: Enhancement, new functionality * BUG: Bug fix * DOC: Additions/updates to documentation * TST: Additions/updates to tests * BLD: Updates to the build process/scripts * PERF: Performance improvement * TYP: Type annotations * CLN: Code cleanup The following defines how a commit message should be structured. Please refer to the relevant GitHub issues in your commit message using GH1234 or #1234. Either style is fine, but the former is generally preferred: * a subject line with `< 80` chars. * One blank line. * Optionally, a commit message body. Now you can commit your changes in your local repository:: git commit -m geopandas-0.10.2/doc/source/community/ecosystem.md000066400000000000000000000402531413247402600221700ustar00rootroot00000000000000# Ecosystem ## GeoPandas dependencies GeoPandas brings together the full capability of `pandas` and the open-source geospatial tools `Shapely`, which brings manipulation and analysis of geometric objects backed by [`GEOS`](https://trac.osgeo.org/geos) library, `Fiona`, allowing us to read and write geographic data files using [`GDAL`](https://gdal.org), and `pyproj`, a library for cartographic projections and coordinate transformations, which is a Python interface to [`PROJ`](https://proj.org). Furthermore, GeoPandas has several optional dependencies as `rtree`, `pygeos`, `mapclassify`, or `geopy`. ### Required dependencies #### [pandas](https://github.com/pandas-dev/pandas) `pandas` is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. #### [Shapely](https://github.com/Toblerity/Shapely) `Shapely` is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed `GEOS` (the engine of PostGIS) and `JTS` (from which `GEOS` is ported) libraries. `Shapely` is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are. #### [Fiona](https://github.com/Toblerity/Fiona) `Fiona` is `GDAL’s` neat and nimble vector API for Python programmers. Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to `OGR`. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as `pyproj`, `Rtree`, and `Shapely`. #### [pyproj](https://github.com/pyproj4/pyproj) `pyproj` is a Python interface to `PROJ` (cartographic projections and coordinate transformations library). GeoPandas uses a `pyproj.crs.CRS` object to keep track of the projection of each `GeoSeries` and its `Transformer` object to manage re-projections. ### Optional dependencies #### [rtree](https://github.com/Toblerity/rtree) `Rtree` is a ctypes Python wrapper of `libspatialindex` that provides a number of advanced spatial indexing features for the spatially curious Python user. #### [PyGEOS](https://github.com/pygeos/pygeos) `PyGEOS` is a C/Python library with vectorized geometry functions. The geometry operations are done in the open-source geometry library `GEOS`. PyGEOS wraps these operations in `NumPy` ufuncs providing a performance improvement when operating on arrays of geometries. #### [mapclassify](https://github.com/pysal/mapclassify) `mapclassify` provides functionality for Choropleth map classification. Currently, fifteen different classification schemes are available, including a highly-optimized implementation of Fisher-Jenks optimal classification. Each scheme inherits a common structure that ensures computations are scalable and supports applications in streaming contexts. #### [geopy](https://github.com/geopy/geopy) `geopy` is a Python client for several popular geocoding web services. `geopy` makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources. #### [matplotlib](https://github.com/matplotlib/matplotlib) `Matplotlib` is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. ## GeoPandas ecosystem Various packages are built on top of GeoPandas addressing specific geospatial data processing needs, analysis, and visualization. Below is an incomplete list (in no particular order) of tools which form the GeoPandas-related Python ecosystem. ### Spatial analysis and Machine Learning #### [PySAL](https://github.com/pysal/pysal) `PySAL`, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. `PySAL` is a family of packages, some of which are listed below. ##### [libpysal](https://github.com/pysal/libpysal) `libpysal` provides foundational algorithms and data structures that support the rest of the library. This currently includes the following modules: input/output (`io`), which provides readers and writers for common geospatial file formats; weights (`weights`), which provides the main class to store spatial weights matrices, as well as several utilities to manipulate and operate on them; computational geometry (`cg`), with several algorithms, such as Voronoi tessellations or alpha shapes that efficiently process geometric shapes; and an additional module with example data sets (`examples`). ##### [esda](https://github.com/pysal/esda) `esda` implements methods for the analysis of both global (map-wide) and local (focal) spatial autocorrelation, for both continuous and binary data. In addition, the package increasingly offers cutting-edge statistics about boundary strength and measures of aggregation error in statistical analyses. ##### [segregation](https://github.com/pysal/segregation) `segregation` package calculates over 40 different segregation indices and provides a suite of additional features for measurement, visualization, and hypothesis testing that together represent the state of the art in quantitative segregation analysis. ##### [mgwr](https://github.com/pysal/mgwr) `mgwr` provides scalable algorithms for estimation, inference, and prediction using single- and multi-scale geographically weighted regression models in a variety of generalized linear model frameworks, as well as model diagnostics tools. ##### [tobler](https://github.com/pysal/tobler) `tobler` provides functionality for areal interpolation and dasymetric mapping. `tobler` includes functionality for interpolating data using area-weighted approaches, regression model-based approaches that leverage remotely-sensed raster data as auxiliary information, and hybrid approaches. #### [movingpandas](https://github.com/anitagraser/movingpandas) `MovingPandas` is a package for dealing with movement data. `MovingPandas` implements a `Trajectory` class and corresponding methods based on GeoPandas. A trajectory has a time-ordered series of point geometries. These points and associated attributes are stored in a `GeoDataFrame`. `MovingPandas` implements spatial and temporal data access and analysis functions as well as plotting functions. #### [momepy](https://github.com/martinfleis/momepy) `momepy` is a library for quantitative analysis of urban form - urban morphometrics. It is built on top of `GeoPandas`, `PySAL` and `networkX`. `momepy` aims to provide a wide range of tools for a systematic and exhaustive analysis of urban form. It can work with a wide range of elements, while focused on building footprints and street networks. #### [geosnap](https://github.com/spatialucr/geosnap) `geosnap` makes it easier to explore, model, analyze, and visualize the social and spatial dynamics of neighborhoods. `geosnap` provides a suite of tools for creating socio-spatial datasets, harmonizing those datasets into consistent set of time-static boundaries, modeling bespoke neighborhoods and prototypical neighborhood types, and modeling neighborhood change using classic and spatial statistical methods. It also provides a set of static and interactive visualization tools to help you display and understand the critical information at each step of the process. #### [mesa-geo](https://github.com/Corvince/mesa-geo) `mesa-geo` implements a GeoSpace that can host GIS-based GeoAgents, which are like normal Agents, except they have a shape attribute that is a `Shapely` object. You can use `Shapely` directly to create arbitrary shapes, but in most cases you will want to import your shapes from a file. Mesa-geo allows you to create GeoAgents from any vector data file (e.g. shapefiles), valid GeoJSON objects or a GeoPandas `GeoDataFrame`. #### [Pyspatialml](https://github.com/stevenpawley/Pyspatialml) `Pyspatialml` is a Python module for applying `scikit-learn` machine learning models to 'stacks' of raster datasets. Pyspatialml includes functions and classes for working with multiple raster datasets and performing a typical machine learning workflow consisting of extracting training data and applying the predict or `predict_proba` methods of `scikit-learn` estimators to a stack of raster datasets. Pyspatialml is built upon the `rasterio` Python module for all of the heavy lifting, and is also designed for working with vector data using the `geopandas` module. #### [PyGMI](https://github.com/Patrick-Cole/pygmi) `PyGMI` stands for Python Geoscience Modelling and Interpretation. It is a modelling and interpretation suite aimed at magnetic, gravity and other datasets. ### Visualization #### [hvPlot](https://hvplot.holoviz.org/user_guide/Geographic_Data.html#Geopandas) `hvPlot` provides interactive Bokeh-based plotting for GeoPandas dataframes and series using the same API as the Matplotlib `.plot()` support that comes with GeoPandas. hvPlot makes it simple to pan and zoom into your plots, use widgets to explore multidimensional data, and render even the largest datasets in web browsers using [Datashader](https://datashader.org). #### [contextily](https://github.com/geopandas/contextily) `contextily` is a small Python 3 (3.6 and above) package to retrieve tile maps from the internet. It can add those tiles as basemap to `matplotlib` figures or write tile maps to disk into geospatial raster files. Bounding boxes can be passed in both WGS84 (EPSG:4326) and Spheric Mercator (EPSG:3857). #### [cartopy](https://github.com/SciTools/cartopy) `Cartopy` is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: object oriented projection definitions; point, line, polygon and image transformations between projections; integration to expose advanced mapping in `Matplotlib` with a simple and intuitive interface; powerful vector data handling by integrating shapefile reading with `Shapely` capabilities. #### [bokeh](https://github.com/bokeh/bokeh) `Bokeh` is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. `Bokeh` can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications. #### [folium](https://github.com/python-visualization/folium) `folium` builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the `Leaflet.js` library. Manipulate your data in Python, then visualize it in a `Leaflet` map via `folium`. #### [kepler.gl](https://github.com/keplergl/kepler.gl) `Kepler.gl` is a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets. Built on top of Mapbox GL and `deck.gl`, `kepler.gl` can render millions of points representing thousands of trips and perform spatial aggregations on the fly. #### [geoplot](https://github.com/ResidentMario/geoplot) `geoplot` is a high-level Python geospatial plotting library. It's an extension to `cartopy` and `matplotlib` which makes mapping easy: like `seaborn` for geospatial. It comes with the high-level plotting API, native projection support and compatibility with `matplotlib`. #### [GeoViews](https://github.com/holoviz/geoviews) `GeoViews` is a Python library that makes it easy to explore and visualize any data that includes geographic locations, with native support for GeoPandas dataframes and series objects. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map! #### [EarthPy](https://github.com/earthlab/earthpy) `EarthPy` is a python package that makes it easier to plot and work with spatial raster and vector data using open source tools. `Earthpy` depends upon `geopandas` which has a focus on vector data and `rasterio` with facilitates input and output of raster data files. It also requires `matplotlib` for plotting operations. `EarthPy’s` goal is to make working with spatial data easier for scientists. #### [splot](https://github.com/pysal/splot) `splot` provides statistical visualizations for spatial analysis. It methods for visualizing global and local spatial autocorrelation (through Moran scatterplots and cluster maps), temporal analysis of cluster dynamics (through heatmaps and rose diagrams), and multivariate choropleth mapping (through value-by-alpha maps). A high level API supports the creation of publication-ready visualizations #### [legendgram](https://github.com/pysal/legendgram) `legendgram` is a small package that provides "legendgrams" legends that visualize the distribution of observations by color in a given map. These distributional visualizations for map classification schemes assist in analytical cartography and spatial data visualization. ### Geometry manipulation #### [TopoJSON](https://github.com/mattijn/topojson) `topojson` is a library for creating a TopoJSON encoding of nearly any geographical object in Python. With topojson it is possible to reduce the size of your geographical data, typically by orders of magnitude. It is able to do so through eliminating redundancy through computation of a topology, fixed-precision integer encoding of coordinates, and simplification and quantization of arcs. #### [geocube](https://github.com/corteva/geocube) Tool to convert geopandas vector data into rasterized `xarray` data. ### Data retrieval #### [OSMnx](https://github.com/gboeing/osmnx) `OSMnx` is a Python package that lets you download spatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code and then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time. #### [pyrosm](https://github.com/HTenkanen/pyrosm) `Pyrosm` is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (`*.osm.pbf`) into Geopandas `GeoDataFrames`. Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible to parse the data from OSM with more specific filters. #### [geobr](https://github.com/ipeaGIT/geobr) `geobr` is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology. #### [cenpy](https://github.com/cenpy-devs/cenpy) An interface to explore and query the US Census API and return Pandas `Dataframes`. This package is intended for exploratory data analysis and draws inspiration from sqlalchemy-like interfaces and `acs.R`. With separate APIs for application developers and folks who only want to get their data quickly & painlessly, `cenpy` should meet the needs of most who aim to get US Census Data into Python. ```{admonition} Expand this page Do know a package which should be here? [Let us know](https://github.com/geopandas/geopandas/issues) or [add it by yourself](contributing.rst)! ``` geopandas-0.10.2/doc/source/conf.py000066400000000000000000000326431413247402600171120ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # GeoPandas documentation build configuration file, created by # sphinx-quickstart on Tue Oct 15 08:08:14 2013. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os import warnings # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ "IPython.sphinxext.ipython_console_highlighting", "IPython.sphinxext.ipython_directive", "sphinx_gallery.load_style", "sphinx.ext.autosummary", "sphinx.ext.intersphinx", "sphinx.ext.autodoc", "myst_parser", "nbsphinx", "numpydoc", "sphinx_toggleprompt", "matplotlib.sphinxext.plot_directive", ] # continue doc build and only print warnings/errors in examples ipython_warning_is_error = False ipython_exec_lines = [ # ensure that dataframes are not truncated in the IPython code blocks "import pandas as _pd", '_pd.set_option("display.max_columns", 20)', '_pd.set_option("display.width", 100)', ] # Fix issue with warnings from numpydoc (see discussion in PR #534) numpydoc_show_class_members = False def setup(app): app.add_css_file("custom.css") # may also be an URL # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] autosummary_generate = True nbsphinx_execute = "always" nbsphinx_allow_errors = True nbsphinx_kernel_name = "python3" # suppress matplotlib warning in examples warnings.filterwarnings( "ignore", category=UserWarning, message="Matplotlib is currently using agg, which is a" " non-GUI backend, so cannot show the figure.", ) # The suffix of source filenames. source_suffix = [".rst", ".md"] # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = "index" # General information about the project. project = u"GeoPandas" copyright = u"2013–2021, GeoPandas developers" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. import geopandas version = release = geopandas.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { "search_bar_position": "sidebar", "github_url": "https://github.com/geopandas/geopandas", "twitter_url": "https://twitter.com/geopandas", } # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "_static/logo/geopandas_logo_web.svg" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = "_static/logo/favicon.png" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # Add redirect for previously existing pages, each item is like `(from_old, to_new)` moved_pages = [ # user guide ("aggregation_with_dissolve", "docs/user_guide/aggregation_with_dissolve"), ("data_structures", "docs/user_guide/data_structures"), ("geocoding", "docs/user_guide/geocoding"), ("geometric_manipulations", "docs/user_guide/geometric_manipulations"), ("indexing", "docs/user_guide/indexing"), ("io", "docs/user_guide/io"), ("mapping", "docs/user_guide/mapping"), ("mergingdata", "docs/user_guide/mergingdata"), ("missing_empty", "docs/user_guide/missing_empty"), ("projections", "docs/user_guide/projections"), ("set_operations", "docs/user_guide/set_operations"), # other ("install", "getting_started/install"), ("reference", "docs/reference"), ("changelog", "docs/changelog"), ("code_of_conduct", "community/code_of_conduct"), ("contributing", "community/contributing"), ] html_additional_pages = {page[0]: "redirect.html" for page in moved_pages} html_context = {"redirects": {old: new for old, new in moved_pages}} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = "GeoPandasdoc" # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ("index", "GeoPandas.tex", u"GeoPandas Documentation", u"Kelsey Jordahl", "manual") ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [("index", "geopandas", u"GeoPandas Documentation", [u"Kelsey Jordahl"], 1)] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( "index", "GeoPandas", u"GeoPandas Documentation", u"Kelsey Jordahl", "GeoPandas", "One line description of project.", "Miscellaneous", ) ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote' nbsphinx_prolog = r""" {% set docname = env.doc2path(env.docname, base=None) %} .. only:: html .. role:: raw-html(raw) :format: html .. note:: | This page was generated from `{{ docname }}`__. | Interactive online version: :raw-html:`Binder badge` __ https://github.com/geopandas/geopandas/blob/master/doc/source/{{ docname }} """ # --Options for sphinx extensions ----------------------------------------------- # connect docs in other projects intersphinx_mapping = { "cartopy": ( "https://scitools.org.uk/cartopy/docs/latest/", "https://scitools.org.uk/cartopy/docs/latest/objects.inv", ), "contextily": ( "https://contextily.readthedocs.io/en/stable/", "https://contextily.readthedocs.io/en/stable/objects.inv", ), "fiona": ( "https://fiona.readthedocs.io/en/stable/", "https://fiona.readthedocs.io/en/stable/objects.inv", ), "folium": ( "https://python-visualization.github.io/folium/", "https://python-visualization.github.io/folium/objects.inv", ), "geoplot": ( "https://residentmario.github.io/geoplot/index.html", "https://residentmario.github.io/geoplot/objects.inv", ), "geopy": ( "https://geopy.readthedocs.io/en/stable/", "https://geopy.readthedocs.io/en/stable/objects.inv", ), "libpysal": ( "https://pysal.org/libpysal/", "https://pysal.org/libpysal/objects.inv", ), "mapclassify": ( "https://pysal.org/mapclassify/", "https://pysal.org/mapclassify/objects.inv", ), "matplotlib": ( "https://matplotlib.org/stable/", "https://matplotlib.org/stable/objects.inv", ), "pandas": ( "https://pandas.pydata.org/pandas-docs/stable/", "https://pandas.pydata.org/pandas-docs/stable/objects.inv", ), "pyarrow": ("https://arrow.apache.org/docs/", None), "pyepsg": ( "https://pyepsg.readthedocs.io/en/stable/", "https://pyepsg.readthedocs.io/en/stable/objects.inv", ), "pygeos": ( "https://pygeos.readthedocs.io/en/latest/", "https://pygeos.readthedocs.io/en/latest/objects.inv", ), "pyproj": ( "https://pyproj4.github.io/pyproj/stable/", "https://pyproj4.github.io/pyproj/stable/objects.inv", ), "python": ( "https://docs.python.org/3", "https://docs.python.org/3/objects.inv", ), "rtree": ( "https://rtree.readthedocs.io/en/stable/", "https://rtree.readthedocs.io/en/stable/objects.inv", ), "rasterio": ( "https://rasterio.readthedocs.io/en/stable/", "https://rasterio.readthedocs.io/en/stable/objects.inv", ), "shapely": ( "https://shapely.readthedocs.io/en/stable/", "https://shapely.readthedocs.io/en/stable/objects.inv", ), "branca": ( "https://python-visualization.github.io/branca/", "https://python-visualization.github.io/branca/objects.inv", ), "xyzservices": ( "https://xyzservices.readthedocs.io/en/stable/", "https://xyzservices.readthedocs.io/en/stable/objects.inv", ), } geopandas-0.10.2/doc/source/docs.rst000066400000000000000000000014261413247402600172700ustar00rootroot00000000000000Documentation ------------- The documentation of GeoPandas consists of four parts - :doc:`User Guide ` with explanation of the basic functionality, :doc:`Advanced Guide ` covering topics which assume knowledge of basics, :doc:`Examples `, and :doc:`API reference ` detailing every class, method, function and attribute used implemented by GeoPandas. .. container:: button :doc:`User Guide ` :doc:`Advanced Guide ` :doc:`Examples ` :doc:`API reference ` .. toctree:: :maxdepth: 2 :caption: Documentation User Guide Advanced Guide API reference Changelog geopandas-0.10.2/doc/source/docs/000077500000000000000000000000001413247402600165335ustar00rootroot00000000000000geopandas-0.10.2/doc/source/docs/advanced_guide.rst000066400000000000000000000011721413247402600222100ustar00rootroot00000000000000Advanced Guide ============== The Advanced Guide covers advanced usage of GeoPandas. Each page focuses on a single topic and outlines how it is implemented in GeoPandas, with reproducible examples. If you don't know anything about GeoPandas, start with the :doc:`Introduction to GeoPandas <../getting_started/introduction>`. Basic topics can be found in the :doc:`User Guide ` and further specification in the :doc:`API Reference `. .. note:: This section is currently work in progress. See the available pages below. .. toctree:: :maxdepth: 2 user_guide/missing_empty user_guide/reproject_fiona geopandas-0.10.2/doc/source/docs/changelog.rst000066400000000000000000000000431413247402600212110ustar00rootroot00000000000000.. include:: ../../../CHANGELOG.md geopandas-0.10.2/doc/source/docs/reference.rst000066400000000000000000000011671413247402600212300ustar00rootroot00000000000000.. _reference: API Reference ============= The API Reference provides an overview of all public objects, functions and methods implemented in GeoPandas. All classes and function exposed in ``geopandas.*`` namespace plus those listed in the reference are public. .. warning:: The ``geopandas.array`` and ``geopandas.base`` modules are private. Stable functionality in such modules is not guaranteed. .. toctree:: :maxdepth: 2 GeoSeries GeoDataFrame Input/output Tools Spatial index Testing geopandas-0.10.2/doc/source/docs/reference/000077500000000000000000000000001413247402600204715ustar00rootroot00000000000000geopandas-0.10.2/doc/source/docs/reference/geodataframe.rst000066400000000000000000000040131413247402600236400ustar00rootroot00000000000000============ GeoDataFrame ============ .. currentmodule:: geopandas A ``GeoDataFrame`` is a tabular data structure that contains a column which contains a ``GeoSeries`` storing geometry. Constructor ----------- .. autosummary:: :toctree: api/ GeoDataFrame Serialization / IO / conversion ------------------------------- .. autosummary:: :toctree: api/ GeoDataFrame.from_file GeoDataFrame.from_features GeoDataFrame.from_postgis GeoDataFrame.to_file GeoDataFrame.to_json GeoDataFrame.to_parquet GeoDataFrame.to_feather GeoDataFrame.to_postgis GeoDataFrame.to_wkb GeoDataFrame.to_wkt Projection handling ------------------- .. autosummary:: :toctree: api/ GeoDataFrame.crs GeoDataFrame.set_crs GeoDataFrame.to_crs GeoDataFrame.estimate_utm_crs Active geometry handling ------------------------ .. autosummary:: :toctree: api/ GeoDataFrame.rename_geometry GeoDataFrame.set_geometry Aggregating and exploding ------------------------- .. autosummary:: :toctree: api/ GeoDataFrame.dissolve GeoDataFrame.explode Spatial joins ------------- .. autosummary:: :toctree: api/ GeoDataFrame.sjoin GeoDataFrame.sjoin_nearest Overlay operations ------------------ .. autosummary:: :toctree: api/ GeoDataFrame.clip GeoDataFrame.overlay Plotting -------- .. autosummary:: :toctree: api/ GeoDataFrame.explore .. autosummary:: :toctree: api/ :template: accessor_callable.rst GeoDataFrame.plot Spatial index ------------- .. autosummary:: :toctree: api/ GeoDataFrame.sindex GeoDataFrame.has_sindex Indexing -------- .. autosummary:: :toctree: api/ GeoDataFrame.cx Interface --------- .. autosummary:: :toctree: api/ GeoDataFrame.__geo_interface__ GeoDataFrame.iterfeatures All pandas ``DataFrame`` methods are also available, although they may not operate in a meaningful way on the ``geometry`` column. All methods listed in `GeoSeries `__ work directly on an active geometry column of GeoDataFrame. geopandas-0.10.2/doc/source/docs/reference/geoseries.rst000066400000000000000000000061241413247402600232130ustar00rootroot00000000000000========= GeoSeries ========= .. currentmodule:: geopandas Constructor ----------- .. autosummary:: :toctree: api/ GeoSeries General methods and attributes ------------------------------ .. autosummary:: :toctree: api/ GeoSeries.area GeoSeries.boundary GeoSeries.bounds GeoSeries.total_bounds GeoSeries.length GeoSeries.geom_type GeoSeries.distance GeoSeries.representative_point GeoSeries.exterior GeoSeries.interiors GeoSeries.x GeoSeries.y GeoSeries.z Unary predicates ---------------- .. autosummary:: :toctree: api/ GeoSeries.is_empty GeoSeries.is_ring GeoSeries.is_simple GeoSeries.is_valid GeoSeries.has_z Binary Predicates ----------------- .. autosummary:: :toctree: api/ GeoSeries.contains GeoSeries.crosses GeoSeries.disjoint GeoSeries.geom_equals GeoSeries.geom_almost_equals GeoSeries.geom_equals_exact GeoSeries.intersects GeoSeries.overlaps GeoSeries.touches GeoSeries.within GeoSeries.covers GeoSeries.covered_by Set-theoretic Methods --------------------- .. autosummary:: :toctree: api/ GeoSeries.difference GeoSeries.intersection GeoSeries.symmetric_difference GeoSeries.union Constructive Methods and Attributes ----------------------------------- .. autosummary:: :toctree: api/ GeoSeries.buffer GeoSeries.boundary GeoSeries.centroid GeoSeries.convex_hull GeoSeries.envelope GeoSeries.simplify Affine transformations ---------------------- .. autosummary:: :toctree: api/ GeoSeries.affine_transform GeoSeries.rotate GeoSeries.scale GeoSeries.skew GeoSeries.translate Aggregating and exploding ------------------------- .. autosummary:: :toctree: api/ GeoSeries.unary_union GeoSeries.explode Serialization / IO / conversion ------------------------------- .. autosummary:: :toctree: api/ GeoSeries.from_file GeoSeries.from_wkb GeoSeries.from_wkt GeoSeries.from_xy GeoSeries.to_file GeoSeries.to_json GeoSeries.to_wkb GeoSeries.to_wkt Projection handling ------------------- .. autosummary:: :toctree: api/ GeoSeries.crs GeoSeries.set_crs GeoSeries.to_crs GeoSeries.estimate_utm_crs Missing values -------------- .. autosummary:: :toctree: api/ GeoSeries.fillna GeoSeries.isna GeoSeries.notna Overlay operations ------------------ .. autosummary:: :toctree: api/ GeoSeries.clip Plotting -------- .. autosummary:: :toctree: api/ GeoSeries.plot GeoSeries.explore Spatial index ------------- .. autosummary:: :toctree: api/ GeoSeries.sindex GeoSeries.has_sindex Indexing -------- .. autosummary:: :toctree: api/ GeoSeries.cx Interface --------- .. autosummary:: :toctree: api/ GeoSeries.__geo_interface__ Methods of pandas ``Series`` objects are also available, although not all are applicable to geometric objects and some may return a ``Series`` rather than a ``GeoSeries`` result when appropriate. The methods ``isna()`` and ``fillna()`` have been implemented specifically for ``GeoSeries`` and are expected to work correctly. geopandas-0.10.2/doc/source/docs/reference/io.rst000066400000000000000000000007211413247402600216320ustar00rootroot00000000000000============ Input/output ============ .. currentmodule:: geopandas GIS vector files ---------------- .. autosummary:: :toctree: api/ read_file GeoDataFrame.to_file PostGIS ------- .. autosummary:: :toctree: api/ read_postgis GeoDataFrame.to_postgis Feather ------- .. autosummary:: :toctree: api/ read_feather GeoDataFrame.to_feather Parquet ------- .. autosummary:: :toctree: api/ read_parquet GeoDataFrame.to_parquet geopandas-0.10.2/doc/source/docs/reference/sindex.rst000066400000000000000000000030741413247402600225210ustar00rootroot00000000000000============= Spatial index ============= .. currentmodule:: geopandas GeoPandas offers built-in support for spatial indexing using an R-Tree algorithm. Depending on the ability to import ``pygeos``, GeoPandas will either use ``pygeos.STRtree`` or ``rtree.index.Index``. The main interface for both is the same and follows the ``pygeos`` model. ``GeoSeries.sindex`` creates a spatial index, which can use the methods and properties documented below. Constructor ----------- .. autosummary:: :toctree: api/ GeoSeries.sindex Spatial Index object -------------------- The spatial index object returned from :attr:`GeoSeries.sindex` has the following methods: .. currentmodule:: geopandas.sindex.SpatialIndex .. autosummary:: :toctree: api/ intersection is_empty nearest query query_bulk size valid_query_predicates The concrete implementations currently available are ``geopandas.sindex.PyGEOSSTRTreeIndex`` and ``geopandas.sindex.RTreeIndex``. In addition to the methods listed above, the ``rtree``-based spatial index (``geopandas.sindex.RTreeIndex``) offers the full capability of ``rtree.index.Index`` - see the full API in the `rtree documentation`_. Similarly, the ``pygeos``-based spatial index (``geopandas.sindex.PyGEOSSTRTreeIndex``) offers the full capability of ``pygeos.STRtree``, including nearest-neighbor queries. See the full API in the `PyGEOS STRTree documentation`_. .. _rtree documentation: https://rtree.readthedocs.io/en/stable/class.html .. _PyGEOS STRTree documentation: https://pygeos.readthedocs.io/en/latest/strtree.html geopandas-0.10.2/doc/source/docs/reference/testing.rst000066400000000000000000000004301413247402600226750ustar00rootroot00000000000000======= Testing ======= .. currentmodule:: geopandas GeoPandas includes specific functions to test its objects. .. autosummary:: :toctree: api/ .. testing.geom_equals .. testing.geom_almost_equals testing.assert_geoseries_equal testing.assert_geodataframe_equal geopandas-0.10.2/doc/source/docs/reference/tools.rst000066400000000000000000000003711413247402600223640ustar00rootroot00000000000000===== Tools ===== .. currentmodule:: geopandas .. autosummary:: :toctree: api/ sjoin sjoin_nearest overlay clip tools.geocode tools.reverse_geocode tools.collect points_from_xy datasets.available datasets.get_path geopandas-0.10.2/doc/source/docs/user_guide.rst000066400000000000000000000020421413247402600214160ustar00rootroot00000000000000User Guide ========== The User Guide covers different parts of basic usage of GeoPandas. Each page focuses on a single topic and outlines how it is implemented in GeoPandas, with reproducible examples. If you don't know anything about GeoPandas, start with the :doc:`Introduction to GeoPandas <../getting_started/introduction>`. Advanced topics can be found in the :doc:`Advanced Guide ` and further specification in the :doc:`API Reference `. .. toctree:: :maxdepth: 2 Data Structures Reading and Writing Files Indexing and Selecting Data Making Maps and plots Interactive mapping Managing Projections Geometric Manipulations Set Operations with overlay Aggregation with dissolve Merging Data Geocoding geopandas-0.10.2/doc/source/docs/user_guide/000077500000000000000000000000001413247402600206665ustar00rootroot00000000000000geopandas-0.10.2/doc/source/docs/user_guide/aggregation_with_dissolve.rst000066400000000000000000000057601413247402600266620ustar00rootroot00000000000000.. ipython:: python :suppress: import geopandas import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]] Aggregation with dissolve ============================= Spatial data are often more granular than we need. For example, we might have data on sub-national units, but we're actually interested in studying patterns at the level of countries. In a non-spatial setting, when all we need are summary statistics of the data, we aggregate our data using the :meth:`~pandas.DataFrame.groupby` function. But for spatial data, we sometimes also need to aggregate geometric features. In the *geopandas* library, we can aggregate geometric features using the :meth:`~geopandas.GeoDataFrame.dissolve` function. :meth:`~geopandas.GeoDataFrame.dissolve` can be thought of as doing three things: (a) it dissolves all the geometries within a given group together into a single geometric feature (using the :attr:`~geopandas.GeoSeries.unary_union` method), and (b) it aggregates all the rows of data in a group using :ref:`groupby.aggregate `, and (c) it combines those two results. :meth:`~geopandas.GeoDataFrame.dissolve` Example ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Suppose we are interested in studying continents, but we only have country-level data like the country dataset included in *geopandas*. We can easily convert this to a continent-level dataset. First, let's look at the most simple case where we just want continent shapes and names. By default, :meth:`~geopandas.GeoDataFrame.dissolve` will pass ``'first'`` to :ref:`groupby.aggregate `. .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) world = world[['continent', 'geometry']] continents = world.dissolve(by='continent') @savefig continents1.png continents.plot(); continents.head() If we are interested in aggregate populations, however, we can pass different functions to the :meth:`~geopandas.GeoDataFrame.dissolve` method to aggregate populations using the ``aggfunc =`` argument: .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) world = world[['continent', 'geometry', 'pop_est']] continents = world.dissolve(by='continent', aggfunc='sum') @savefig continents2.png continents.plot(column = 'pop_est', scheme='quantiles', cmap='YlOrRd'); continents.head() .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig .. toctree:: :maxdepth: 2 Dissolve Arguments ~~~~~~~~~~~~~~~~~~ The ``aggfunc =`` argument defaults to 'first' which means that the first row of attributes values found in the dissolve routine will be assigned to the resultant dissolved geodataframe. However it also accepts other summary statistic options as allowed by :meth:`pandas.groupby ` including: * 'first' * 'last' * 'min' * 'max' * 'sum' * 'mean' * 'median' geopandas-0.10.2/doc/source/docs/user_guide/data_structures.rst000066400000000000000000000170521413247402600246410ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]] Data Structures ========================================= GeoPandas implements two main data structures, a :class:`GeoSeries` and a :class:`GeoDataFrame`. These are subclasses of :class:`pandas.Series` and :class:`pandas.DataFrame`, respectively. GeoSeries --------- A :class:`GeoSeries` is essentially a vector where each entry in the vector is a set of shapes corresponding to one observation. An entry may consist of only one shape (like a single polygon) or multiple shapes that are meant to be thought of as one observation (like the many polygons that make up the State of Hawaii or a country like Indonesia). *geopandas* has three basic classes of geometric objects (which are actually *shapely* objects): * Points / Multi-Points * Lines / Multi-Lines * Polygons / Multi-Polygons Note that all entries in a :class:`GeoSeries` need not be of the same geometric type, although certain export operations will fail if this is not the case. Overview of Attributes and Methods ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The :class:`GeoSeries` class implements nearly all of the attributes and methods of Shapely objects. When applied to a :class:`GeoSeries`, they will apply elementwise to all geometries in the series. Binary operations can be applied between two :class:`GeoSeries`, in which case the operation is carried out elementwise. The two series will be aligned by matching indices. Binary operations can also be applied to a single geometry, in which case the operation is carried out for each element of the series with that geometry. In either case, a :class:`~pandas.Series` or a :class:`GeoSeries` will be returned, as appropriate. A short summary of a few attributes and methods for GeoSeries is presented here, and a full list can be found in the :doc:`all attributes and methods page <../reference/geoseries>`. There is also a family of methods for creating new shapes by expanding existing shapes or applying set-theoretic operations like "union" described in :doc:`geometric manipulations `. Attributes ^^^^^^^^^^^^^^^ * :attr:`~GeoSeries.area`: shape area (units of projection -- see :doc:`projections `) * :attr:`~GeoSeries.bounds`: tuple of max and min coordinates on each axis for each shape * :attr:`~GeoSeries.total_bounds`: tuple of max and min coordinates on each axis for entire GeoSeries * :attr:`~GeoSeries.geom_type`: type of geometry. * :attr:`~GeoSeries.is_valid`: tests if coordinates make a shape that is reasonable geometric shape (`according to this `_). Basic Methods ^^^^^^^^^^^^^^ * :meth:`~GeoSeries.distance`: returns :class:`~pandas.Series` with minimum distance from each entry to ``other`` * :attr:`~GeoSeries.centroid`: returns :class:`GeoSeries` of centroids * :meth:`~GeoSeries.representative_point`: returns :class:`GeoSeries` of points that are guaranteed to be within each geometry. It does **NOT** return centroids. * :meth:`~GeoSeries.to_crs`: change coordinate reference system. See :doc:`projections ` * :meth:`~GeoSeries.plot`: plot :class:`GeoSeries`. See :doc:`mapping `. Relationship Tests ^^^^^^^^^^^^^^^^^^^ * :meth:`~GeoSeries.geom_almost_equals`: is shape almost the same as ``other`` (good when floating point precision issues make shapes slightly different) * :meth:`~GeoSeries.contains`: is shape contained within ``other`` * :meth:`~GeoSeries.intersects`: does shape intersect ``other`` GeoDataFrame ------------ A :class:`GeoDataFrame` is a tabular data structure that contains a :class:`GeoSeries`. The most important property of a :class:`GeoDataFrame` is that it always has one :class:`GeoSeries` column that holds a special status. This :class:`GeoSeries` is referred to as the :class:`GeoDataFrame`'s "geometry". When a spatial method is applied to a :class:`GeoDataFrame` (or a spatial attribute like ``area`` is called), this commands will always act on the "geometry" column. The "geometry" column -- no matter its name -- can be accessed through the :attr:`~GeoDataFrame.geometry` attribute (``gdf.geometry``), and the name of the ``geometry`` column can be found by typing ``gdf.geometry.name``. A :class:`GeoDataFrame` may also contain other columns with geometrical (shapely) objects, but only one column can be the active geometry at a time. To change which column is the active geometry column, use the :meth:`GeoDataFrame.set_geometry` method. An example using the ``worlds`` GeoDataFrame: .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) world.head() #Plot countries @savefig world_borders.png world.plot(); Currently, the column named "geometry" with country borders is the active geometry column: .. ipython:: python world.geometry.name We can also rename this column to "borders": .. ipython:: python world = world.rename(columns={'geometry': 'borders'}).set_geometry('borders') world.geometry.name Now, we create centroids and make it the geometry: .. ipython:: python :okwarning: world['centroid_column'] = world.centroid world = world.set_geometry('centroid_column') @savefig world_centroids.png world.plot(); **Note:** A :class:`GeoDataFrame` keeps track of the active column by name, so if you rename the active geometry column, you must also reset the geometry:: gdf = gdf.rename(columns={'old_name': 'new_name'}).set_geometry('new_name') **Note 2:** Somewhat confusingly, by default when you use the :func:`~geopandas.read_file` command, the column containing spatial objects from the file is named "geometry" by default, and will be set as the active geometry column. However, despite using the same term for the name of the column and the name of the special attribute that keeps track of the active column, they are distinct. You can easily shift the active geometry column to a different :class:`GeoSeries` with the :meth:`~GeoDataFrame.set_geometry` command. Further, ``gdf.geometry`` will always return the active geometry column, *not* the column named ``geometry``. If you wish to call a column named "geometry", and a different column is the active geometry column, use ``gdf['geometry']``, not ``gdf.geometry``. Attributes and Methods ~~~~~~~~~~~~~~~~~~~~~~ Any of the attributes calls or methods described for a :class:`GeoSeries` will work on a :class:`GeoDataFrame` -- effectively, they are just applied to the "geometry" :class:`GeoSeries`. However, :class:`GeoDataFrames ` also have a few extra methods for input and output which are described on the :doc:`Input and Output ` page and for geocoding with are described in :doc:`Geocoding `. .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig Display options --------------- GeoPandas has an ``options`` attribute with currently a single configuration option to control: .. ipython:: python import geopandas geopandas.options The ``geopandas.options.display_precision`` option can control the number of decimals to show in the display of coordinates in the geometry column. In the ``world`` example of above, the default is to show 5 decimals for geographic coordinates: .. ipython:: python world['centroid_column'].head() If you want to change this, for example to see more decimals, you can do: .. ipython:: python geopandas.options.display_precision = 9 world['centroid_column'].head() geopandas-0.10.2/doc/source/docs/user_guide/geocoding.rst000066400000000000000000000037201413247402600233600ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Geocoding ========== ``geopandas`` supports geocoding (i.e., converting place names to location on Earth) through `geopy`_, an optional dependency of ``geopandas``. The following example shows how to get the locations of boroughs in New York City, and plots those locations along with the detailed borough boundary file included within ``geopandas``. .. _geopy: http://geopy.readthedocs.io/ .. ipython:: python boros = geopandas.read_file(geopandas.datasets.get_path("nybb")) boros.BoroName boro_locations = geopandas.tools.geocode(boros.BoroName) boro_locations import matplotlib.pyplot as plt fig, ax = plt.subplots() boros.to_crs("EPSG:4326").plot(ax=ax, color="white", edgecolor="black"); @savefig boro_centers_over_bounds.png boro_locations.plot(ax=ax, color="red"); By default, the :func:`~geopandas.tools.geocode` function uses the `Photon geocoding API `__. But a different geocoding service can be specified with the ``provider`` keyword. The argument to ``provider`` can either be a string referencing geocoding services, such as ``'google'``, ``'bing'``, ``'yahoo'``, and ``'openmapquest'``, or an instance of a :mod:`Geocoder ` from :mod:`geopy`. See ``geopy.geocoders.SERVICE_TO_GEOCODER`` for the full list. For many providers, parameters such as API keys need to be passed as ``**kwargs`` in the :func:`~geopandas.tools.geocode` call. For example, to use the OpenStreetMap Nominatim geocoder, you need to specify a user agent: .. code-block:: python geopandas.tools.geocode(boros.BoroName, provider='nominatim', user_agent="my-application") .. attention:: Please consult the Terms of Service for the chosen provider. The example above uses ``'photon'`` (the default), which expects fair usage - extensive usage will be throttled. (`Photon's Terms of Use `_). geopandas-0.10.2/doc/source/docs/user_guide/geometric_manipulations.rst000066400000000000000000000174111413247402600263450ustar00rootroot00000000000000.. _geometric_manipulations: Geometric Manipulations ======================== *geopandas* makes available all the tools for geometric manipulations in the `shapely library `_. Note that documentation for all set-theoretic tools for creating new shapes using the relationship between two different spatial datasets -- like creating intersections, or differences -- can be found on the :doc:`set operations ` page. Constructive Methods ~~~~~~~~~~~~~~~~~~~~ .. method:: GeoSeries.buffer(distance, resolution=16) Returns a :class:`~geopandas.GeoSeries` of geometries representing all points within a given `distance` of each geometric object. .. attribute:: GeoSeries.boundary Returns a :class:`~geopandas.GeoSeries` of lower dimensional objects representing each geometries's set-theoretic `boundary`. .. attribute:: GeoSeries.centroid Returns a :class:`~geopandas.GeoSeries` of points for each geometric centroid. .. attribute:: GeoSeries.convex_hull Returns a :class:`~geopandas.GeoSeries` of geometries representing the smallest convex `Polygon` containing all the points in each object unless the number of points in the object is less than three. For two points, the convex hull collapses to a `LineString`; for 1, a `Point`. .. attribute:: GeoSeries.envelope Returns a :class:`~geopandas.GeoSeries` of geometries representing the point or smallest rectangular polygon (with sides parallel to the coordinate axes) that contains each object. .. method:: GeoSeries.simplify(tolerance, preserve_topology=True) Returns a :class:`~geopandas.GeoSeries` containing a simplified representation of each object. .. attribute:: GeoSeries.unary_union Return a geometry containing the union of all geometries in the :class:`~geopandas.GeoSeries`. Affine transformations ~~~~~~~~~~~~~~~~~~~~~~~~ .. method:: GeoSeries.affine_transform(self, matrix) Transform the geometries of the :class:`~geopandas.GeoSeries` using an affine transformation matrix .. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False) Rotate the coordinates of the :class:`~geopandas.GeoSeries`. .. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center') Scale the geometries of the :class:`~geopandas.GeoSeries` along each (x, y, z) dimensio. .. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False) Shear/Skew the geometries of the :class:`~geopandas.GeoSeries` by angles along x and y dimensions. .. method:: GeoSeries.translate(self, xoff=0.0, yoff=0.0, zoff=0.0) Shift the coordinates of the :class:`~geopandas.GeoSeries`. Examples of Geometric Manipulations ------------------------------------ .. sourcecode:: python >>> import geopandas >>> from geopandas import GeoSeries >>> from shapely.geometry import Polygon >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0)) dtype: geometry .. image:: ../../_static/test.png Some geographic operations return normal pandas object. The :attr:`~geopandas.GeoSeries.area` property of a :class:`~geopandas.GeoSeries` will return a :class:`pandas.Series` containing the area of each item in the :class:`~geopandas.GeoSeries`: .. sourcecode:: python >>> print(g.area) 0 0.5 1 1.0 2 1.0 dtype: float64 Other operations return GeoPandas objects: .. sourcecode:: python >>> g.buffer(0.5) 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ... 2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04... dtype: geometry .. image:: ../../_static/test_buffer.png GeoPandas objects also know how to plot themselves. GeoPandas uses `matplotlib`_ for plotting. To generate a plot of our GeoSeries, use: .. sourcecode:: python >>> g.plot() GeoPandas also implements alternate constructors that can read any data format recognized by `fiona`_. To read a zip file containing an ESRI shapefile with the `borough boundaries of New York City`_ (GeoPandas includes this as an example dataset): .. sourcecode:: python >>> nybb_path = geopandas.datasets.get_path('nybb') >>> boros = geopandas.read_file(nybb_path) >>> boros.set_index('BoroCode', inplace=True) >>> boros.sort_index(inplace=True) >>> boros BoroName Shape_Leng Shape_Area \ BoroCode 1 Manhattan 359299.096471 6.364715e+08 2 Bronx 464392.991824 1.186925e+09 3 Brooklyn 741080.523166 1.937479e+09 4 Queens 896344.047763 3.045213e+09 5 Staten Island 330470.010332 1.623820e+09 geometry BoroCode 1 MULTIPOLYGON (((981219.0557861328 188655.31579... 2 MULTIPOLYGON (((1012821.805786133 229228.26458... 3 MULTIPOLYGON (((1021176.479003906 151374.79699... 4 MULTIPOLYGON (((1029606.076599121 156073.81420... 5 MULTIPOLYGON (((970217.0223999023 145643.33221... .. image:: ../../_static/nyc.png .. sourcecode:: python >>> boros['geometry'].convex_hull BoroCode 1 POLYGON ((977855.4451904297 188082.3223876953,... 2 POLYGON ((1017949.977600098 225426.8845825195,... 3 POLYGON ((988872.8212280273 146772.0317993164,... 4 POLYGON ((1000721.531799316 136681.776184082, ... 5 POLYGON ((915517.6877458114 120121.8812543372,... dtype: geometry .. image:: ../../_static/nyc_hull.png To demonstrate a more complex operation, we'll generate a :class:`~geopandas.GeoSeries` containing 2000 random points: .. sourcecode:: python >>> import numpy as np >>> from shapely.geometry import Point >>> xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 >>> xc = (xmax - xmin) * np.random.random(2000) + xmin >>> yc = (ymax - ymin) * np.random.random(2000) + ymin >>> pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)]) Now draw a circle with fixed radius around each point: .. sourcecode:: python >>> circles = pts.buffer(2000) We can collapse these circles into a single :class:`MultiPolygon` geometry with .. sourcecode:: python >>> mp = circles.unary_union To extract the part of this geometry contained in each borough, we can just use: .. sourcecode:: python >>> holes = boros['geometry'].intersection(mp) .. image:: ../../_static/holes.png and to get the area outside of the holes: .. sourcecode:: python >>> boros_with_holes = boros['geometry'].difference(mp) .. image:: ../../_static/boros_with_holes.png Note that this can be simplified a bit, since ``geometry`` is available as an attribute on a :class:`~geopandas.GeoDataFrame`, and the :meth:`~geopandas.GeoSeries.intersection` and :meth:`~geopandas.GeoSeries.difference` methods are implemented with the "&" and "-" operators, respectively. For example, the latter could have been expressed simply as ``boros.geometry - mp``. It's easy to do things like calculate the fractional area in each borough that are in the holes: .. sourcecode:: python >>> holes.area / boros.geometry.area BoroCode 1 0.579939 2 0.586833 3 0.608174 4 0.582172 5 0.558075 dtype: float64 .. _matplotlib: http://matplotlib.org .. _fiona: http://fiona.readthedocs.io/en/latest/ .. _geopy: https://github.com/geopy/geopy .. _geo_interface: https://gist.github.com/sgillies/2217756 .. _borough boundaries of New York City: https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm .. toctree:: :maxdepth: 2 geopandas-0.10.2/doc/source/docs/user_guide/indexing.rst000066400000000000000000000023151413247402600232260ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Indexing and Selecting Data =========================== GeoPandas inherits the standard pandas_ methods for indexing/selecting data. This includes label based indexing with :attr:`~pandas.DataFrame.loc` and integer position based indexing with :attr:`~pandas.DataFrame.iloc`, which apply to both :class:`GeoSeries` and :class:`GeoDataFrame` objects. For more information on indexing/selecting, see the pandas_ documentation. .. _pandas: http://pandas.pydata.org/pandas-docs/stable/indexing.html In addition to the standard pandas_ methods, GeoPandas also provides coordinate based indexing with the :attr:`~GeoDataFrame.cx` indexer, which slices using a bounding box. Geometries in the :class:`GeoSeries` or :class:`GeoDataFrame` that intersect the bounding box will be returned. Using the ``world`` dataset, we can use this functionality to quickly select all countries whose boundaries extend into the southern hemisphere. .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) southern_world = world.cx[:, :0] @savefig world_southern.png southern_world.plot(figsize=(10, 3)); geopandas-0.10.2/doc/source/docs/user_guide/interactive_mapping.ipynb000066400000000000000000000110731413247402600257630ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "source": [ "# Interactive mapping\n", "\n", "Alongside static plots, `geopandas` can create interactive maps based on the [folium](https://python-visualization.github.io/folium/) library.\n", "\n", "Creating maps for interactive exploration mirrors the API of [static plots](../reference/api/geopandas.GeoDataFrame.plot.html) in an [explore()](../reference/api/geopandas.GeoDataFrame.explore.html) method of a GeoSeries or GeoDataFrame.\n", "\n", "Loading some example data:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "import geopandas\n", "\n", "nybb = geopandas.read_file(geopandas.datasets.get_path('nybb'))\n", "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", "cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "The simplest option is to use `GeoDataFrame.explore()`:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "nybb.explore()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Interactive plotting offers largely the same customisation as static one plus some features on top of that. Check the code below which plots a customised choropleth map. You can use `\"BoroName\"` column with NY boroughs names as an input of the choropleth, show (only) its name in the tooltip on hover but show all values on click. You can also pass custom background tiles (either a name supported by folium, a name recognized by `xyzservices.providers.query_name()`, XYZ URL or `xyzservices.TileProvider` object), specify colormap (all supported by `matplotlib`) and specify black outline." ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "nybb.explore( \n", " column=\"BoroName\", # make choropleth based on \"BoroName\" column\n", " tooltip=\"BoroName\", # show \"BoroName\" value in tooltip (on hover)\n", " popup=True, # show all values in popup (on click)\n", " tiles=\"CartoDB positron\", # use \"CartoDB positron\" tiles\n", " cmap=\"Set1\", # use \"Set1\" matplotlib colormap\n", " style_kwds=dict(color=\"black\") # use black outline\n", " )" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "The `explore()` method returns a `folium.Map` object, which can also be passed directly (as you do with `ax` in `plot()`). You can then use folium functionality directly on the resulting map. In the example below, you can plot two GeoDataFrames on the same map and add layer control using folium. You can also add additional tiles allowing you to change the background directly in the map." ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "import folium\n", "\n", "m = world.explore(\n", " column=\"pop_est\", # make choropleth based on \"BoroName\" column\n", " scheme=\"naturalbreaks\", # use mapclassify's natural breaks scheme\n", " legend=True, # show legend\n", " k=10, # use 10 bins\n", " legend_kwds=dict(colorbar=False), # do not use colorbar\n", " name=\"countries\" # name of the layer in the map\n", ")\n", "\n", "cities.explore(\n", " m=m, # pass the map object\n", " color=\"red\", # use red color on all points\n", " marker_kwds=dict(radius=10, fill=True), # make marker radius 10px with fill\n", " tooltip=\"name\", # show \"name\" column in the tooltip\n", " tooltip_kwds=dict(labels=False), # do not show column label in the tooltip\n", " name=\"cities\" # name of the layer in the map\n", ")\n", "\n", "folium.TileLayer('Stamen Toner', control=True).add_to(m) # use folium to add alternative tiles\n", "folium.LayerControl().add_to(m) # use folium to add layer control\n", "\n", "m # show map" ], "outputs": [], "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 5 }geopandas-0.10.2/doc/source/docs/user_guide/io.rst000066400000000000000000000166401413247402600220360ustar00rootroot00000000000000.. _io: Reading and Writing Files ========================= Reading Spatial Data --------------------- *geopandas* can read almost any vector-based spatial data format including ESRI shapefile, GeoJSON files and more using the command:: geopandas.read_file() which returns a GeoDataFrame object. This is possible because *geopandas* makes use of the great `fiona `_ library, which in turn makes use of a massive open-source program called `GDAL/OGR `_ designed to facilitate spatial data transformations. Any arguments passed to :func:`geopandas.read_file` after the file name will be passed directly to :func:`fiona.open`, which does the actual data importation. In general, :func:`geopandas.read_file` is pretty smart and should do what you want without extra arguments, but for more help, type:: import fiona; help(fiona.open) Among other things, one can explicitly set the driver (shapefile, GeoJSON) with the ``driver`` keyword, or pick a single layer from a multi-layered file with the ``layer`` keyword:: countries_gdf = geopandas.read_file("package.gpkg", layer='countries') Where supported in :mod:`fiona`, *geopandas* can also load resources directly from a web URL, for example for GeoJSON files from `geojson.xyz `_:: url = "http://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_land.geojson" df = geopandas.read_file(url) You can also load ZIP files that contain your data:: zipfile = "zip:///Users/name/Downloads/cb_2017_us_state_500k.zip" states = geopandas.read_file(zipfile) If the dataset is in a folder in the ZIP file, you have to append its name:: zipfile = "zip:///Users/name/Downloads/gadm36_AFG_shp.zip!data" If there are multiple datasets in a folder in the ZIP file, you also have to specify the filename:: zipfile = "zip:///Users/name/Downloads/gadm36_AFG_shp.zip!data/gadm36_AFG_1.shp" It is also possible to read any file-like objects with a :func:`os.read` method, such as a file handler (e.g. via built-in :func:`open` function) or :class:`~io.StringIO`:: filename = "test.geojson" file = open(filename) df = geopandas.read_file(file) File-like objects from `fsspec `_ can also be used to read data, allowing for any combination of storage backends and caching supported by that project:: path = "simplecache::http://download.geofabrik.de/antarctica-latest-free.shp.zip" with fsspec.open(path) as file: df = geopandas.read_file(file) You can also read path objects:: import pathlib path_object = pathlib.path(filename) df = geopandas.read_file(path_object) Reading subsets of the data ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Since geopandas is powered by Fiona, which is powered by GDAL, you can take advantage of pre-filtering when loading in larger datasets. This can be done geospatially with a geometry or bounding box. You can also filter rows loaded with a slice. Read more at :func:`geopandas.read_file`. Geometry Filter ^^^^^^^^^^^^^^^ .. versionadded:: 0.7.0 The geometry filter only loads data that intersects with the geometry. .. code-block:: python gdf_mask = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres") ) gdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_cities"), mask=gdf_mask[gdf_mask.continent=="Africa"], ) Bounding Box Filter ^^^^^^^^^^^^^^^^^^^ .. versionadded:: 0.1.0 The bounding box filter only loads data that intersects with the bounding box. .. code-block:: python bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244 ) gdf = geopandas.read_file( geopandas.datasets.get_path("nybb"), bbox=bbox, ) Row Filter ^^^^^^^^^^ .. versionadded:: 0.7.0 Filter the rows loaded in from the file using an integer (for the first n rows) or a slice object. .. code-block:: python gdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres"), rows=10, ) gdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres"), rows=slice(10, 20), ) Field/Column Filters ^^^^^^^^^^^^^^^^^^^^ Load in a subset of fields from the file: .. note:: Requires Fiona 1.8+ .. code-block:: python gdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres"), ignore_fields=["iso_a3", "gdp_md_est"], ) Skip loading geometry from the file: .. note:: Requires Fiona 1.8+ .. note:: Returns :obj:`pandas.DataFrame` .. code-block:: python pdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres"), ignore_geometry=True, ) Writing Spatial Data --------------------- GeoDataFrames can be exported to many different standard formats using the :meth:`geopandas.GeoDataFrame.to_file` method. For a full list of supported formats, type ``import fiona; fiona.supported_drivers``. In addition, GeoDataFrames can be uploaded to `PostGIS `__ database (starting with GeoPandas 0.8) by using the :meth:`geopandas.GeoDataFrame.to_postgis` method. .. note:: GeoDataFrame can contain more field types than supported by most of the file formats. For example tuples or lists can be easily stored in the GeoDataFrame, but saving them to e.g. GeoPackage or Shapefile will raise a ValueError. Before saving to a file, they need to be converted to a format supported by a selected driver. **Writing to Shapefile**:: countries_gdf.to_file("countries.shp") **Writing to GeoJSON**:: countries_gdf.to_file("countries.geojson", driver='GeoJSON') **Writing to GeoPackage**:: countries_gdf.to_file("package.gpkg", layer='countries', driver="GPKG") cities_gdf.to_file("package.gpkg", layer='cities', driver="GPKG") Spatial databases ----------------- *geopandas* can also get data from a PostGIS database using the :func:`geopandas.read_postgis` command. Writing to PostGIS:: from sqlalchemy import create_engine db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydatabase"; engine = create_engine(db_connection_url) countries_gdf.to_postgis("countries_table", con=engine) Apache Parquet and Feather file formats --------------------------------------- .. versionadded:: 0.8.0 GeoPandas supports writing and reading the Apache Parquet and Feather file formats. `Apache Parquet `__ is an efficient, columnar storage format (originating from the Hadoop ecosystem). It is a widely used binary file format for tabular data. The Feather file format is the on-disk representation of the `Apache Arrow `__ memory format, an open standard for in-memory columnar data. The :func:`geopandas.read_parquet`, :func:`geopandas.read_feather`, :meth:`GeoDataFrame.to_parquet` and :meth:`GeoDataFrame.to_feather` methods enable fast roundtrip from GeoPandas to those binary file formats, preserving the spatial information. .. warning:: This is an initial implementation of Parquet file support and associated metadata. This is tracking version 0.1.0 of the metadata specification at: https://github.com/geopandas/geo-arrow-spec This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Parquet or Feather files. geopandas-0.10.2/doc/source/docs/user_guide/mapping.rst000066400000000000000000000233331413247402600230570ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]] import matplotlib.pyplot as plt plt.close('all') Mapping and Plotting Tools ========================================= *geopandas* provides a high-level interface to the matplotlib_ library for making maps. Mapping shapes is as easy as using the :meth:`~GeoDataFrame.plot()` method on a :class:`GeoSeries` or :class:`GeoDataFrame`. .. _matplotlib: https://matplotlib.org/stable/ Loading some example data: .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities')) We can now plot those GeoDataFrames: .. ipython:: python # Examine country GeoDataFrame world.head() # Basic plot, random colors @savefig world_randomcolors.png world.plot(); Note that in general, any options one can pass to `pyplot `_ in matplotlib_ (or `style options that work for lines `_) can be passed to the :meth:`~GeoDataFrame.plot` method. Choropleth Maps ----------------- *geopandas* makes it easy to create Choropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors. .. ipython:: python :okwarning: # Plot by GDP per capita world = world[(world.pop_est>0) & (world.name!="Antarctica")] world['gdp_per_cap'] = world.gdp_md_est / world.pop_est @savefig world_gdp_per_cap.png world.plot(column='gdp_per_cap'); Creating a legend ~~~~~~~~~~~~~~~~~ When plotting a map, one can enable a legend using the ``legend`` argument: .. ipython:: python # Plot population estimates with an accurate legend import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) @savefig world_pop_est.png world.plot(column='pop_est', ax=ax, legend=True) However, the default appearance of the legend and plot axes may not be desirable. One can define the plot axes (with ``ax``) and the legend axes (with ``cax``) and then pass those in to the :meth:`~GeoDataFrame.plot` call. The following example uses ``mpl_toolkits`` to vertically align the plot axes and the legend axes: .. ipython:: python # Plot population estimates with an accurate legend from mpl_toolkits.axes_grid1 import make_axes_locatable fig, ax = plt.subplots(1, 1) divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.1) @savefig world_pop_est_fixed_legend_height.png world.plot(column='pop_est', ax=ax, legend=True, cax=cax) And the following example plots the color bar below the map and adds its label using ``legend_kwds``: .. ipython:: python # Plot population estimates with an accurate legend import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) @savefig world_pop_est_horizontal.png world.plot(column='pop_est', ax=ax, legend=True, legend_kwds={'label': "Population by Country", 'orientation': "horizontal"}) Choosing colors ~~~~~~~~~~~~~~~~ One can also modify the colors used by :meth:`~GeoDataFrame.plot` with the ``cmap`` option (for a full list of colormaps, see the `matplotlib website `_): .. ipython:: python @savefig world_gdp_per_cap_red.png world.plot(column='gdp_per_cap', cmap='OrRd'); To make the color transparent for when you just want to show the boundary, you have two options. One option is to do ``world.plot(facecolor="none", edgecolor="black")``. However, this can cause a lot of confusion because ``"none"`` and ``None`` are different in the context of using ``facecolor`` and they do opposite things. ``None`` does the "default behavior" based on matplotlib, and if you use it for ``facecolor``, it actually adds a color. The second option is to use ``world.boundary.plot()``. This option is more explicit and clear.: .. ipython:: python @savefig world_gdp_per_cap_transparent.png world.boundary.plot(); The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``mapclassify`` installed, which can be accomplished via ``conda install -c conda-forge mapclassify``). The ``scheme`` option can be set to any scheme provided by mapclassify (e.g. 'box_plot', 'equal_interval', 'fisher_jenks', 'fisher_jenks_sampled', 'headtail_breaks', 'jenks_caspall', 'jenks_caspall_forced', 'jenks_caspall_sampled', 'max_p_classifier', 'maximum_breaks', 'natural_breaks', 'quantiles', 'percentiles', 'std_mean' or 'user_defined'). Arguments can be passed in classification_kwds dict. See the `mapclassify documentation `_ for further details about these map classification schemes. .. ipython:: python @savefig world_gdp_per_cap_quantiles.png world.plot(column='gdp_per_cap', cmap='OrRd', scheme='quantiles'); Missing data ~~~~~~~~~~~~ In some cases one may want to plot data which contains missing values - for some features one simply does not know the value. Geopandas (from the version 0.7) by defaults ignores such features. .. ipython:: python import numpy as np world.loc[np.random.choice(world.index, 40), 'pop_est'] = np.nan @savefig missing_vals.png world.plot(column='pop_est'); However, passing ``missing_kwds`` one can specify the style and label of features containing None or NaN. .. ipython:: python @savefig missing_vals_grey.png world.plot(column='pop_est', missing_kwds={'color': 'lightgrey'}); @savefig missing_vals_hatch.png world.plot( column="pop_est", legend=True, scheme="quantiles", figsize=(15, 10), missing_kwds={ "color": "lightgrey", "edgecolor": "red", "hatch": "///", "label": "Missing values", }, ); Other map customizations ~~~~~~~~~~~~~~~~~~~~~~~~ Maps usually do not have to have axis labels. You can turn them off using ``set_axis_off()`` or ``axis("off")`` axis methods. .. ipython:: python ax = world.plot() @savefig set_axis_off.png ax.set_axis_off(); Maps with Layers ----------------- There are two strategies for making a map with multiple layers -- one more succinct, and one that is a little more flexible. Before combining maps, however, remember to always ensure they share a common CRS (so they will align). .. ipython:: python # Look at capitals # Note use of standard `pyplot` line style options @savefig capitals.png cities.plot(marker='*', color='green', markersize=5); # Check crs cities = cities.to_crs(world.crs) # Now we can overlay over country outlines # And yes, there are lots of island capitals # apparently in the middle of the ocean! **Method 1** .. ipython:: python base = world.plot(color='white', edgecolor='black') @savefig capitals_over_countries_1.png cities.plot(ax=base, marker='o', color='red', markersize=5); **Method 2: Using matplotlib objects** .. ipython:: python import matplotlib.pyplot as plt fig, ax = plt.subplots() # set aspect to equal. This is done automatically # when using *geopandas* plot on it's own, but not when # working with pyplot directly. ax.set_aspect('equal') world.plot(ax=ax, color='white', edgecolor='black') cities.plot(ax=ax, marker='o', color='red', markersize=5) @savefig capitals_over_countries_2.png plt.show(); Control the order of multiple layers in a plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When plotting multiple layers, use ``zorder`` to take control of the order of layers being plotted. The lower the ``zorder`` is, the lower the layer is on the map and vice versa. Without specified ``zorder``, cities (Points) gets plotted below world (Polygons), following the default order based on geometry types. .. ipython:: python ax = cities.plot(color='k') @savefig zorder_default.png world.plot(ax=ax); We can set the ``zorder`` for cities higher than for world to move it of top. .. ipython:: python ax = cities.plot(color='k', zorder=2) @savefig zorder_set.png world.plot(ax=ax, zorder=1); Pandas Plots ----------------- Plotting methods also allow for different plot styles from pandas along with the default ``geo`` plot. These methods can be accessed using the ``kind`` keyword argument in :meth:`~GeoDataFrame.plot`, and include: * ``geo`` for mapping * ``line`` for line plots * ``bar`` or ``barh`` for bar plots * ``hist`` for histogram * ``box`` for boxplot * ``kde`` or ``density`` for density plots * ``area`` for area plots * ``scatter`` for scatter plots * ``hexbin`` for hexagonal bin plots * ``pie`` for pie plots .. ipython:: python gdf = world.head(10) @savefig pandas_line_plot.png gdf.plot(kind='scatter', x="pop_est", y="gdp_md_est") You can also create these other plots using the ``GeoDataFrame.plot.`` accessor methods instead of providing the ``kind`` keyword argument. .. ipython:: python @savefig pandas_bar_plot.png gdf.plot.bar() For more information check out the `pandas documentation `_. Other Resources ----------------- Links to jupyter Notebooks for different mapping tasks: `Making Heat Maps `_ .. ipython:: python :suppress: matplotlib.rcParams['figure.figsize'] = orig .. ipython:: python :suppress: import matplotlib.pyplot as plt plt.close('all') geopandas-0.10.2/doc/source/docs/user_guide/mergingdata.rst000066400000000000000000000135731413247402600237130ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Merging Data ========================================= There are two ways to combine datasets in *geopandas* -- attribute joins and spatial joins. In an attribute join, a :class:`GeoSeries` or :class:`GeoDataFrame` is combined with a regular :class:`pandas.Series` or :class:`pandas.DataFrame` based on a common variable. This is analogous to normal merging or joining in *pandas*. In a Spatial Join, observations from two :class:`GeoSeries` or :class:`GeoDataFrame` are combined based on their spatial relationship to one another. In the following examples, we use these datasets: .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities')) # For attribute join country_shapes = world[['geometry', 'iso_a3']] country_names = world[['name', 'iso_a3']] # For spatial join countries = world[['geometry', 'name']] countries = countries.rename(columns={'name':'country'}) Appending --------- Appending :class:`GeoDataFrame` and :class:`GeoSeries` uses pandas :meth:`~pandas.DataFrame.append` methods. Keep in mind, that appended geometry columns needs to have the same CRS. .. ipython:: python # Appending GeoSeries joined = world.geometry.append(cities.geometry) # Appending GeoDataFrames europe = world[world.continent == 'Europe'] asia = world[world.continent == 'Asia'] eurasia = europe.append(asia) Attribute Joins ---------------- Attribute joins are accomplished using the :meth:`~pandas.DataFrame.merge` method. In general, it is recommended to use the ``merge()`` method called from the spatial dataset. With that said, the stand-alone :func:`pandas.merge` function will work if the :class:`GeoDataFrame` is in the ``left`` argument; if a :class:`~pandas.DataFrame` is in the ``left`` argument and a :class:`GeoDataFrame` is in the ``right`` position, the result will no longer be a :class:`GeoDataFrame`. For example, consider the following merge that adds full names to a :class:`GeoDataFrame` that initially has only ISO codes for each country by merging it with a :class:`~pandas.DataFrame`. .. ipython:: python # `country_shapes` is GeoDataFrame with country shapes and iso codes country_shapes.head() # `country_names` is DataFrame with country names and iso codes country_names.head() # Merge with `merge` method on shared variable (iso codes): country_shapes = country_shapes.merge(country_names, on='iso_a3') country_shapes.head() Spatial Joins ---------------- In a Spatial Join, two geometry objects are merged based on their spatial relationship to one another. .. ipython:: python # One GeoDataFrame of countries, one of Cities. # Want to merge so we can get each city's country. countries.head() cities.head() # Execute spatial join cities_with_country = cities.sjoin(countries, how="inner", predicate='intersects') cities_with_country.head() GeoPandas provides two spatial-join functions: - :meth:`GeoDataFrame.sjoin`: joins based on binary predicates (intersects, contains, etc.) - :meth:`GeoDataFrame.sjoin_nearest`: joins based on proximity, with the ability to set a maximum search radius. .. note:: For historical reasons, both methods are also available as top-level functions :func:`sjoin` and :func:`sjoin_nearest`. It is recommended to use methods as the functions may be deprecated in the future. Binary Predicate Joins ~~~~~~~~~~~~~~~~~~~~~~ Binary predicate joins are available via :meth:`GeoDataFrame.sjoin`. :meth:`GeoDataFrame.sjoin` has two core arguments: ``how`` and ``predicate``. **predicate** The ``predicate`` argument specifies how ``geopandas`` decides whether or not to join the attributes of one object to another, based on their geometric relationship. The values for ``predicate`` correspond to the names of geometric binary predicates and depend on the spatial index implementation. The default spatial index in ``geopandas`` currently supports the following values for ``predicate`` which are defined in the `Shapely documentation `__: * `intersects` * `contains` * `within` * `touches` * `crosses` * `overlaps` **how** The `how` argument specifies the type of join that will occur and which geometry is retained in the resultant :class:`GeoDataFrame`. It accepts the following options: * ``left``: use the index from the first (or `left_df`) :class:`GeoDataFrame` that you provide to :meth:`GeoDataFrame.sjoin`; retain only the `left_df` geometry column * ``right``: use index from second (or `right_df`); retain only the `right_df` geometry column * ``inner``: use intersection of index values from both :class:`GeoDataFrame`; retain only the `left_df` geometry column Note more complicated spatial relationships can be studied by combining geometric operations with spatial join. To find all polygons within a given distance of a point, for example, one can first use the :meth:`~geopandas.GeoSeries.buffer` method to expand each point into a circle of appropriate radius, then intersect those buffered circles with the polygons in question. Nearest Joins ~~~~~~~~~~~~~ Proximity-based joins can be done via :meth:`GeoDataFrame.sjoin_nearest`. :meth:`GeoDataFrame.sjoin_nearest` shares the ``how`` argument with :meth:`GeoDataFrame.sjoin`, and includes two additional arguments: ``max_distance`` and ``distance_col``. **max_distance** The ``max_distance`` argument specifies a maximum search radius for matching geometries. This can have a considerable performance impact in some cases. If you can, it is highly recommended that you use this parameter. **distance_col** If set, the resultant GeoDataFrame will include a column with this name containing the computed distances between an input geometry and the nearest geometry. geopandas-0.10.2/doc/source/docs/user_guide/missing_empty.rst000066400000000000000000000125141413247402600243120ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas .. _missing-empty: Missing and empty geometries ============================ GeoPandas supports, just like in pandas, the concept of missing values (NA or null values). But for geometry values, we have an additional concept of empty geometries: - **Empty geometries** are actual geometry objects but that have no coordinates (and thus also no area, for example). They can for example originate from taking the intersection of two polygons that have no overlap. The scalar object (when accessing a single element of a GeoSeries) is still a Shapely geometry object. - **Missing geometries** are unknown values in a GeoSeries. They will typically be propagated in operations (for example in calculations of the area or of the intersection), or ignored in reductions such as :attr:`~GeoSeries.unary_union`. The scalar object (when accessing a single element of a GeoSeries) is the Python ``None`` object. .. warning:: Starting from GeoPandas v0.6.0, those two concepts are more consistently separated. See :ref:`below ` for more details on what changed compared to earlier versions. Consider the following example GeoSeries with one polygon, one missing value and one empty polygon: .. ipython:: python from shapely.geometry import Polygon s = geopandas.GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])]) s In spatial operations, missing geometries will typically propagate (be missing in the result as well), while empty geometries are treated as a geometry and the result will depend on the operation: .. ipython:: python s.area s.union(Polygon([(0, 0), (0, 1), (1, 1), (1, 0)])) s.intersection(Polygon([(0, 0), (0, 1), (1, 1), (1, 0)])) The :meth:`GeoSeries.isna` method will only check for missing values and not for empty geometries: .. ipython:: python :okwarning: s.isna() On the other hand, if you want to know which values are empty geometries, you can use the :attr:`GeoSeries.is_empty` attribute: .. ipython:: python s.is_empty To get only the actual geometry objects that are neither missing nor empty, you can use a combination of both: .. ipython:: python :okwarning: s.is_empty | s.isna() s[~(s.is_empty | s.isna())] .. _missing-empty.changes-0.6.0: Changes since GeoPandas v0.6.0 ------------------------------ In GeoPandas v0.6.0, the missing data handling was refactored and made more consistent across the library. Historically, missing ("NA") values in a GeoSeries could be represented by empty geometric objects, in addition to standard representations such as ``None`` and ``np.nan``. At least, this was the case in :meth:`GeoSeries.isna` or when a GeoSeries got aligned in geospatial operations. But, other methods like :meth:`~GeoSeries.dropna` and :meth:`~GeoSeries.fillna` did not follow this approach and did not consider empty geometries as missing. In GeoPandas v0.6.0, the most important change is :meth:`GeoSeries.isna` no longer treating empty as missing: * Using the small example from above, the old behaviour treated both the empty as missing geometry as "missing": .. code-block:: python >>> s 0 POLYGON ((0 0, 1 1, 0 1, 0 0)) 1 None 2 GEOMETRYCOLLECTION EMPTY dtype: object >>> s.isna() 0 False 1 True 2 True dtype: bool * Starting from GeoPandas v0.6.0, it will now only see actual missing values as missing: .. ipython:: python :okwarning: s.isna() For now, when ``isna()`` is called on a GeoSeries with empty geometries, a warning is raised to alert the user of the changed behaviour with an indication how to solve this. Additionally, the behaviour of :meth:`GeoSeries.align` changed to use missing values instead of empty geometries to fill non-matching indexes. Consider the following small toy example: .. ipython:: python from shapely.geometry import Point s1 = geopandas.GeoSeries([Point(0, 0), Point(1, 1)], index=[0, 1]) s2 = geopandas.GeoSeries([Point(1, 1), Point(2, 2)], index=[1, 2]) s1 s2 * Previously, the ``align`` method would use empty geometries to fill values: .. code-block:: python >>> s1_aligned, s2_aligned = s1.align(s2) >>> s1_aligned 0 POINT (0 0) 1 POINT (1 1) 2 GEOMETRYCOLLECTION EMPTY dtype: object >>> s2_aligned 0 GEOMETRYCOLLECTION EMPTY 1 POINT (1 1) 2 POINT (2 2) dtype: object This method is used under the hood when performing spatial operations on mis-aligned GeoSeries objects: .. code-block:: python >>> s1.intersection(s2) 0 GEOMETRYCOLLECTION EMPTY 1 POINT (1 1) 2 GEOMETRYCOLLECTION EMPTY dtype: object * Starting from GeoPandas v0.6.0, :meth:`GeoSeries.align` will use missing values to fill in the non-aligned indices, to be consistent with the behaviour in pandas: .. ipython:: python s1_aligned, s2_aligned = s1.align(s2) s1_aligned s2_aligned This has the consequence that spatial operations will also use missing values instead of empty geometries, which can have a different behaviour depending on the spatial operation: .. ipython:: python :okwarning: s1.intersection(s2) geopandas-0.10.2/doc/source/docs/user_guide/projections.rst000066400000000000000000000417461413247402600237730ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas Managing Projections ========================================= Coordinate Reference Systems ----------------------------- The Coordinate Reference System (CRS) is important because the geometric shapes in a GeoSeries or GeoDataFrame object are simply a collection of coordinates in an arbitrary space. A CRS tells Python how those coordinates relate to places on the Earth. You can find the codes for most commonly used projections from `www.spatialreference.org `_. The same CRS can often be referred to in many ways. For example, one of the most commonly used CRS is the WGS84 latitude-longitude projection. This can be referred to using the authority code ``"EPSG:4326"``. *geopandas* can accept anything accepted by :meth:`pyproj.CRS.from_user_input() `: - CRS WKT string - An authority string (i.e. "epsg:4326") - An EPSG integer code (i.e. 4326) - A :class:`pyproj.CRS ` - An object with a to_wkt method. - PROJ string - Dictionary of PROJ parameters - PROJ keyword arguments for parameters - JSON string with PROJ parameters For reference, a few very common projections and their EPSG codes: * WGS84 Latitude/Longitude: ``"EPSG:4326"`` * UTM Zones (North): ``"EPSG:32633"`` * UTM Zones (South): ``"EPSG:32733"`` What is the best format to store the CRS information? ----------------------------------------------------- Generally, WKT or SRID's are preferred over PROJ strings as they can contain more information about a given CRS. Conversions between WKT and PROJ strings will in most cases cause a loss of information, potentially leading to erroneous transformations. If possible WKT2 should be used. For more details, see https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems Setting a Projection ---------------------- There are two relevant operations for projections: setting a projection and re-projecting. Setting a projection may be necessary when for some reason *geopandas* has coordinate data (x-y values), but no information about how those coordinates refer to locations in the real world. Setting a projection is how one tells *geopandas* how to interpret coordinates. If no CRS is set, *geopandas* geometry operations will still work, but coordinate transformations will not be possible and exported files may not be interpreted correctly by other software. Be aware that **most of the time** you don't have to set a projection. Data loaded from a reputable source (using the :func:`geopandas.read_file()` command) *should* always include projection information. You can see an objects current CRS through the :attr:`GeoSeries.crs` attribute. From time to time, however, you may get data that does not include a projection. In this situation, you have to set the CRS so *geopandas* knows how to interpret the coordinates. For example, if you convert a spreadsheet of latitudes and longitudes into a GeoSeries by hand, you would set the projection by passing the WGS84 latitude-longitude CRS to the :meth:`GeoSeries.set_crs` method (or by setting the :attr:`GeoSeries.crs` attribute): .. sourcecode:: python my_geoseries = my_geoseries.set_crs("EPSG:4326") my_geoseries = my_geoseries.set_crs(epsg=4326) Re-Projecting ---------------- Re-projecting is the process of changing the representation of locations from one coordinate system to another. All projections of locations on the Earth into a two-dimensional plane `are distortions `_, the projection that is best for your application may be different from the projection associated with the data you import. In these cases, data can be re-projected using the :meth:`GeoDataFrame.to_crs` command: .. ipython:: python # load example data world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) # Check original projection # (it's Plate Carrée! x-y are long and lat) world.crs # Visualize ax = world.plot() @savefig world_starting.png ax.set_title("WGS84 (lat/lon)"); # Reproject to Mercator (after dropping Antartica) world = world[(world.name != "Antarctica") & (world.name != "Fr. S. Antarctic Lands")] world = world.to_crs("EPSG:3395") # world.to_crs(epsg=3395) would also work ax = world.plot() @savefig world_reproj.png ax.set_title("Mercator"); Projection for multiple geometry columns ---------------------------------------- GeoPandas 0.8 implements support for different projections assigned to different geometry columns of the same GeoDataFrame. The projection is now stored together with geometries per column (directly on the GeometryArray level). Note that if GeometryArray has assigned projection, it is preferred over the projection passed to GeoSeries or GeoDataFrame during the creation: .. code-block:: python >>> array.crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... >>> GeoSeries(array, crs=3395).crs # crs=3395 is ignored as array already has CRS FutureWarning: CRS mismatch between CRS of the passed geometries and 'crs'. Use 'GeoDataFrame.set_crs(crs, allow_override=True)' to overwrite CRS or 'GeoDataFrame.to_crs(crs)' to reproject geometries. CRS mismatch will raise an error in the future versions of GeoPandas. GeoSeries(array, crs=3395).crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... If you want to overwrite projection, you can then assign it to the GeoSeries manually or re-project geometries to the target projection using either ``GeoSeries.set_crs(epsg=3395, allow_override=True)`` or ``GeoSeries.to_crs(epsg=3395)``. All GeometryArray-based operations preserve projection; however, if you loop over a column containing geometry, this information might be lost. Upgrading to GeoPandas 0.7 with pyproj > 2.2 and PROJ > 6 --------------------------------------------------------- Starting with GeoPandas 0.7, the `.crs` attribute of a GeoSeries or GeoDataFrame stores the CRS information as a :class:`pyproj.CRS `, and no longer as a proj4 string or dict. Before, you might have seen this: .. code-block:: python >>> gdf.crs {'init': 'epsg:4326'} while now you will see something like this: .. code-block:: python >>> gdf.crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... >>> type(gdf.crs) pyproj.crs.CRS This gives a better user interface and integrates improvements from pyproj and PROJ 6, but might also require some changes in your code. See `this blogpost `__ for some more background, and the subsections below cover different possible migration issues. See the `pyproj docs `__ for more on the :class:`pyproj.CRS ` object. Importing data from files ^^^^^^^^^^^^^^^^^^^^^^^^^ When reading geospatial files with :func:`geopandas.read_file`, things should mostly work out of the box. For example, reading the example countries dataset yields a proper CRS: .. ipython:: python df = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) df.crs However, in certain cases (with older CRS formats), the resulting CRS object might not be fully as expected. See the :ref:`section below ` for possible reasons and how to solve it. Manually specifying the CRS ^^^^^^^^^^^^^^^^^^^^^^^^^^^ When specifying the CRS manually in your code (e.g., because your data has not yet a CRS, or when converting to another CRS), this might require a change in your code. **"init" proj4 strings/dicts** Currently, a lot of people (and also the GeoPandas docs showed that before) specify the EPSG code using the "init" proj4 string: .. code-block:: python ## OLD GeoDataFrame(..., crs={'init': 'epsg:4326'}) # or gdf.crs = {'init': 'epsg:4326'} # or gdf.to_crs({'init': 'epsg:4326'}) The above will now raise a deprecation warning from pyproj, and instead of the "init" proj4 string, you should use only the EPSG code itself as follows: .. code-block:: python ## NEW GeoDataFrame(..., crs="EPSG:4326") # or gdf.crs = "EPSG:4326" # or gdf.to_crs("EPSG:4326") **proj4 strings/dicts** Although a full proj4 string is not deprecated (as opposed to the "init" string above), it is still recommended to change it with an EPSG code if possible. For example, instead of: .. code-block:: python gdf.crs = "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs" we recommend to do: .. code-block:: python gdf.crs = "EPSG:2163" *if* you know the EPSG code for the projection you are using. One possible way to find out the EPSG code is using pyproj for this: .. code-block:: python >>> import pyproj >>> crs = pyproj.CRS("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs") >>> crs.to_epsg() 2163 (you might need to set the ``min_confidence`` keyword of ``to_epsg`` to a lower value if the match is not perfect) Further, on websites such as `spatialreference.org `__ and `epsg.io `__ the descriptions of many CRS can be found including their EPSG codes and proj4 string definitions. **Other formats** Next to the EPSG code mentioned above, there are also other ways to specify the CRS: an actual :class:`pyproj.CRS ` object, a WKT string, a PROJ JSON string, etc. Anything that is accepted by :meth:`pyproj.CRS.from_user_input() ` can by specified to the ``crs`` keyword/attribute in GeoPandas. Also compatible CRS objects, such as from the :mod:`rasterio` package, can be passed directly to GeoPandas. The axis order of a CRS ^^^^^^^^^^^^^^^^^^^^^^^ Starting with PROJ 6 / pyproj 2, the axis order of the official EPSG definition is honoured. For example, when using geographic coordinates (degrees of longitude and latitude) in the standard EPSG:4326, the CRS will look like: .. code-block:: python >>> pyproj.CRS(3EPSG:4326") ... Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) ... This mentions the order as (lat, lon), as that is the official order of coordinates in EPSG:4326. In GeoPandas, however, the coordinates are always stored as (x, y), and thus as (lon, lat) order, regardless of the CRS (i.e. the "traditional" order used in GIS). When reprojecting, GeoPandas and pyproj will under the hood take care of this difference in axis order, so the user doesn't need to care about this. .. _unrecognized-crs-reasons: Why is it not properly recognizing my CRS? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ There are many file sources and CRS definitions out there "in the wild" that might have a CRS description that does not fully conform to the new standards of PROJ > 6 (proj4 strings, older WKT formats, ...). In such cases, you will get a :class:`pyproj.CRS ` object that might not be fully what you expected (e.g. not equal to the expected EPSG code). Below we list a few possible cases. I get a "Bound CRS"? ~~~~~~~~~~~~~~~~~~~~ Some CRS definitions include a *"towgs84" clause*, which can give problems in recognizing the actual CRS. For example, both the proj4 and WKT representation for EPSG:31370 (the local projection used in Belgium) as can be found at `https://spatialreference.org/ref/epsg/31370/ `__ include this. When taking one of those definitions from that site, and creating a CRS object: .. code-block:: python >>> import pyproj >>> crs = pyproj.CRS("+proj=lcc +lat_1=51.16666723333333 +lat_2=49.8333339 +lat_0=90 +lon_0=4.367486666666666 +x_0=150000.013 +y_0=5400088.438 +ellps=intl +towgs84=106.869,-52.2978,103.724,-0.33657,0.456955,-1.84218,1 +units=m +no_defs") >>> crs Name: unknown Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - undefined Coordinate Operation: - name: Transformation from unknown to WGS84 - method: Position Vector transformation (geog2D domain) Datum: Unknown based on International 1909 (Hayford) ellipsoid - Ellipsoid: International 1909 (Hayford) - Prime Meridian: Greenwich Source CRS: unknown You notice that the above is a not a "Projected CRS" as expected, but a "Bound CRS". This is because it is "bound" to a conversion to WGS84, and will always use this when reprojecting instead of letting PROJ determine the best conversion. To get the actual underlying projected CRS, you can use the ``.source_crs`` attribute: .. code-block:: python >>> crs.source_crs Name: unknown ... Now we have a "Projected CRS", and now it will also recognize the correct EPSG number: .. code-block:: python >>> crs.to_epsg() >>> crs.source_crs.to_epsg() 31370 I have a different axis order? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ As mentioned above, pyproj now honours the axis order of the EPSG definition. However, proj4 strings or older WKT versions don't specify this correctly, which can be a reason that the CRS object is not equal to the expected EPSG code. Consider the following example of a Canadian projected CRS "EPSG:2953". When constructing the CRS object from the WKT string as provided on `https://epsg.io/2953 `__: .. code-block:: python >>> crs = pyproj.CRS("""PROJCS["NAD83(CSRS) / New Brunswick Stereographic", ... GEOGCS["NAD83(CSRS)", ... DATUM["NAD83_Canadian_Spatial_Reference_System", ... SPHEROID["GRS 1980",6378137,298.257222101, ... AUTHORITY["EPSG","7019"]], ... AUTHORITY["EPSG","6140"]], ... PRIMEM["Greenwich",0, ... AUTHORITY["EPSG","8901"]], ... UNIT["degree",0.0174532925199433, ... AUTHORITY["EPSG","9122"]], ... AUTHORITY["EPSG","4617"]], ... PROJECTION["Oblique_Stereographic"], ... PARAMETER["latitude_of_origin",46.5], ... PARAMETER["central_meridian",-66.5], ... PARAMETER["scale_factor",0.999912], ... PARAMETER["false_easting",2500000], ... PARAMETER["false_northing",7500000], ... UNIT["metre",1, ... AUTHORITY["EPSG","9001"]], ... AUTHORITY["EPSG","2953"]]""") >>> crs Name: NAD83(CSRS) / New Brunswick Stereographic Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) ... Although this is the WKT string as found online for "EPSG:2953", this CRS object does not evaluate equal to this EPSG code: .. code-block:: python >>> crs == "EPSG:2953" False If we construct the CRS object from the EPSG code (truncated output): .. code-block:: python >>> pyproj.CRS("EPSG:2953") Name: NAD83(CSRS) / New Brunswick Stereographic Axis Info [cartesian]: - N[north]: Northing (metre) - E[east]: Easting (metre) ... You can see that the CRS object constructed from the WKT string has a "Easting, Northing" (i.e. x, y) axis order, while the CRS object constructed from the EPSG code has a (Northing, Easting) axis order. Only having this difference in axis order is no problem when using the CRS in GeoPandas, since GeoPandas always uses a (x, y) order to store the data regardless of the CRS definition. But, you might still want to verify it is equivalent to the expected EPSG code. By lowering the `min_confidence`, the axis order will be ignored: .. code-block:: python >>> crs.to_epsg() >>> crs.to_epsg(min_confidence=20) 2953 The ``.crs`` attribute is no longer a dict or string ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you relied on the ``.crs`` object being a dict or a string, such code can be broken given it is now a :class:`pyproj.CRS ` object. But this object actually provides a more robust interface to get information about the CRS. For example, if you used the following code to get the EPSG code: .. code-block:: python gdf.crs['init'] This will no longer work. To get the EPSG code from a ``crs`` object, you can use the :meth:`~pyproj.crs.CRS.to_epsg` method. Or to check if a CRS was a certain UTM zone: .. code-block:: python '+proj=utm ' in gdf.crs could be replaced with the more robust check (requires pyproj 2.6+): .. code-block:: python gdf.crs.utm_zone is not None And there are many other methods available on the :class:`pyproj.CRS ` class to get information about the CRS. geopandas-0.10.2/doc/source/docs/user_guide/reproject_fiona.rst000066400000000000000000000055171413247402600246010ustar00rootroot00000000000000Re-projecting using GDAL with Rasterio and Fiona ================================================ The simplest method of re-projecting is :meth:`GeoDataFrame.to_crs`. It uses ``pyproj`` as the engine and transforms the points within the geometries. These examples demonstrate how to use ``Fiona`` or ``rasterio`` as the engine to re-project your data. Fiona and rasterio are powered by GDAL and with algorithms that consider the geometry instead of just the points the geometry contains. This is particularly useful for antimeridian cutting. However, this also means the transformation is not as fast. Fiona Example ------------- .. code-block:: python from functools import partial import fiona import geopandas from fiona.transform import transform_geom from packaging import version from pyproj import CRS from pyproj.enums import WktVersion from shapely.geometry import mapping, shape # set up Fiona transformer def crs_to_fiona(proj_crs): proj_crs = CRS.from_user_input(proj_crs) if version.parse(fiona.__gdal_version__) < version.parse("3.0.0"): fio_crs = proj_crs.to_wkt(WktVersion.WKT1_GDAL) else: # GDAL 3+ can use WKT2 fio_crs = proj_crs.to_wkt() return fio_crs def base_transformer(geom, src_crs, dst_crs): return shape( transform_geom( src_crs=crs_to_fiona(src_crs), dst_crs=crs_to_fiona(dst_crs), geom=mapping(geom), antimeridian_cutting=True, ) ) # load example data world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) destination_crs = "EPSG:3395" forward_transformer = partial(base_transformer, src_crs=world.crs, dst_crs=destination_crs) # Reproject to Mercator (after dropping Antartica) world = world[(world.name != "Antarctica") & (world.name != "Fr. S. Antarctic Lands")] with fiona.Env(OGR_ENABLE_PARTIAL_REPROJECTION="YES"): mercator_world = world.set_geometry(world.geometry.apply(forward_transformer), crs=destination_crs) Rasterio Example ---------------- This example requires rasterio 1.2+ and GDAL 3+. .. code-block:: python import geopandas import rasterio.warp from shapely.geometry import shape # load example data world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) # Reproject to Mercator (after dropping Antartica) world = world[(world.name != "Antarctica") & (world.name != "Fr. S. Antarctic Lands")] destination_crs = "EPSG:3395" geometry = rasterio.warp.transform_geom( src_crs=world.crs, dst_crs=destination_crs, geom=world.geometry.values, ) mercator_world = world.set_geometry( [shape(geom) for geom in geometry], crs=destination_crs, ) geopandas-0.10.2/doc/source/docs/user_guide/set_operations.rst000066400000000000000000000176231413247402600244670ustar00rootroot00000000000000.. currentmodule:: geopandas .. ipython:: python :suppress: import geopandas import matplotlib.pyplot as plt plt.close('all') Set-Operations with Overlay ============================ When working with multiple spatial datasets -- especially multiple *polygon* or *line* datasets -- users often wish to create new shapes based on places where those datasets overlap (or don't overlap). These manipulations are often referred using the language of sets -- intersections, unions, and differences. These types of operations are made available in the *geopandas* library through the :meth:`~geopandas.GeoDataFrame.overlay` method. The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the DataFrame level, not on individual geometries, and the properties from both are retained. In effect, for every shape in the left :class:`~geopandas.GeoDataFrame`, this operation is executed against every other shape in the right :class:`~geopandas.GeoDataFrame`: .. image:: ../../_static/overlay_operations.png **Source: QGIS Documentation** .. note:: Note to users familiar with the *shapely* library: :meth:`~geopandas.GeoDataFrame.overlay` can be thought of as offering versions of the standard *shapely* set-operations that deal with the complexities of applying set operations to two *GeoSeries*. The standard *shapely* set-operations are also available as :class:`~geopandas.GeoSeries` methods. The different Overlay operations -------------------------------- First, we create some example data: .. ipython:: python from shapely.geometry import Polygon polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), Polygon([(2,2), (4,2), (4,4), (2,4)])]) polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), Polygon([(3,3), (5,3), (5,5), (3,5)])]) df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1':[1,2]}) df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2':[1,2]}) These two GeoDataFrames have some overlapping areas: .. ipython:: python ax = df1.plot(color='red'); @savefig overlay_example.png width=5in df2.plot(ax=ax, color='green', alpha=0.5); We illustrate the different overlay modes with the above example. The :meth:`~geopandas.GeoDataFrame.overlay` method will determine the set of all individual geometries from overlaying the two input GeoDataFrames. This result covers the area covered by the two input GeoDataFrames, and also preserves all unique regions defined by the combined boundaries of the two GeoDataFrames. .. note:: For historical reasons, the overlay method is also available as a top-level function :func:`overlay`. It is recommended to use the method as the function may be deprecated in the future. When using ``how='union'``, all those possible geometries are returned: .. ipython:: python res_union = df1.overlay(df2, how='union') res_union ax = res_union.plot(alpha=0.5, cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_union.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); The other ``how`` operations will return different subsets of those geometries. With ``how='intersection'``, it returns only those geometries that are contained by both GeoDataFrames: .. ipython:: python res_intersection = df1.overlay(df2, how='intersection') res_intersection ax = res_intersection.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_intersection.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); ``how='symmetric_difference'`` is the opposite of ``'intersection'`` and returns the geometries that are only part of one of the GeoDataFrames but not of both: .. ipython:: python res_symdiff = df1.overlay(df2, how='symmetric_difference') res_symdiff ax = res_symdiff.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_symdiff.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); To obtain the geometries that are part of ``df1`` but are not contained in ``df2``, you can use ``how='difference'``: .. ipython:: python res_difference = df1.overlay(df2, how='difference') res_difference ax = res_difference.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_difference.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Finally, with ``how='identity'``, the result consists of the surface of ``df1``, but with the geometries obtained from overlaying ``df1`` with ``df2``: .. ipython:: python res_identity = df1.overlay(df2, how='identity') res_identity ax = res_identity.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_identity.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Overlay Countries Example ------------------------- First, we load the countries and cities example datasets and select : .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) capitals = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities')) # Select South America and some columns countries = world[world['continent'] == "South America"] countries = countries[['geometry', 'name']] # Project to crs that uses meters as distance measure countries = countries.to_crs('epsg:3395') capitals = capitals.to_crs('epsg:3395') To illustrate the :meth:`~geopandas.GeoDataFrame.overlay` method, consider the following case in which one wishes to identify the "core" portion of each country -- defined as areas within 500km of a capital -- using a ``GeoDataFrame`` of countries and a ``GeoDataFrame`` of capitals. .. ipython:: python # Look at countries: @savefig world_basic.png width=5in countries.plot(); # Now buffer cities to find area within 500km. # Check CRS -- World Mercator, units of meters. capitals.crs # make 500km buffer capitals['geometry']= capitals.buffer(500000) @savefig capital_buffers.png width=5in capitals.plot(); To select only the portion of countries within 500km of a capital, we specify the ``how`` option to be "intersect", which creates a new set of polygons where these two layers overlap: .. ipython:: python country_cores = countries.overlay(capitals, how='intersection') @savefig country_cores.png width=5in country_cores.plot(alpha=0.5, edgecolor='k', cmap='tab10'); Changing the "how" option allows for different types of overlay operations. For example, if we were interested in the portions of countries *far* from capitals (the peripheries), we would compute the difference of the two. .. ipython:: python country_peripheries = countries.overlay(capitals, how='difference') @savefig country_peripheries.png width=5in country_peripheries.plot(alpha=0.5, edgecolor='k', cmap='tab10'); .. ipython:: python :suppress: import matplotlib.pyplot as plt plt.close('all') keep_geom_type keyword ---------------------- In default settings, :meth:`~geopandas.GeoDataFrame.overlay` returns only geometries of the same geometry type as GeoDataFrame (left one) has, where Polygon and MultiPolygon is considered as a same type (other types likewise). You can control this behavior using ``keep_geom_type`` option, which is set to True by default. Once set to False, ``overlay`` will return all geometry types resulting from selected set-operation. Different types can result for example from intersection of touching geometries, where two polygons intersects in a line or a point. More Examples ------------- A larger set of examples of the use of :meth:`~geopandas.GeoDataFrame.overlay` can be found `here `_ .. toctree:: :maxdepth: 2 geopandas-0.10.2/doc/source/gallery/000077500000000000000000000000001413247402600172425ustar00rootroot00000000000000geopandas-0.10.2/doc/source/gallery/cartopy_convert.ipynb000066400000000000000000000137531413247402600235370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Plotting with CartoPy and GeoPandas\n", "\n", "Converting between GeoPandas and CartoPy for visualizing data.\n", "\n", "[CartoPy](http://scitools.org.uk/cartopy/) is a Python library\n", "that specializes in creating geospatial\n", "visualizations. It has a slightly different way of representing\n", "Coordinate Reference Systems (CRS) as well as constructing plots.\n", "This example steps through a round-trip transfer of data\n", "between GeoPandas and CartoPy.\n", "\n", "First we'll load in the data using GeoPandas.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import geopandas\n", "from cartopy import crs as ccrs\n", "\n", "path = geopandas.datasets.get_path('naturalearth_lowres')\n", "df = geopandas.read_file(path)\n", "# Add a column we'll use later\n", "df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll visualize the map using GeoPandas\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting with CartoPy\n", "=====================\n", "\n", "Cartopy also handles Shapely objects well, but it uses a different system for\n", "CRS. To plot this data with CartoPy, we'll first need to project it into a\n", "new CRS. We'll use a CRS defined within CartoPy and use the GeoPandas\n", "``to_crs`` method to make the transformation.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define the CartoPy CRS object.\n", "crs = ccrs.AzimuthalEquidistant()\n", "\n", "# This can be converted into a `proj4` string/dict compatible with GeoPandas\n", "crs_proj4 = crs.proj4_init\n", "df_ae = df.to_crs(crs_proj4)\n", "\n", "# Here's what the plot looks like in GeoPandas\n", "df_ae.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that our data is in a CRS based off of CartoPy, we can easily\n", "plot it.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(subplot_kw={'projection': crs})\n", "ax.add_geometries(df_ae['geometry'], crs=crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we could have easily done this with an EPSG code like so:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "crs_epsg = ccrs.epsg('3857')\n", "df_epsg = df.to_crs(epsg='3857')\n", "\n", "# Generate a figure with two axes, one for CartoPy, one for GeoPandas\n", "fig, axs = plt.subplots(1, 2, subplot_kw={'projection': crs_epsg},\n", " figsize=(10, 5))\n", "# Make the CartoPy plot\n", "axs[0].add_geometries(df_epsg['geometry'], crs=crs_epsg,\n", " facecolor='white', edgecolor='black')\n", "# Make the GeoPandas plot\n", "df_epsg.plot(ax=axs[1], color='white', edgecolor='black')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CartoPy to GeoPandas\n", "====================\n", "\n", "Next we'll perform a CRS projection in CartoPy, and then convert it\n", "back into a GeoPandas object.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "crs_new = ccrs.AlbersEqualArea()\n", "new_geometries = [crs_new.project_geometry(ii, src_crs=crs)\n", " for ii in df_ae['geometry'].values]\n", "\n", "fig, ax = plt.subplots(subplot_kw={'projection': crs_new})\n", "ax.add_geometries(new_geometries, crs=crs_new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've created new Shapely objects with the CartoPy CRS,\n", "we can use this to create a GeoDataFrame.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_aea = geopandas.GeoDataFrame(df['gdp_pp'], geometry=new_geometries,\n", " crs=crs_new.proj4_init)\n", "df_aea.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can even combine these into the same figure. Here we'll plot the\n", "shapes of the countries with CartoPy. We'll then calculate the centroid\n", "of each with GeoPandas and plot it on top.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "# Generate a CartoPy figure and add the countries to it\n", "fig, ax = plt.subplots(subplot_kw={'projection': crs_new})\n", "ax.add_geometries(new_geometries, crs=crs_new)\n", "\n", "# Calculate centroids and plot\n", "df_aea_centroids = df_aea.geometry.centroid\n", "# Need to provide \"zorder\" to ensure the points are plotted above the polygons\n", "df_aea_centroids.plot(ax=ax, markersize=5, color='r', zorder=10)\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/choro_legends.ipynb000066400000000000000000022343621413247402600231340ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Choro legends" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "from geopandas import read_file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.4.2'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify\n", "mapclassify.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'4.4.0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import libpysal\n", "libpysal.__version__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name Description Installed\n", "0 10740 Albuquerque, New Mexico, Census 2000 Tract Data. 10740 i... True\n", "1 AirBnB Airbnb rentals, socioeconomics, and crime in Chicago False\n", "2 Atlanta Atlanta, GA region homicide counts and rates False\n", "3 Baltimore Baltimore house sales prices and hedonics False\n", "4 Bostonhsg Boston housing and neighborhood data False\n", "5 Buenosaires Electoral Data for 1999 Argentinean Elections False\n", "6 Charleston1 2000 Census Tract Data for Charleston, SC MSA and counties False\n", "7 Charleston2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "8 Chicago Health Chicago Health + Socio-Economics False\n", "9 Chicago commpop Chicago Community Area Population Percent Change for 20... False\n", "10 Chicago parcels Tax parcel polygons of Cook county False\n", "11 Chile Labor Labor Markets in Chile (1982-2002) False\n", "12 Chile Migration Internal Migration in Chile (1977-2002) False\n", "13 Cincinnati 2008 Cincinnati Crime + Socio-Demographics False\n", "14 Cleveland 2015 sales prices of homes in Cleveland, OH. False\n", "15 Columbus Columbus neighborhood crime False\n", "16 Elections 2012 and 2016 Presidential Elections False\n", "17 Grid100 Grid with simulated variables False\n", "18 Groceries 2015 Chicago supermarkets False\n", "19 Guerry Moral statistics of France (Guerry, 1833) False\n", "20 Health Indicators Chicago Health Indicators (2005-11) False\n", "21 Health+ 2000 Health, Income + Diversity False\n", "22 Hickory1 2000 Census Tract Data for Hickory, NC MSA and counties False\n", "23 Hickory2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "24 Home Sales 2014-15 Home Sales in King County, WA False\n", "25 Houston Houston, TX region homicide counts and rates False\n", "26 Juvenile Cardiff juvenile delinquent residences False\n", "27 Lansing1 2000 Census Tract Data for Lansing, MI MSA and counties False\n", "28 Lansing2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "29 Laozone Ozone measures at monitoring stations in Los Angeles basin False\n", "30 LasRosas Corn yield, fertilizer and field data for precision agr... False\n", "31 Line Line Shapefile True\n", "32 Liquor Stores 2015 Chicago Liquor Stores False\n", "33 Malaria Malaria incidence and population (1973, 95, 93 censuses... False\n", "34 Milwaukee1 2000 Census Tract Data for Milwaukee, WI MSA False\n", "35 Milwaukee2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "36 NCOVR US county homicides 1960-1990 True\n", "37 NDVI Normalized Difference Vegetation Index grid False\n", "38 NYC Demographic and housing data for New York City subborou... False\n", "39 NYC Earnings Block-level Earnings in NYC (2002-14) False\n", "40 NYC Education NYC Education (2000) False\n", "41 NYC Neighborhoods Demographics for New York City neighborhoods False\n", "42 NYC Socio-Demographics NYC Education + Socio-Demographics False\n", "43 Natregimes NCOVR with regimes (book/PySAL) False\n", "44 Nepal Health, poverty and education indicators for Nepal dist... False\n", "45 Ohiolung Ohio lung cancer data, 1968, 1978, 1988 False\n", "46 Orlando1 2000 Census Tract Data for Orlando, FL MSA and counties False\n", "47 Orlando2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "48 Oz9799 Monthly ozone data, 1997-99 False\n", "49 Phoenix ACS Phoenix American Community Survey Data (2010, 5-year av... False\n", "50 Pittsburgh Pittsburgh homicide locations False\n", "51 Point Point Shapefile True\n", "52 Police Police expenditures Mississippi counties False\n", "53 Polygon Polygon Shapefile True\n", "54 Polygon_Holes Example to test treatment of holes True\n", "55 Rio Grande do Sul Cities of the Brazilian State of Rio Grande do Sul True\n", "56 SIDS North Carolina county SIDS death counts False\n", "57 SIDS2 North Carolina county SIDS death counts and rates False\n", "58 Sacramento1 2000 Census Tract Data for Sacramento MSA True\n", "59 Sacramento2 1998 and 2001 Zip Code Business Patterns (Census Bureau... True\n", "60 SanFran Crime July-Dec 2012 crime incidents in San Francisco (points ... False\n", "61 Savannah1 2000 Census Tract Data for Savannah, GA MSA and counties False\n", "62 Savannah2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "63 Scotlip Male lip cancer in Scotland, 1975-80 False\n", "64 Seattle1 2000 Census Tract Data for Seattle, WA MSA and counties False\n", "65 Seattle2 1998 and 2001 Zip Code Business Patterns (Census Bureau... False\n", "66 Snow John Snow & the 19th Century Cholera Epidemic False\n", "67 South US Southern county homicides 1960-1990 True\n", "68 Spirals Synthetic spiral points False\n", "69 StLouis St Louis region county homicide counts and rates False\n", "70 Tampa1 2000 Census Tract Data for Tampa, FL MSA and counties False\n", "71 US SDOH 2014 US Social Determinants of Health Data False\n", "72 arcgis arcgis testing files True\n", "73 baltim Baltimore house sales prices and hedonics, 1978. True\n", "74 berlin Prenzlauer Berg neighborhood AirBnB data from Berlin True\n", "75 book Synthetic data to illustrate spatial weights True\n", "76 burkitt Burkitt's lymphoma in the Western Nile district of Uganda True\n", "77 calemp Employment density for California counties True\n", "78 chicago Chicago neighborhoods True\n", "79 clearwater mgwr testing dataset False\n", "80 columbus Columbus neighborhood crime data 1980 True\n", "81 desmith Small dataset to illustrate Moran's I statistic True\n", "82 geodanet Datasets from GeoDaNet for network analysis True\n", "83 georgia Various socio-economic variables for counties within the... True\n", "84 juvenile Residences of juvenile offenders in Cardiff, UK True\n", "85 mexico Decennial per capita incomes of Mexican states 1940-2000 True\n", "86 networks Datasets used for network testing True\n", "87 newHaven Network testing dataset False\n", "88 nyc_bikes New York City Bike Trips False\n", "89 sids2 North Carolina county SIDS death counts and rates True\n", "90 snow_maps Public water pumps and Cholera deaths in London 1854 (Jo... True\n", "91 stl Homicides and selected socio-economic characteristics fo... True\n", "92 street_net_pts Street network points True\n", "93 taz Traffic Analysis Zones in So. California False\n", "94 tokyo Tokyo Mortality data True\n", "95 us_income Per-capita income for the lower 48 US states 1929-2009 True\n", "96 virginia Virginia counties shapefile True\n", "97 wmat Datasets used for spatial weights testing True\n" ] } ], "source": [ "libpysal.examples.available()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "_ = libpysal.examples.load_example('South')\n", "pth = libpysal.examples.get_path('south.shp')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "df = read_file(pth)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default legend formatting" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "ax = df.plot(column='HR60', scheme='QUANTILES', k=4, \\\n", " cmap='BuPu', legend=True,\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5)})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[' 0.00, 3.21', ' 3.21, 6.25', ' 6.25, 9.96', ' 9.96, 92.94']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = [t.get_text() for t in ax.get_legend().get_texts()]\n", "labels" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Quantiles \n", "\n", " Interval Count\n", "----------------------\n", "[ 0.00, 3.21] | 353\n", "( 3.21, 6.25] | 353\n", "( 6.25, 9.96] | 353\n", "( 9.96, 92.94] | 353" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q4 = mapclassify.Quantiles(df.HR60, k=4)\n", "q4" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels == q4.get_legend_classes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in this case, the first interval is closed on the minimum value in the dataset. The other intervals have an open lower bound. This can be now displayed in the legend using `legend_kwds={'interval': True}`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overriding numerical format" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='HR60', scheme='QUANTILES', k=4, \\\n", " cmap='BuPu', legend=True,\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5)},\n", " )" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='HR60', scheme='QUANTILES', k=4, \\\n", " cmap='BuPu', legend=True,\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5), 'fmt':\"{:.4f}\"})" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='HR60', scheme='QUANTILES', k=4, \\\n", " cmap='BuPu', legend=True,\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5), 'fmt':\"{:.0f}\"})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The new legends_kwds arg `fmt` takes a string to set the numerical formatting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## When first class lower bound < y.min()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='HR60', scheme='BoxPlot', \\\n", " cmap='BuPu', legend=True,\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5),\n", " 'fmt': \"{:.0f}\"})" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BoxPlot \n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -6.90] | 0\n", "(-6.90, 3.21] | 353\n", "( 3.21, 6.25] | 353\n", "( 6.25, 9.96] | 353\n", "( 9.96, 20.07] | 311\n", "(20.07, 92.94] | 42" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp = mapclassify.BoxPlot(df.HR60)\n", "bp\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['(-inf, -7]',\n", " '( -7, 3]',\n", " '( 3, 6]',\n", " '( 6, 10]',\n", " '( 10, 20]',\n", " '( 20, 93]']" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp.get_legend_classes(fmt=\"{:.0f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In some classifiers the user should be aware that the lower (upper) bound of the first (last) interval is not equal to the minimum (maximum) of the attribute values. This is useful to detect extreme values and highly skewed distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Show interval bracket" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='HR60', scheme='BoxPlot', \\\n", " cmap='BuPu', legend=True,\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5),\n", " 'interval': True})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical Data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df.plot(column='STATE_NAME', categorical=True, legend=True, \\\n", " legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5),\n", " 'fmt': \"{:.0f}\"}) # fmt is ignored for categorical data" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" }, "nbsphinx": { "execute": "never" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/choropleths.ipynb000066400000000000000000012031471413247402600226470ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Choropleth classification schemes from PySAL for use with GeoPandas\n", "\"PySAL\n", "PySAL is a [Spatial Analysis Library](), which packages fast spatial algorithms used in various fields. These include Exploratory spatial data analysis, spatial inequality analysis, spatial analysis on networks, spatial dynamics, and many more.\n", "\n", "It is used under the hood in geopandas when plotting measures with a set of colors. There are many ways to classify data into different bins, depending on a number of classification schemes.\n", "\n", "\n", "\n", "For example, if we have 20 countries whose average annual temperature varies between 5C and 25C, we can classify them in 4 bins by:\n", "* Quantiles\n", " - Separates the rows into equal parts, 5 countries per bin.\n", "* Equal Intervals\n", " - Separates the measure's interval into equal parts, 5C per bin.\n", "* Natural Breaks (Fischer Jenks)\n", " - This algorithm tries to split the rows into naturally occurring clusters. The numbers per bin will depend on how the observations are located on the interval." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:29:37.736444Z", "start_time": "2017-12-15T21:29:37.716444Z" } }, "outputs": [], "source": [ "import geopandas as gpd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:29:39.866422Z", "start_time": "2017-12-15T21:29:39.846422Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observations, Attributes: (49, 21)\n" ] }, { "data": { "text/html": [ "
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00.3094412.440629251580.46700319.53115.7259802.850747...5.0338.79999944.0700001.01.01.00.01000.01005.0POLYGON ((8.62413 14.23698, 8.55970 14.74245, ...
10.2593292.236939312144.56700121.23218.8017545.296720...4.2735.61999942.3800011.01.00.00.01000.01001.0POLYGON ((8.25279 14.23694, 8.28276 14.22994, ...
20.1924682.187547463626.35000015.95630.6267814.534649...3.8939.82000041.1800001.01.01.00.01000.01006.0POLYGON ((8.65331 14.00809, 8.81814 14.00205, ...
30.0838411.427635524233.2000014.47732.3877600.394427...3.7036.50000040.5200001.01.00.00.01000.01002.0POLYGON ((8.45950 13.82035, 8.47341 13.83227, ...
40.4888882.997133675723.22500011.25250.7315100.405664...2.8340.00999838.0000001.01.01.00.01000.01007.0POLYGON ((8.68527 13.63952, 8.67758 13.72221, ...
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " AREA PERIMETER COLUMBUS_ COLUMBUS_I POLYID NEIG HOVAL \\\n", "0 0.309441 2.440629 2 5 1 5 80.467003 \n", "1 0.259329 2.236939 3 1 2 1 44.567001 \n", "2 0.192468 2.187547 4 6 3 6 26.350000 \n", "3 0.083841 1.427635 5 2 4 2 33.200001 \n", "4 0.488888 2.997133 6 7 5 7 23.225000 \n", "\n", " INC CRIME OPEN ... DISCBD X Y NSA NSB \\\n", "0 19.531 15.725980 2.850747 ... 5.03 38.799999 44.070000 1.0 1.0 \n", "1 21.232 18.801754 5.296720 ... 4.27 35.619999 42.380001 1.0 1.0 \n", "2 15.956 30.626781 4.534649 ... 3.89 39.820000 41.180000 1.0 1.0 \n", "3 4.477 32.387760 0.394427 ... 3.70 36.500000 40.520000 1.0 1.0 \n", "4 11.252 50.731510 0.405664 ... 2.83 40.009998 38.000000 1.0 1.0 \n", "\n", " EW CP THOUS NEIGNO geometry \n", "0 1.0 0.0 1000.0 1005.0 POLYGON ((8.62413 14.23698, 8.55970 14.74245, ... \n", "1 0.0 0.0 1000.0 1001.0 POLYGON ((8.25279 14.23694, 8.28276 14.22994, ... \n", "2 1.0 0.0 1000.0 1006.0 POLYGON ((8.65331 14.00809, 8.81814 14.00205, ... \n", "3 0.0 0.0 1000.0 1002.0 POLYGON ((8.45950 13.82035, 8.47341 13.83227, ... \n", "4 1.0 0.0 1000.0 1007.0 POLYGON ((8.68527 13.63952, 8.67758 13.72221, ... \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We use a PySAL example shapefile\n", "import libpysal as ps\n", "\n", "pth = ps.examples.get_path(\"columbus.shp\")\n", "tracts = gpd.GeoDataFrame.from_file(pth)\n", "print('Observations, Attributes:',tracts.shape)\n", "tracts.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the CRIME variable\n", "In this example, we are taking a look at neighbourhood-level statistics for the city of Columbus, OH. We'd like to have an idea of how the crime rate variable is distributed around the city.\n", "\n", "From the [shapefile's metadata](https://github.com/pysal/pysal/blob/master/pysal/examples/columbus/columbus.html):\n", ">**CRIME**: residential burglaries and vehicle thefts per 1000 households" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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WhXinHH5eR8PPmLqIY+amHdN539y1GyENqSrM40443u4bbTGXjVfSsCaNbp4Ivwe4JyKuyt1nAVt3sT4zM+uyriWNiHgAuFtS7Z3AbyQdqjIzs1Gq21dPHQSclq+cugM4oMv1mZlZF3U1aUTEHGDYjrWZmVl3+Y5wMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrbYVOBpa0HDAhIp4oOfw84EngOWBRRPR0HKGZmVVG2z0NSb+QtLqkVYGbgdskfbaDOqZFxFZOGGZmo1+Zw1Ob5z2LdwLnAxsB+3YzKDMzq6YySWOcpHGkpPHbiHgWiJLlB3CBpNmSpg8wRjMzqwhFtF7/SzoYOAy4HtiVtKdxakTs2LZwab2IuE/SOsCFwEERcVndMNOB6QCTJ0/eZubMmR1PRH9/PxMmTOh4vJEyVPHOvffxIYimnMnj4cGF6fPU9ScOW70DVaU2UWY5FedvUVXndZXmb1mjLeay8U6bNm32cB7+b5s0Go4krRARizoc5yigPyK+02yYnp6emDVrVsfx9PX10dvb2/F4I2Wo4p1y+HmDD6akGVMXcczcdN3EvG/uOmz1DlSV2kSZ5VScv0VVnddVmr9ljbaYy8YraViTRtOrpyR9ps24x7b6Mp84Xy4insyf3wJ8ufMQzcysKlpdcrta/r8Z8Brgd7l7N+CyhmMsbTLwG0m1en4REX8YYJxmZlYBTZNGRBwNIOkCYOuIeDJ3HwWc2a7giLgD2HJowjQzsyooc/XURsAzhe5ngCldicbMzCqtzB3hpwBXS/pN7n4ncFLXIjIzs8pqmzQi4muSfg/sSLrv4oCIuK7rkZmZWeWUffbUc8DzpKTxfPfCMTOzKivz7KlDgNOAScA6wKmSDup2YGZmVj1l9jQOBF4bEQsAJH0LuAL4QTcDMzOz6ilz9ZRIh6dqnsv9zMxsjCmzp/Fz4Kp89ZSAPYCfdjUqMzOrpDJXTx0rqQ/YIffy1VNmZmNUJ1dPBb56ysxsTPPVU2ZmVpqvnjIzs9J89ZSZmZXW6dVTkJ495aunzMzGoLJXT10KbE/aw/DVU2ZmY1TZq6fmAPfXhpe0UUTc1a2gzMysmtomjXyl1JHAgyw5nxHAq7obmpmZVU2ZPY1DgM0i4uFuB2NmZtVW5uqpu4HHux2ImZlVX9M9DUmfyR/vAPoknQc8Xfs+Io7tcmxmZlYxrQ5PrZb//xO4C1gx/5mZ2RjVNGlExNGSlgdOiogPDGNMZmZWUS3PaUTEc8DakryHYWZmpa6emgf8RdLvgAW1nj6nYWY29pRJGvflv+VYcp7DzMzGoDKPETl6OAIxM7PqK3NH+CWkO8CXEhE7l6kgn0yfBdwbEe/oOEIzM6uMMoenDi18Xhl4D7CogzoOAW4BVu9gHDMzq6Ayh6dm1/X6S37qbVuSNgB2Bb4GfKbN4GZmVnGKeMGRp6UHkNYqdC4HbAN8PyI2a1u4dBbwDdIJ9EMbHZ6SNB2YDjB58uRtZs6cWT76rL+/nwkTJnQ83kgZqnjn3jt8T3eZPB4eXJg+T11/4rDVW6/sNBfjrRmpuMvE3CheGNl53Uq7NjyYttmtaV5W1xPTpk2bHRE9wxASUO7w1GzSOQ2RDkvdSXoFbEuS3gHMj4jZknqbDRcRxwPHA/T09ERvb9NBm+rr62Mg442UoYp3/8PPG3wwJc2Yuohj5qbmMm+f3mGrt17ZaS7GWzNScZeJuVG8MLLzupV2bXgwbbNb0zxW1xNDrczhqU0GWPb2wO6S3k46F7K6pFN9d7mZ2ejV9im3ksZJOljSWfnvk5LGtRsvIj4fERtExBTgfcDFThhmZqNbmcNTPwLGAT/M3fvmfh/uVlBmZlZNZZLGayJiy0L3xZKu76SSiOgD+joZx8zMqqfMS5iek7RprUPSS0ivfTUzszGmzJ7GZ4FLJN1BuoJqY+CArkZlZmaVVObqqYskvQzYjJQ0bo2Ip9uMZmZmy6Ayz55aGfg4sAPpfo0/SzouIp7qdnBmZlYtZQ5PnQw8Cfwgd78fOAXYq1tBmZlZNZVJGpvVXT11SadXT5mZ2bKhzNVT10nartYh6bXAX7oXkpmZVVXTPQ1Jc0nnMMYBH5R0V+7eGLh5eMIzM7MqaXV4yi9MMjOzpTRNGhHxz9rn/Pa9ya2GNzOzZV+ZS24PAo4EHgSez70DeFUX4zIzswoqs+dwCOkKqoe7HYyZmVVbmaun7gaG7xVxZmZWWa2unqq90/sOoE/SecDix4dExLFdjs3MzCqm1eGp1fL/u/LfivnPzMzGqFZXTx09nIGYmVn1lbl66hzS1VJFjwOzgB/7wYVmZmNHmRPhdwD9wAn57wnS5bcvz91mZjZGlLnk9tURsVOh+xxJl0XETpJu6lZgZmZWPWX2NNaWtFGtI3+elDuf6UpUZmZWSWX2NGYAl0v6B+nNfZsAH5e0KnBSN4MzM7NqKfO61/Pz615fwZLXvdZOfn+3i7GZmVnFtLq5b+eIuFjSu+u+eokkIuLXXY7NzMwqptWexhuAi4HdGnwXgJOGmdkY0+rmviPz/wOGLxwzM6uytldPSZos6aeSfp+7N5d0YInxVpZ0taTrJd0kyXeYm5mNcmUuuT0R+COwXu7+G/CpEuM9DewcEVsCWwG7FN81bmZmo0+ZpDEpIs4gv4ApIhYBz7UbKZL+3Dku/9U/jsTMzEaRMkljgaQXkVf4eW+h1Ps1JC0vaQ4wH7gwIq4aaKBmZjbyFNF641/S1sAPgC2AG4G1gT0j4obSlUhrAL8BDoqIG+u+mw5MB5g8efI2M2fO7CR+APr7+5kwYULH442UoYp37r3D926syePhwYXp89T1Jw64nOGKuRjvUOj2NA91vDWDibuVdm14ONtmvWbTvKyuJ6ZNmzY7InqGISSgRNIAkLQCsBnp5r7bIuLZjiuSjgQWRMR3mg3T09MTs2bN6rRo+vr66O3t7Xi8kTJU8U45/LzBB1PSjKmLOGZuuthu3jd3HXA5wxVzMd6h0O1pHup4awYTdyvt2vBwts16zaZ5WV1PSBrWpFG2lW4LTMnDb51v7ju51QiS1gaejYjHJI0H3gR8azDBmpnZyCrzPo1TgE2BOSw5AR5Ay6QBrAucJGl50rmTMyLi3IGHamZmI63MnkYPsHmUOY5VkM95vHpAUZmZWSWVuXrqRuDF3Q7EzMyqr8yexiTgZklXk27YAyAidu9aVGZmVkllksZR3Q7CzMxGhzLv07h0OAIxM7PqK3NOw8zMDHDSMDOzDjRNGpIuyv99Q56ZmQGtz2msK+kNwO6SZpIeIbJYRFzb1cjMzKxyWiWNI4DDgQ2AY+u+C2DnbgVlZmbV1Op1r2cBZ0n6UkR8ZRhjMjOziipzye1XJO0O7JR79fkZUmZmY1OZd4R/AzgEuDn/HZL7mZnZGFPmjvBdga0i4nkASScB1wGf72ZgZmZWPWXv01ij8Lk7rwIzM7PKK7On8Q3gOkmXkC673QnvZZiZjUllToSfLqkPeA0paRwWEQ90OzAzM6ueUq97jYj7gd91ORYzM6s4P3vKzMxKc9IwM7PSWiYNSctJunG4gjEzs2prmTTyvRnXS9pomOIxM7MKK3MifF3gpvyO8AW1nn5HuJnZ2FMmaRzd9SjMzGxUKPWOcEkbAy+LiD9JWgVYvvuhmZlZ1ZR5YOFHgLOAH+de6wNndzEmMzOrqDKX3H4C2B54AiAi/g6s082gzMysmsokjacj4plah6QVSG/ua0nShpIukXSLpJskHTKYQM3MbOSVSRqXSvovYLykNwNnAueUGG8RMCMiXglsB3xC0uYDD9XMzEZamaRxOPAvYC7wn8D5wBfbjRQR90fEtfnzk8AtpPMhZmY2Simi7ZEmJK0IvIJ0WOq24uGqUpVIU4DLgC0i4om676YD0wEmT568zcyZMzspGoD+/n7ufPy5jsermbr+8L4ipL+/nwkTJgy6nLn3Pj4E0ZQzeTw8uHDYqhs0x5sMpm23al+jbf5CuZiHe13QStn1xLRp02ZHRM8whASUSBqSdgWOA/5BejT6JsB/RsTvS1UgTQAuBb4WEb9uNWxPT0/MmjWrTLFL6evrY/8/LGg/YBPzvrnrgMcdiL6+Pnp7ewddzpTDzxt8MCXNmLqIY+aWeihyJTjeZDBtu1X7Gm3zF8rFPNzrglbKrickDWvSKLPUjwGmRcTtAJI2Bc4D2iYNSeOAXwGntUsYZmZWfWXOacyvJYzsDmB+u5EkCfgpcEtEHDvA+MzMrEKa7mlIenf+eJOk84EzSOc09gKuKVH29sC+wFxJc3K//4qI8wcerpmZjaRWh6d2K3x+EHhD/vwvYM12BUfE5aRzIGZmtoxomjQi4oDhDMTMzKqv7YlwSZsABwFTisP70ehmZmNPmaunziad0D4HeL6r0ZiZWaWVSRpPRcT3ux6JmZlVXpmk8T1JRwIXAE/XetYeEWJmZmNHmaQxlXTp7M4sOTwVudvMzMaQMknjXcBLOn3elJmZLXvK3BF+PbBGl+MwM7NRoMyexmTgVknXsPQ5DV9ya2Y2xpRJGkd2PQozMxsV2iaNiLh0OAIxM7PqK3NH+JMseSf4isA4YEFErN7NwMzMrHrK7GmsVuyW9E5g224FZGZm1VXm6qmlRMTZ+B4NM7MxqczhqXcXOpcDelhyuMrMzMaQMldPFd+rsQiYB+zRlWjMzKzSypzT8Hs1zMwMaP261yNajBcR8ZUuxGNmZhXWak9jQYN+qwIHAi8CnDTMzMaYVq97Pab2WdJqwCHAAcBM4Jhm45mZ2bKr5TkNSWsBnwH2AU4Cto6IR4cjMDMzq55W5zS+DbwbOB6YGhH9wxaVmZlVUqub+2YA6wFfBO6T9ET+e1LSE8MTnpmZVUmrcxod3y1uZmbLNicGMzMrrWtJQ9LPJM2XdGO36jAzs+HVzT2NE4Fduli+mZkNs64ljYi4DHikW+Wbmdnw8zkNMzMrTRHde8q5pCnAuRGxRYthpgPTASZPnrzNzJkzO66nv7+fOx9/bqBhMnX9iQMedyD6+/uZMGHCoMuZe+/jQxBNOZPHw4MLh626QXO83TXa4oVqx9xoHVR2PTFt2rTZEdHTjbgaKfNo9K6KiONJNxDS09MTvb29HZfR19fHMZc3elRWOfP26bzOwejr62Mg01lv/8PPG3wwJc2Yuohj5o54cynN8XbXaIsXqh1zo3XQUK0nhpoPT5mZWWndvOT2dOAKYDNJ90g6sFt1mZnZ8OjavlpEvL9bZZuZ2cjw4SkzMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzErratKQtIuk2yTdLunwbtZlZmbd17WkIWl54P+AtwGbA++XtHm36jMzs+7r5p7GtsDtEXFHRDwDzAT26GJ9ZmbWZYqI7hQs7QnsEhEfzt37Aq+NiE/WDTcdmJ47NwNuG0B1k4CHBhHucBtt8cLoi9nxdtdoixdGX8xl4904ItbudjA1K3SxbDXo94IMFRHHA8cPqiJpVkT0DKaM4TTa4oXRF7Pj7a7RFi+MvpirGm83D0/dA2xY6N4AuK+L9ZmZWZd1M2lcA7xM0iaSVgTeB/yui/WZmVmXde3wVEQskvRJ4I/A8sDPIuKmLlU3qMNbI2C0xQujL2bH212jLV4YfTFXMt6unQg3M7Nlj+8INzOz0pw0zMystFGdNEbDY0ok/UzSfEk3FvqtJelCSX/P/9ccyRiLJG0o6RJJt0i6SdIhuX8lY5a0sqSrJV2f4z06969kvDWSlpd0naRzc3fV450naa6kOZJm5X6VjVnSGpLOknRrbsuvq3i8m+V5W/t7QtKnqhjzqE0ao+gxJScCu9T1Oxy4KCJeBlyUu6tiETAjIl4JbAd8Is/Xqsb8NLBzRGwJbAXsImk7qhtvzSHALYXuqscLMC0itircO1DlmL8H/CEiXgFsSZrXlY03Im7L83YrYBvg38BvqGLMETEq/4DXAX8sdH8e+PxIx9Uk1inAjYXu24B18+d1gdtGOsYWsf8WePNoiBlYBbgWeG2V4yXds3QRsDNw7mhoE8A8YFJdv0rGDKwO3Em+0Kfq8TaI/y3AX6oa86jd0wDWB+4udN+T+40GkyPifoD8f50RjqchSVOAVwNXUeGY86GeOcB84MKIqHS8wHeBzwHPF/pVOV5IT3O4QNLs/OgfqG7MLwH+Bfw8HwL8iaRVqW689d4HnJ4/Vy7m0Zw0Sj2mxAZG0gTgV8CnIuKJkY6nlYh4LtJu/QbAtpK2GOGQmpL0DmB+RMwe6Vg6tH1EbE06HPwJSTuNdEAtrABsDfwoIl4NLKAKh3VKyDdC7w6cOdKxNDOak8ZofkzJg5LWBcj/549wPEuRNI6UME6LiF/n3pWOGSAiHgP6SOeQqhrv9sDukuaRnvy8s6RTqW68AETEffn/fNKx9m2pbsz3APfkPU6As0hJpKrxFr0NuDYiHszdlYt5NCeN0fyYkt8B++XP+5HOG1SCJAE/BW6JiGMLX1UyZklrS1ojfx4PvAm4lYrGGxGfj4gNImIKqc1eHBEfoKLxAkhaVdJqtc+kY+43UtGYI+IB4G5Jm+VebwRupqLx1nk/Sw5NQRVjHumTKoM8YfR24G/AP4AvjHQ8TWI8HbgfeJa0BXQg8CLSidC/5/9rjXSchXh3IB3muwGYk//eXtWYgVcB1+V4bwSOyP0rGW9d7L0sORFe2XhJ5wiuz3831X5rFY95K2BWbhdnA2tWOd4c8yrAw8DEQr/KxezHiJiZWWmj+fCUmZkNMycNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0bUyS9WNJMSf+QdLOk8yW9XNLC/HTRmyWdnG9wRFJv4Um0+0sKSW8slPeu3G/P3N2n9OTl2tNKzxqZKTXrjq697tWsavKNi78BToqI9+V+WwGTgX9ExFb56ckXAu8FTmtQzFzSDVgX5e73ke5fKNonImYN/RSYjTzvadhYMg14NiKOq/WIiDkUHnwZEc8BV9P84Zd/Jj3falx+PtdLSTdAmo0J3tOwsWQLoOWDAiWtTHq0+iFNBgngT8BbgYmkxzxsUjfMaZIW5s8XRsRnBxyxWcV4T8Ms2TQ/Xv1h4K6IuKHFsDNJh6WKj7Au2ifyC3WcMGxZ46RhY8lNpLeiNfKPSI9XfymwnaTdmxUSEVeT9lomRcTfhjxKswpz0rCx5GJgJUkfqfWQ9Bpg41p3pBfdHE56E2Qrnwf+qxtBmlWZk4aNGZGezvku4M35ktubgKN44XtYzgZWkbRji7J+HxGXNPn6tMIlt38agtDNKsNPuTUzs9K8p2FmZqU5aZiZWWmjPmlIei4fO75R0jm1V392WEaPpO83+W6epEkDjO2dkjYvdH9Z0pvajHNi7ZEUdf37JPUMJI4ScTass804H5X0wW7E00EMUyTdOATlLH5USIPvflJchg2+31/S/3ZQ11aS3l7oPkrSoR3Gu5ekWyRdkmN/fSfjDyVJO0m6VtKi+jYkaT9Jf89/+xX6byLpqtz/l/l1zSj5vqTbJd0gaesmdfZ3d6oa63Rd0KptjNQ0DIVRnzSAhfl6+C2AR4BPdFpARMyKiIOHPjTeCSxe4UTEEREx7CdG849xyJa1pBUi4riIOHmoyqyqiPhwRNw8hEVuRXp97mAcCHw8IqaRXhk7bEkjP2al6C5gf+AXdcOtBRxJulFyW+BISWvmr78F/E9EvAx4lDQ9AG8DXpb/pgM/6sIk2CAtC0mj6Ary4x8kbSrpD5JmS/qzpFfk/nvlvZLrJV2W+xUfSvciSRdIuk7SjwHVCpf0AUlX5z2bH9d+QJL6JX0tl3mlpMl562934Nt5+E2LW/SSjpB0TY7leEmivQ9I+mseZ9tczlJbqvm7KfnvFkk/BK4FNpT0JUm3SrpQ0umNtnCbxZX3dL4u6VLgkGK9nczruromSLoob6nOlbRH7l+L/QRJN+XlMT5/t00u7wqabCDkrdfi1vyJkt4jaXlJ387Td4Ok/yyMNkHSWXn+nFY33T358y451uslXVRXLZLWlvSrXP41krav+35F4MvA3rlN7J2/2jzXc4ekgwvDv6C9STqC9B734ySdCXwU+HQeZscS87xX0mWSfqP0cMbjlDcoJL1F0hV5Gs9UekxKbQv7CEmXA3sVy4uIeflGyOfrqnor6W74RyLiUdLzvHbJ83VnoPYgx5NIG1cAewAnR3IlsIakdeunIce01O8t99s4t6cb8v+Ncv+l9qSVt/IlrZvnRe1IxY6t5kN2UKG91tr5WpLOzvVeKelVDeLdJJd5jaSvFPo3jKHSRvol5UPwMvb+/H954Exgl9x9EfCy/Pm1wMX581xg/fx5jfy/Fzg3f/4+cET+vCvpsRGTgFcC5wDj8nc/BD6YPwewW/7838AX8+cTgT0LsS7upvCCeOCUwvhLjVMYpg84IX/eCbgxfz4KOLQw3I3AlPz3PLBd7t9DekbSeGA10ovqD+0grj7gh4XvjiqMX3pe103TCsDq+fMk4HZSkp4CLAK2yt+dAXwgf74BeEP+/O3afKgr912khxICrEh6ttR40tZrbdmsBMwiPQKkF3gc2IC0IXUFsENhunuAtXM5mxTnE2kr+3/z518UxtsIuKVBbIuHL8zHv+Z4JpHuSB9H6/bWB/Q0Wf7t5nkv8BTwEtJv5kJgz1z3ZcCqebjDWPI7mAd8rs3v8ESWbuuH1uZ17v5S7jcJuL3Qf0OWtOVza/Ov0K56GtTV7Pd2DrBf/vwh4OwmsdXWGTOALxTWH6uVmA8H5c8fB36SP/8AODJ/3hmY06Bt/K6w/D7RKoZO14HD/bcsPHtqvNLjH6aQnit0Yd4yeD1wppZswK+U//8FOFHSGcCvG5S3E/BugIg4T9Kjuf8bSXcTX5PLHA/Mz989Q2rw5BjeXCLuaZI+B6wCrEW6W/mcNuOcnuO6TNLqan/+5p+RttggbZ3+NiIWAkhqVleruH5ZP/Ag57WAr0vaiZTg1ic9cRbgzkgPE4Q0T6dImkhaEV6a+59COqRR7/fA9yWtBOwCXBYRCyW9BXhVYatzIulQyDPA1RFxT56mOaT2dHmhzO1yOXcCRMQjDep9E2mvoda9uqTVIuLJBsMWnRcRTwNPS5qf50Gr9tZKu3kOaVrvAJB0OqltPEU6lPqXXN+KpORZ84Jl30ajPedo0b/VOPWa/d5eR/7tktrGf7eJ8RrgZ0qPwT87IuZIegOt50Ntns4u1LUD8B6AiLhY6WjFxLq6tq8Nk2P7VrMY2sQ84paFpLEw0iOtJ5Ia0idIWxaPRXosxFIi4qOSXkvai5ij9GjsFwzWoJ9IW6+N7hR+NvKmAvAcbear0kPxfkjairpb0lHAyq3GaRJXkLbIi4cZi+UsKFbbrvAScS1oMNpydDCvI+LhwiD7kLbgt4mIZyXNK9T3dGG450grTdF42dTX+5SkPtIhkr1Z8nwokbYU/1g33b0N6qtfhmXqXg54XS0xd6BR3a3aW1Ml5jk0bkciHU56f5OiGy37Vu4h7dXUbEDaQ3qIdNhphYhYlPvfVxhnw7px6m+8hPK/t9owi38j+fDYirB442sn0rw6RdK3SedYWs2H2rIq1ls22b2gX6MYouLnCpeZcxoR8ThwMGkXeCFwp6S9YPGJ4C3z500j4qqIOILUgDesK+oy0soMSW8DaifvLgL2lLRO/m4tSRvT2pOkXd56tRXjQ3lLveyVS3vnuncAHs/TPA/YOvffmhc+cbXmcmA3SSvnOncdirgi4gkGPq8nAvNzwphG4XEeTep6DHg8Tz/k5dTETOAAYEegliT+CHxMS16w9HJJq7abxuwK4A2SNsnjrtVgmAuAT9Y6mmyQNGsT9cq2t6XKKzHPIT3afROlcxl7k9rGlcD2kl6ay1lF0stLxNnMH4G3SFpT6QT4W4A/5pX9JSxpW/sBv82ffwd8MLeh7Uht/P4O6vwr6SGSkNpGbU9xHkueObYH6fAfeX7Oj4gTgJ+SfkcDmQ/FdUYv8FD+XRT9pS42WsRQactM0gCIiOtIL8R5H2nBHCjpetIhlj3yYN9WOol1I2lh179A52hgJ0nXkhr6Xbnsm4EvAhdIuoF0LLjhSbqCmcBnlU6qb1qI8zHgBNLx57NJu6hlPCrpr8BxLLni5FfAWvmQyseAhg/Qi4hrSD/K60m72LNIx/GLwww0roHO69OAHkmzchm3lqjrAOD/lE6Et9qiv4B0qPFPEfFM7vcT4Gbg2hzTjym5tx0R/yKdE/l1ns5Gh2sOztNzg6SbSSep611COoRVPBHeqL6y7e0c4F25vB1pP88hJcBvks5/3Qn8Jk/f/sDpub4rgVc0i69G0msk3UM6Qf5jpUez1A7ffYXUhq4Bvlw4pHcY8BlJtwMvIq0sAc4H7iCd2zqBdN6gEwcDB+T492XJ4+1PICX8q0nn3Gp7Tb2kvbHrSIeOvjfA+XAUebmT5ut+DYY5BPiEpGtIG0s1L4ih7MSOFD9GZAyRNCEi+iWtQlqhTI+Ia0c6Lhs+eUv40Ih4xwiHYqPUsnBOw8o7XulGtZVJx8udMMysI97TMDOz0papcxpmZtZdThpmZlaak4aZmZXmpGFmZqU5aZiZWWlOGmZmVtr/BwQNjQmf6ze9AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Let's take a look at how the CRIME variable is distributed with a histogram\n", "tracts['CRIME'].hist(bins=20)\n", "plt.xlabel('CRIME\\nResidential burglaries and vehicle thefts per 1000 households')\n", "plt.ylabel('Number of neighbourhoods')\n", "plt.title('Distribution of neighbourhoods by crime rate in Columbus, OH')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see what it looks like without a classification scheme:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:29:54.097280Z", "start_time": "2017-12-15T21:29:53.766283Z" }, "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts.plot(column='CRIME', cmap='OrRd', edgecolor='k', legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the 49 neighbourhoods are colored along a white-to-dark-red gradient, but the human eye can have a hard time comparing the color of shapes that are distant one to the other. In this case, it is especially hard to rank the peripheral districts colored in beige.\n", "\n", "Instead, we'll classify them in color bins." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification by quantiles\n", ">QUANTILES will create attractive maps that place an equal number of observations in each class: If you have 30 counties and 6 data classes, you’ll have 5 counties in each class. The problem with quantiles is that you can end up with classes that have very different numerical ranges (e.g., 1-4, 4-9, 9-250)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:30:30.408917Z", "start_time": "2017-12-15T21:30:30.088920Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Splitting the data in three shows some spatial clustering around the center\n", "tracts.plot(column='CRIME', scheme='quantiles', k=3, cmap='OrRd', edgecolor='k', legend=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.376417Z", "start_time": "2017-12-15T21:27:57.039Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We can also see where the top and bottom halves are located\n", "tracts.plot(column='CRIME', scheme='quantiles', k=2, cmap='OrRd', edgecolor='k', legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification by equal intervals\n", ">EQUAL INTERVAL divides the data into equal size classes (e.g., 0-10, 10-20, 20-30, etc.) and works best on data that is generally spread across the entire range. CAUTION: Avoid equal interval if your data are skewed to one end or if you have one or two really large outlier values." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.376417Z", "start_time": "2017-12-15T21:27:57.045Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts.plot(column='CRIME', scheme='equal_interval', k=4, cmap='OrRd', edgecolor='k', legend=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.386417Z", "start_time": "2017-12-15T21:27:57.048Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# No legend here as we'd be out of space\n", "tracts.plot(column='CRIME', scheme='equal_interval', k=12, cmap='OrRd', edgecolor='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classificaton by natural breaks\n", ">NATURAL BREAKS is a kind of “optimal” classification scheme that finds class breaks that will minimize within-class variance and maximize between-class differences. One drawback of this approach is each dataset generates a unique classification solution, and if you need to make comparison across maps, such as in an atlas or a series (e.g., one map each for 1980, 1990, 2000) you might want to use a single scheme that can be applied across all of the maps." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-12-15T21:28:00.376417Z", "start_time": "2017-12-15T21:27:57.042Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Compare this to the previous 3-bin figure with quantiles\n", "tracts.plot(column='CRIME', scheme='natural_breaks', k=3, cmap='OrRd', edgecolor='k', legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other classification schemes in PySAL\n", "\n", "Geopandas includes only the most used classifiers found in PySAL. In order to use the others, you will need to add them as additional columns to your GeoDataFrame.\n", "\n", ">The max-p algorithm determines the number of regions (p) endogenously based on a set of areas, a matrix of attributes on each area and a floor constraint. The floor constraint defines the minimum bound that a variable must reach for each region; for example, a constraint might be the minimum population each region must have. max-p further enforces a contiguity constraint on the areas within regions." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AREAPERIMETERCOLUMBUS_COLUMBUS_IPOLYIDNEIGHOVALINCCRIMEOPEN...XYNSANSBEWCPTHOUSNEIGNOgeometryMax_P
00.3094412.440629251580.46700319.53115.7259802.850747...38.79999944.0700001.01.01.00.01000.01005.0POLYGON ((8.62413 14.23698, 8.55970 14.74245, ...0
10.2593292.236939312144.56700121.23218.8017545.296720...35.61999942.3800011.01.00.00.01000.01001.0POLYGON ((8.25279 14.23694, 8.28276 14.22994, ...0
20.1924682.187547463626.35000015.95630.6267814.534649...39.82000041.1800001.01.01.00.01000.01006.0POLYGON ((8.65331 14.00809, 8.81814 14.00205, ...2
30.0838411.427635524233.2000014.47732.3877600.394427...36.50000040.5200001.01.00.00.01000.01002.0POLYGON ((8.45950 13.82035, 8.47341 13.83227, ...2
40.4888882.997133675723.22500011.25250.7315100.405664...40.00999838.0000001.01.01.00.01000.01007.0POLYGON ((8.68527 13.63952, 8.67758 13.72221, ...3
\n", "

5 rows × 22 columns

\n", "
" ], "text/plain": [ " AREA PERIMETER COLUMBUS_ COLUMBUS_I POLYID NEIG HOVAL \\\n", "0 0.309441 2.440629 2 5 1 5 80.467003 \n", "1 0.259329 2.236939 3 1 2 1 44.567001 \n", "2 0.192468 2.187547 4 6 3 6 26.350000 \n", "3 0.083841 1.427635 5 2 4 2 33.200001 \n", "4 0.488888 2.997133 6 7 5 7 23.225000 \n", "\n", " INC CRIME OPEN ... X Y NSA NSB EW CP \\\n", "0 19.531 15.725980 2.850747 ... 38.799999 44.070000 1.0 1.0 1.0 0.0 \n", "1 21.232 18.801754 5.296720 ... 35.619999 42.380001 1.0 1.0 0.0 0.0 \n", "2 15.956 30.626781 4.534649 ... 39.820000 41.180000 1.0 1.0 1.0 0.0 \n", "3 4.477 32.387760 0.394427 ... 36.500000 40.520000 1.0 1.0 0.0 0.0 \n", "4 11.252 50.731510 0.405664 ... 40.009998 38.000000 1.0 1.0 1.0 0.0 \n", "\n", " THOUS NEIGNO geometry Max_P \n", "0 1000.0 1005.0 POLYGON ((8.62413 14.23698, 8.55970 14.74245, ... 0 \n", "1 1000.0 1001.0 POLYGON ((8.25279 14.23694, 8.28276 14.22994, ... 0 \n", "2 1000.0 1006.0 POLYGON ((8.65331 14.00809, 8.81814 14.00205, ... 2 \n", "3 1000.0 1002.0 POLYGON ((8.45950 13.82035, 8.47341 13.83227, ... 2 \n", "4 1000.0 1007.0 POLYGON ((8.68527 13.63952, 8.67758 13.72221, ... 3 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def max_p(values, k):\n", " \"\"\"\n", " Given a list of values and `k` bins,\n", " returns a list of their Maximum P bin number.\n", " \"\"\"\n", " from mapclassify import MaxP\n", " binning = MaxP(values, k=k)\n", " return binning.yb\n", "\n", "tracts['Max_P'] = max_p(tracts['CRIME'].values, k=5)\n", "tracts.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tracts.plot(column='Max_P', cmap='OrRd', edgecolor='k', categorical=True, legend=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "nbsphinx": { "execute": "never" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/create_geopandas_from_pandas.ipynb000066400000000000000000000124501413247402600261440ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Creating a GeoDataFrame from a DataFrame with coordinates\n", "\n", "This example shows how to create a ``GeoDataFrame`` when starting from\n", "a *regular* ``DataFrame`` that has coordinates either WKT\n", "([well-known text](https://en.wikipedia.org/wiki/Well-known_text))\n", "format, or in\n", "two columns.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import geopandas\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From longitudes and latitudes\n", "=============================\n", "\n", "First, let's consider a ``DataFrame`` containing cities and their respective\n", "longitudes and latitudes.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(\n", " {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n", " 'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n", " 'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],\n", " 'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A ``GeoDataFrame`` needs a ``shapely`` object. We use geopandas\n", "``points_from_xy()`` to transform **Longitude** and **Latitude** into a list\n", "of ``shapely.Point`` objects and set it as a ``geometry`` while creating the\n", "``GeoDataFrame``. (note that ``points_from_xy()`` is an enhanced wrapper for\n", "``[Point(x, y) for x, y in zip(df.Longitude, df.Latitude)]``)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = geopandas.GeoDataFrame(\n", " df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``gdf`` looks like this :\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(gdf.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we plot the coordinates over a country-level map.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", "\n", "# We restrict to South America.\n", "ax = world[world.continent == 'South America'].plot(\n", " color='white', edgecolor='black')\n", "\n", "# We can now plot our ``GeoDataFrame``.\n", "gdf.plot(ax=ax, color='red')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From WKT format\n", "===============\n", "Here, we consider a ``DataFrame`` having coordinates in WKT format.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(\n", " {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n", " 'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n", " 'Coordinates': ['POINT(-58.66 -34.58)', 'POINT(-47.91 -15.78)',\n", " 'POINT(-70.66 -33.45)', 'POINT(-74.08 4.60)',\n", " 'POINT(-66.86 10.48)']})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use ``shapely.wkt`` sub-module to parse wkt format:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from shapely import wkt\n", "\n", "df['Coordinates'] = geopandas.GeoSeries.from_wkt(df['Coordinates'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``GeoDataFrame`` is constructed as follows :\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')\n", "\n", "print(gdf.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we can plot our ``GeoDataFrame``.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = world[world.continent == 'South America'].plot(\n", " color='white', edgecolor='black')\n", "\n", "gdf.plot(ax=ax, color='red')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/geopandas_rasterio_sample.ipynb000066400000000000000000000077201413247402600255250ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Using GeoPandas with Rasterio to sample point data\n", "\n", "This example shows how to use GeoPandas with Rasterio. [Rasterio](https://rasterio.readthedocs.io/en/latest/index.html) is a package for reading and writing raster data.\n", "\n", "In this example a set of vector points is used to sample raster data at those points.\n", "\n", "The raster data used is Copernicus Sentinel data 2018 for Sentinel data.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import rasterio\n", "import matplotlib.pyplot as plt\n", "from shapely.geometry import Point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create example vector data\n", "=============================\n", "\n", "Generate a geodataframe from a set of points\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create sampling points\n", "points = [Point(625466, 5621289), Point(626082, 5621627), Point(627116, 5621680), Point(625095, 5622358)]\n", "gdf = geopandas.GeoDataFrame([1, 2, 3, 4], geometry=points, crs=32630)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``GeoDataFrame`` looks like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open the raster data\n", "=============================\n", "\n", "Use ``rasterio`` to open the raster data to be sampled" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "src = rasterio.open('s2a_l2a_fishbourne.tif')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see the raster data with the point data overlaid.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "from rasterio.plot import show\n", "\n", "fig, ax = plt.subplots()\n", "\n", "# transform rasterio plot to real world coords\n", "extent=[src.bounds[0], src.bounds[2], src.bounds[1], src.bounds[3]]\n", "ax = rasterio.plot.show(src, extent=extent, ax=ax, cmap='pink')\n", "\n", "gdf.plot(ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sampling the data\n", "===============\n", "Rasterio requires a list of the coordinates in x,y format rather than as the points that are in the geomentry column.\n", "\n", "This can be achieved using the code below" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coord_list = [(x,y) for x,y in zip(gdf['geometry'].x , gdf['geometry'].y)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Carry out the sampling of the data and store the results in a new column called `value`. Note that if the image has more than one band, a value is returned for each band." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf['value'] = [x for x in src.sample(coord_list)]\n", "gdf.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/index.rst000066400000000000000000000004051413247402600211020ustar00rootroot00000000000000Examples Gallery ================ The following examples show off the functionality in GeoPandas. They highlight many of the things you can do with this package, and show off some best-practices. .. nbgallery:: :name: nbshpinx-gallery :glob: ./*geopandas-0.10.2/doc/source/gallery/matplotlib_scalebar.ipynb000066400000000000000000000153711413247402600243170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Adding a scale bar to a matplotlib plot\n", "When making a geospatial plot in matplotlib, you can use [maplotlib-scalebar library](https://pypi.org/project/matplotlib-scalebar/) to add a scale bar." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "from matplotlib_scalebar.scalebar import ScaleBar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a ScaleBar object\n", "The only required parameter for creating a ScaleBar object is `dx`. This is equal to a size of one pixel in real world. Value of this parameter depends on units of your CRS.\n", "\n", "### Projected coordinate system (meters)\n", "The easiest way to add a scale bar is using a projected coordinate system with meters as units. Just set `dx = 1`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "nybb = gpd.read_file(gpd.datasets.get_path('nybb'))\n", "nybb = nybb.to_crs(32619) # Convert the dataset to a coordinate\n", "# system which uses meters\n", "\n", "ax = nybb.plot()\n", "ax.add_artist(ScaleBar(1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geographic coordinate system (degrees)\n", "With a geographic coordinate system with degrees as units, `dx` should be equal to a distance in meters of two points with the same latitude (Y coordinate) which are one full degree of longitude (X) apart. You can calculate this distance by online calculator [(e.g. the Great Circle calculator)](http://edwilliams.org/gccalc.htm) or in geopandas.\\\n", "\\\n", "Firstly, we will create a GeoSeries with two points that have roughly the coordinates of NYC. They are located on the same latitude but one degree of longitude from each other. Their initial coordinates are specified in a geographic coordinate system (geographic WGS 84). They are then converted to a projected system for the calculation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from shapely.geometry.point import Point\n", "\n", "points = gpd.GeoSeries([Point(-73.5, 40.5), Point(-74.5, 40.5)], crs=4326) # Geographic WGS 84 - degrees\n", "points = points.to_crs(32619) # Projected WGS 84 - meters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the conversion, we can calculate the distance between the points. The result slightly differs from the Great Circle Calculator but the difference is insignificant (84,921 and 84,767 meters):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "distance_meters = points[0].distance(points[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we are able to use geographic coordinate system in our plot. We set value of `dx` parameter to a distance we just calculated:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "nybb = gpd.read_file(gpd.datasets.get_path('nybb'))\n", "nybb = nybb.to_crs(4326) # Using geographic WGS 84\n", "\n", "ax = nybb.plot()\n", "ax.add_artist(ScaleBar(distance_meters))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using other units \n", "The default unit for `dx` is m (meter). You can change this unit by the `units` and `dimension` parameters. There is a list of some possible `units` for various values of `dimension` below:\n", "\n", "| dimension | units |\n", "| ----- |:-----:|\n", "| si-length | km, m, cm, um|\n", "| imperial-length |in, ft, yd, mi|\n", "|si-length-reciprocal|1/m, 1/cm|\n", "|angle|deg|\n", "\n", "In the following example, we will leave the dataset in its initial CRS which uses feet as units. The plot shows scale of 2 leagues (approximately 11 kilometers):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nybb = gpd.read_file(gpd.datasets.get_path('nybb'))\n", "\n", "ax = nybb.plot()\n", "ax.add_artist(ScaleBar(1, dimension=\"imperial-length\", units=\"ft\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customization of the scale bar" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "nybb = gpd.read_file(gpd.datasets.get_path('nybb')).to_crs(32619)\n", "ax = nybb.plot()\n", "\n", "# Position and layout\n", "scale1 = ScaleBar(\n", "dx=1, label='Scale 1',\n", " location='upper left', # in relation to the whole plot\n", " label_loc='left', scale_loc='bottom' # in relation to the line\n", ")\n", "\n", "# Color\n", "scale2 = ScaleBar(\n", " dx=1, label='Scale 2', location='center', \n", " color='#b32400', box_color='yellow',\n", " box_alpha=0.8 # Slightly transparent box\n", ")\n", "\n", "# Font and text formatting\n", "scale3 = ScaleBar(\n", " dx=1, label='Scale 3',\n", " font_properties={'family':'serif', 'size': 'large'}, # For more information, see the cell below\n", " scale_formatter=lambda value, unit: f'> {value} {unit} <'\n", ")\n", "\n", "ax.add_artist(scale1)\n", "ax.add_artist(scale2)\n", "ax.add_artist(scale3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note:* Font is specified by six properties: `family`, `style`, `variant`, `stretch`, `weight`, `size` (and `math_fontfamily`). See [more](https://matplotlib.org/stable/api/font_manager_api.html#matplotlib.font_manager.FontProperties).\\\n", "\\\n", "For more information about matplotlib-scalebar library, see the [PyPI](https://pypi.org/project/matplotlib-scalebar/) or [GitHub](https://github.com/ppinard/matplotlib-scalebar) page." ] } ], "metadata": { "interpreter": { "hash": "9914e2881520d4f08a067c2c2c181121476026b863eca2e121cd0758701ab602" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/nyc.png000066400000000000000000011771131413247402600205540ustar00rootroot00000000000000PNG  IHDRk.sBIT|d pHYs.#.#x?v 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PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PE`X PEN៺IENDB`geopandas-0.10.2/doc/source/gallery/overlays.ipynb000066400000000000000000000122701413247402600221530ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "source": [ "# Overlays\n", "\n", "Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries \n", "and create a new GeoDataFrame with the new geometries representing the spatial combination *and*\n", "merged properties. This allows you to answer questions like\n", "\n", "> What are the demographics of the census tracts within 1000 ft of the highway?\n", "\n", "The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, \n", "not on individual geometries, and the properties from both are retained\n", "\n", "![illustration](http://docs.qgis.org/testing/en/_images/overlay_operations.png)" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Now we can load up two GeoDataFrames containing (multi)polygon geometries..." ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "%matplotlib inline\n", "from shapely.geometry import Point\n", "from geopandas import datasets, GeoDataFrame, read_file\n", "from geopandas.tools import overlay\n", "\n", "# NYC Boros\n", "zippath = datasets.get_path('nybb')\n", "polydf = read_file(zippath)\n", "\n", "# Generate some circles\n", "b = [int(x) for x in polydf.total_bounds]\n", "N = 10\n", "polydf2 = GeoDataFrame([\n", " {'geometry': Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y}\n", " for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),\n", " range(b[1], b[3], int((b[3] - b[1]) / N)))])" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "The first dataframe contains multipolygons of the NYC boros" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "polydf.plot()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "And the second GeoDataFrame is a sequentially generated set of circles in the same geographic space. We'll plot these with a [different color palette](https://matplotlib.org/examples/color/colormaps_reference.html)." ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "polydf2.plot(cmap='tab20b')" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "The `geopandas.tools.overlay` function takes three arguments:\n", "\n", "* df1\n", "* df2\n", "* how\n", "\n", "Where `how` can be one of:\n", "\n", " ['intersection',\n", " 'union',\n", " 'identity',\n", " 'symmetric_difference',\n", " 'difference']\n", "\n", "So let's identify the areas (and attributes) where both dataframes intersect using the `overlay` method. " ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "newdf = polydf.overlay(polydf2, how=\"intersection\")\n", "newdf.plot(cmap='tab20b')" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained. " ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "polydf.head()" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "polydf2.head()" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "newdf.head()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Now let's look at the other `how` operations:" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "newdf = polydf.overlay(polydf2, how=\"union\")\n", "newdf.plot(cmap='tab20b')" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "newdf = polydf.overlay(polydf2, how=\"identity\")\n", "newdf.plot(cmap='tab20b')" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "newdf = polydf.overlay(polydf2, how=\"symmetric_difference\")\n", "newdf.plot(cmap='tab20b')" ], "outputs": [], "metadata": { "tags": [ "nbsphinx-thumbnail" ] } }, { "cell_type": "code", "execution_count": null, "source": [ "newdf = polydf.overlay(polydf2, how=\"difference\")\n", "newdf.plot(cmap='tab20b')" ], "outputs": [], "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }geopandas-0.10.2/doc/source/gallery/plot_clip.ipynb000066400000000000000000000146211413247402600222760ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "source": [ "# Clip Vector Data with GeoPandas\n", "\n", "\n", "Learn how to clip geometries to the boundary of a polygon geometry\n", "using GeoPandas." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "The example below shows you how to clip a set of vector geometries\n", "to the spatial extent / shape of another vector object. Both sets of geometries\n", "must be opened with GeoPandas as GeoDataFrames and be in the same Coordinate\n", "Reference System (CRS) for the `clip` function in GeoPandas to work.\n", "\n", "This example uses GeoPandas example data ``'naturalearth_cities'`` and\n", "``'naturalearth_lowres'``, alongside a custom rectangle geometry made with\n", "shapely and then turned into a GeoDataFrame.\n", "\n", "
\n", " \n", "Note\n", "\n", "The object to be clipped will be clipped to the full extent of the clip\n", "object. If there are multiple polygons in clip object, the input data will\n", "be clipped to the total boundary of all polygons in clip object.\n", "
\n", "\n" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Import Packages\n", "---------------\n", "\n", "To begin, import the needed packages.\n", "\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "import matplotlib.pyplot as plt\n", "import geopandas\n", "from shapely.geometry import Polygon" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Get or Create Example Data\n", "--------------------------\n", "\n", "Below, the example GeoPandas data is imported and opened as a GeoDataFrame.\n", "Additionally, a polygon is created with shapely and then converted into a\n", "GeoDataFrame with the same CRS as the GeoPandas world dataset.\n", "\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "capitals = geopandas.read_file(geopandas.datasets.get_path(\"naturalearth_cities\"))\n", "world = geopandas.read_file(geopandas.datasets.get_path(\"naturalearth_lowres\"))\n", "\n", "# Create a subset of the world data that is just the South American continent\n", "south_america = world[world[\"continent\"] == \"South America\"]\n", "\n", "# Create a custom polygon\n", "polygon = Polygon([(0, 0), (0, 90), (180, 90), (180, 0), (0, 0)])\n", "poly_gdf = geopandas.GeoDataFrame([1], geometry=[polygon], crs=world.crs)" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Plot the Unclipped Data\n", "-----------------------\n", "\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))\n", "world.plot(ax=ax1)\n", "poly_gdf.boundary.plot(ax=ax1, color=\"red\")\n", "south_america.boundary.plot(ax=ax2, color=\"green\")\n", "capitals.plot(ax=ax2, color=\"purple\")\n", "ax1.set_title(\"All Unclipped World Data\", fontsize=20)\n", "ax2.set_title(\"All Unclipped Capital Data\", fontsize=20)\n", "ax1.set_axis_off()\n", "ax2.set_axis_off()\n", "plt.show()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Clip the Data\n", "--------------\n", "\n", "The object on which you call `clip` is the object that will\n", "be clipped. The object you pass is the clip extent. The returned output\n", "will be a new clipped GeoDataframe. All of the attributes for each returned\n", "geometry will be retained when you clip.\n", "\n", "
\n", "\n", "Note\n", "\n", "Recall that the data must be in the same CRS in order to use the\n", "`clip` method. If the data are not in the same CRS, be sure to use\n", "the GeoPandas `GeoDataFrame.to_crs` method to ensure both datasets\n", "are in the same CRS.\n", "
\n", "\n" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Clip the World Data\n", "--------------------\n", "\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "world_clipped = world.clip(polygon)\n", "\n", "# Plot the clipped data\n", "# The plot below shows the results of the clip function applied to the world\n", "# sphinx_gallery_thumbnail_number = 2\n", "fig, ax = plt.subplots(figsize=(12, 8))\n", "world_clipped.plot(ax=ax, color=\"purple\")\n", "world.boundary.plot(ax=ax)\n", "poly_gdf.boundary.plot(ax=ax, color=\"red\")\n", "ax.set_title(\"World Clipped\", fontsize=20)\n", "ax.set_axis_off()\n", "plt.show()" ], "outputs": [], "metadata": { "tags": [ "nbsphinx-thumbnail" ] } }, { "cell_type": "markdown", "source": [ "
\n", " \n", "Note\n", "\n", "For historical reasons, the clip method is also available as a top-level function `geopandas.clip`.\n", "It is recommended to use the method as the function may be deprecated in the future.\n", "
\n", "\n", "Clip the Capitals Data\n", "----------------------\n", "\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "capitals_clipped = capitals.clip(south_america)\n", "\n", "# Plot the clipped data\n", "# The plot below shows the results of the clip function applied to the capital cities\n", "fig, ax = plt.subplots(figsize=(12, 8))\n", "capitals_clipped.plot(ax=ax, color=\"purple\")\n", "south_america.boundary.plot(ax=ax, color=\"green\")\n", "ax.set_title(\"Capitals Clipped\", fontsize=20)\n", "ax.set_axis_off()\n", "plt.show()" ], "outputs": [], "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }geopandas-0.10.2/doc/source/gallery/plotting_basemap_background.ipynb000066400000000000000000000155111413247402600260370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Adding a background map to plots\n", "\n", "This example shows how you can add a background basemap to plots created\n", "with the geopandas ``.plot()`` method. This makes use of the\n", "[contextily](https://github.com/geopandas/contextily) package to retrieve\n", "web map tiles from several sources (OpenStreetMap, Stamen). Also have a\n", "look at contextily's \n", "[introduction guide](https://contextily.readthedocs.io/en/latest/intro_guide.html#Using-transparent-layers)\n", "for possible new features not covered here.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import contextily as cx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the NYC borough boundary data that is available in geopandas\n", "datasets. Plotting this gives the following result:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = geopandas.read_file(geopandas.datasets.get_path('nybb'))\n", "ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matching coordinate systems \n", "\n", "\n", "Before adding web map tiles to this plot, we first need to ensure the\n", "coordinate reference systems (CRS) of the tiles and the data match.\n", "Web map tiles are typically provided in\n", "[Web Mercator](https://en.wikipedia.org/wiki/Web_Mercator>)\n", "([EPSG 3857](https://epsg.io/3857)), so let us first check what\n", "CRS our NYC boroughs are in:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we know the CRS do not match, so we need to choose in which\n", "CRS we wish to visualize the data: either the CRS of the tiles,\n", "the one of the data, or even a different one.\n", "\n", "The first option to match CRS is to leverage the `to_crs` method\n", "of GeoDataFrames to convert the CRS of our data, here to Web Mercator:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_wm = df.to_crs(epsg=3857)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then use `add_basemap` function of contextily to easily add a\n", "background map to our plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')\n", "cx.add_basemap(ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to convert the CRS of the tiles instead, which might be advisable\n", "for large datasets, we can use the `crs` keyword argument of `add_basemap`\n", "as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')\n", "cx.add_basemap(ax, crs=df.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This reprojects map tiles to a target CRS which may in some cases cause a\n", "loss of sharpness. See \n", "[contextily's guide on warping tiles](https://contextily.readthedocs.io/en/latest/warping_guide.html)\n", "for more information on the subject." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Controlling the level of detail" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can control the detail of the map tiles using the optional `zoom` keyword\n", "(be careful to not specify a too high `zoom` level,\n", "as this can result in a large download).:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')\n", "cx.add_basemap(ax, zoom=12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choosing a different style" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, contextily uses the Stamen Terrain style. We can specify a\n", "different style using ``cx.providers``:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')\n", "cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite)\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding labels as an overlay" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, when you plot data on a basemap, the data will obscure some important map elements, such as labels,\n", "that you would otherwise want to see unobscured. Some map tile providers offer multiple sets of partially\n", "transparent tiles to solve this, and `contextily` will do its best to auto-detect these transparent layers\n", "and put them on top." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')\n", "cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite)\n", "cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By splitting the layers like this, you can also independently manipulate the level of zoom on each layer,\n", "for example to make labels larger while still showing a lot of detail." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')\n", "cx.add_basemap(ax, source=cx.providers.Stamen.Watercolor, zoom=12)\n", "cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels, zoom=10)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/plotting_with_folium.ipynb000066400000000000000000000160371413247402600245620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting with Folium\n", "\n", "__What is Folium?__\n", "\n", "[Folium](https://python-visualization.github.io/folium/) builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the leaflet.js library. This allows you to manipulate your data in Geopandas and visualize it on a Leaflet map via Folium.\n", "\n", "In this example, we will first use Geopandas to load the geometries (volcano point data), and then create the Folium map with markers representing the different types of volcanoes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load geometries\n", "This example uses a freely available [volcano dataset](https://www.kaggle.com/texasdave/volcano-eruptions). We will be reading the csv file using pandas, and then convert the pandas `DataFrame` to a Geopandas `GeoDataFrame`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import Libraries\n", "import pandas as pd\n", "import geopandas\n", "import folium\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1 = pd.read_csv('volcano_data_2010.csv')\n", "\n", "# Keep only relevant columns\n", "df = df1.loc[:, (\"Year\", \"Name\", \"Country\", \"Latitude\", \"Longitude\", \"Type\")]\n", "df.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create point geometries\n", "geometry = geopandas.points_from_xy(df.Longitude, df.Latitude)\n", "geo_df = geopandas.GeoDataFrame(df[['Year','Name','Country', 'Latitude', 'Longitude', 'Type']], geometry=geometry)\n", "\n", "geo_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", "df.Type.unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(24,18))\n", "world.plot(ax=ax, alpha=0.4, color='grey')\n", "geo_df.plot(column='Type', ax=ax, legend=True)\n", "plt.title('Volcanoes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Folium map\n", "Folium has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen. For example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Stamen Terrain\n", "map = folium.Map(location = [13.406,80.110], tiles = \"Stamen Terrain\", zoom_start = 9)\n", "map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# OpenStreetMap\n", "map = folium.Map(location = [13.406,80.110], tiles='OpenStreetMap' , zoom_start = 9)\n", "map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Stamen Toner\n", "map = folium.Map(location = [13.406,80.110], tiles='Stamen Toner', zoom_start = 9)\n", "map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example uses the Stamen Terrain map layer to visualize the volcano terrain." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Use terrain map layer to see volcano terrain\n", "map = folium.Map(location = [4,10], tiles = \"Stamen Terrain\", zoom_start = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add markers\n", "To represent the different types of volcanoes, you can create Folium markers and add them to your map." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a geometry list from the GeoDataFrame\n", "geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]\n", "\n", "# Iterate through list and add a marker for each volcano, color-coded by its type.\n", "i = 0\n", "for coordinates in geo_df_list:\n", " #assign a color marker for the type of volcano, Strato being the most common\n", " if geo_df.Type[i] == \"Stratovolcano\":\n", " type_color = \"green\"\n", " elif geo_df.Type[i] == \"Complex volcano\":\n", " type_color = \"blue\"\n", " elif geo_df.Type[i] == \"Shield volcano\":\n", " type_color = \"orange\"\n", " elif geo_df.Type[i] == \"Lava dome\":\n", " type_color = \"pink\"\n", " else:\n", " type_color = \"purple\"\n", "\n", "\n", " # Place the markers with the popup labels and data\n", " map.add_child(folium.Marker(location = coordinates,\n", " popup =\n", " \"Year: \" + str(geo_df.Year[i]) + '
' +\n", " \"Name: \" + str(geo_df.Name[i]) + '
' +\n", " \"Country: \" + str(geo_df.Country[i]) + '
'\n", " \"Type: \" + str(geo_df.Type[i]) + '
'\n", " \"Coordinates: \" + str(geo_df_list[i]),\n", " icon = folium.Icon(color = \"%s\" % type_color)))\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Folium Heatmaps\n", "\n", "Folium is well known for its heatmaps, which create a heatmap layer. To plot a heatmap in Folium, you need a list of latitudes and longitudes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This example uses heatmaps to visualize the density of volcanoes\n", "# which is more in some parts of the world compared to others.\n", "\n", "from folium import plugins\n", "\n", "map = folium.Map(location = [15,30], tiles='Cartodb dark_matter', zoom_start = 2)\n", "\n", "heat_data = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]\n", "\n", "heat_data\n", "plugins.HeatMap(heat_data).add_to(map)\n", "\n", "map" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/plotting_with_geoplot.ipynb000066400000000000000000000122431413247402600247330ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Plotting with Geoplot and GeoPandas\n", "\n", "[Geoplot](https://residentmario.github.io/geoplot/index.html) is a Python\n", "library providing a selection of easy-to-use geospatial visualizations. It is\n", "built on top of the lower-level [CartoPy](http://scitools.org.uk/cartopy/),\n", "covered in a separate section of this tutorial, and is designed to work with\n", "GeoPandas input.\n", "\n", "This example is a brief tour of the `geoplot` API. For more details on the\n", "library refer to [its documentation](https://residentmario.github.io/geoplot/index.html).\n", "\n", "First we'll load in the data using GeoPandas.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "import geoplot\n", "\n", "world = geopandas.read_file(\n", " geopandas.datasets.get_path('naturalearth_lowres')\n", ")\n", "boroughs = geopandas.read_file(\n", " geoplot.datasets.get_path('nyc_boroughs')\n", ")\n", "collisions = geopandas.read_file(\n", " geoplot.datasets.get_path('nyc_injurious_collisions')\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting with Geoplot\n", "=====================\n", "\n", "We start out by replicating the basic GeoPandas world plot using Geoplot.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "geoplot.polyplot(world, figsize=(8, 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geoplot can re-project data into any of the map projections provided by\n", "CartoPy (see the list\n", "[here](http://scitools.org.uk/cartopy/docs/latest/crs/projections.html)).\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# use the Orthographic map projection (e.g. a world globe)\n", "ax = geoplot.polyplot(\n", " world, projection=geoplot.crs.Orthographic(), figsize=(8, 4)\n", ")\n", "ax.outline_patch.set_visible(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``polyplot`` is trivial and can only plot the geometries you pass to it. If\n", "you want to use color as a visual variable, specify a ``choropleth``. Here\n", "we sort GDP per person by country into five buckets by color, using\n", "\"quantiles\" binning from the [Mapclassify](https://pysal.org/mapclassify/)\n", "library.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import mapclassify\n", "gpd_per_person = world['gdp_md_est'] / world['pop_est']\n", "scheme = mapclassify.Quantiles(gpd_per_person, k=5)\n", "\n", "# Note: this code sample requires geoplot>=0.4.0.\n", "geoplot.choropleth(\n", " world, hue=gpd_per_person, scheme=scheme,\n", " cmap='Greens', figsize=(8, 4)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to use size as a visual variable, use a ``cartogram``. Here are\n", "population estimates for countries in Africa.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "africa = world.query('continent == \"Africa\"')\n", "ax = geoplot.cartogram(\n", " africa, scale='pop_est', limits=(0.2, 1),\n", " edgecolor='None', figsize=(7, 8)\n", ")\n", "geoplot.polyplot(africa, edgecolor='gray', ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we have data in the shape of points in space, we may generate a\n", "three-dimensional heatmap on it using ``kdeplot``.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "ax = geoplot.kdeplot(\n", " collisions.head(1000), clip=boroughs.geometry,\n", " shade=True, cmap='Reds',\n", " projection=geoplot.crs.AlbersEqualArea())\n", "geoplot.polyplot(boroughs, ax=ax, zorder=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are just some of the plots you can make with Geoplot. There are\n", "many other possibilities not covered in this brief introduction. For more\n", "examples, refer to the\n", "[Gallery](https://residentmario.github.io/geoplot/gallery/index.html) in\n", "the Geoplot documentation.\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }geopandas-0.10.2/doc/source/gallery/polygon_plotting_with_folium.ipynb000066400000000000000000000126721413247402600263320ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting polygons with Folium\n", "This example demonstrates how to plot polygons on a Folium map." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import folium\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load geometries\n", "This example uses the nybb dataset, which contains polygons of New York boroughs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path = gpd.datasets.get_path('nybb')\n", "df = gpd.read_file(path)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot from the original dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [], "source": [ "df.plot(figsize=(6, 6))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the values of the polygon geometries do not directly represent the values of latitude of longitude in a geographic coordinate system.\n", "To view the coordinate reference system of the geometry column, access the `crs` attribute:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [epsg:2263](https://epsg.io/2263) crs is a projected coordinate reference system with linear units (ft in this case).\n", "As folium (i.e. leaflet.js) by default accepts values of latitude and longitude (angular units) as input, we need to project the geometry to a geographic coordinate system first." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Use WGS 84 (epsg:4326) as the geographic coordinate system\n", "df = df.to_crs(epsg=4326)\n", "print(df.crs)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.plot(figsize=(6, 6))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Folium map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m = folium.Map(location=[40.70, -73.94], zoom_start=10, tiles='CartoDB positron')\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add polygons to map\n", "Overlay the boundaries of boroughs on map with borough name as popup:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for _, r in df.iterrows():\n", " # Without simplifying the representation of each borough,\n", " # the map might not be displayed \n", " sim_geo = gpd.GeoSeries(r['geometry']).simplify(tolerance=0.001)\n", " geo_j = sim_geo.to_json()\n", " geo_j = folium.GeoJson(data=geo_j,\n", " style_function=lambda x: {'fillColor': 'orange'})\n", " folium.Popup(r['BoroName']).add_to(geo_j)\n", " geo_j.add_to(m)\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add centroid markers\n", "In order to properly compute geometric properties, in this case centroids, of the geometries, we need to project the data to a projected coordinate system." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Project to NAD83 projected crs\n", "df = df.to_crs(epsg=2263)\n", "\n", "# Access the centroid attribute of each polygon\n", "df['centroid'] = df.centroid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we're again adding a new geometry to the Folium map, we need to project the geometry back to a geographic coordinate system with latitude and longitude values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Project to WGS84 geographic crs\n", "\n", "# geometry (active) column\n", "df = df.to_crs(epsg=4326)\n", "\n", "# Centroid column\n", "df['centroid'] = df['centroid'].to_crs(epsg=4326)\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for _, r in df.iterrows():\n", " lat = r['centroid'].y\n", " lon = r['centroid'].x\n", " folium.Marker(location=[lat, lon],\n", " popup='length: {}
area: {}'.format(r['Shape_Leng'], r['Shape_Area'])).add_to(m)\n", "\n", "m" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/gallery/s2a_l2a_fishbourne.tif000066400000000000000000035464421413247402600234370ustar00rootroot00000000000000II*-RS$ , D t Rr-B@SexR"b2"4GrZBmR"b)2<OatrBR" 0CbV2i|ҎrBR%"8J]pb2һrB,?RR"ewŠb2rB!4FYlR"·b2(r;BNasR"b 20BUrhB{R" $ b7 2J ] o r B  R " + > Q bd 2w  Ҝ r B  R3 "F X k ~ b 2  r B  ' : M R` "s ˜ b 2  r B/ B T g z R " b 2 #6rIB\o$@$@#A1sUA# v )#WGS 84 / UTM zone 30N|WGS 84|D=DC5C@D@LDCDC@,D D;DC.D@~D9DvC/D@|D@6DsC@.D D@:DC@0D~D3DC/DyD@2DC3D@|D4DC0D@D1DC-DzD(DC-DsD(DC(DpD#DvC&DsD%DC,DsDDqC@:DD?DC@=D@D@@DC7D 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pure Python, but has several dependencies written in C ([GEOS](https://geos.osgeo.org), [GDAL](https://www.gdal.org/), [PROJ](https://proj.org/)). Those base C libraries can sometimes be a challenge to install. Therefore, we advise you to closely follow the recommendations below to avoid installation problems. ### Easy way The best way to install GeoPandas is using ``conda`` and ``conda-forge`` channel: ``` conda install -c conda-forge geopandas ``` ### Detailed instructions Do you prefer ``pip install`` or installation from source? Or specific version? See {doc}`detailed instructions `. ### What now? - If you don't have GeoPandas yet, check {doc}`Installation `. - If you have never used GeoPandas and want to get familiar with it and its core functionality quickly, see {doc}`Getting Started Tutorial `. - Detailed illustration how to work with different parts of GeoPandas, how to make maps, manage projections, spatially merge data or geocode are part of our {doc}`User Guide `. - And if you are interested in the complete documentation of all classes, functions, method and attributes GeoPandas offers, {doc}`API Reference ` is here for you. ```{container} button {doc}`Installation ` {doc}`Introduction ` {doc}`User Guide ` {doc}`API Reference ` ``` ## Get in touch Haven't found what you were looking for? - Ask usage questions ("How do I?") on [StackOverflow](https://stackoverflow.com/questions/tagged/geopandas) or [GIS StackExchange](https://gis.stackexchange.com/questions/tagged/geopandas). - Report bugs, suggest features or view the source code on [GitHub](https://github.com/geopandas/geopandas). - For a quick question about a bug report or feature request, or Pull Request, head over to the [gitter channel](https://gitter.im/geopandas/geopandas). - For less well defined questions or ideas, or to announce other projects of interest to GeoPandas users, ... use the [mailing list](https://groups.google.com/forum/#!forum/geopandas). geopandas-0.10.2/doc/source/getting_started/000077500000000000000000000000001413247402600207725ustar00rootroot00000000000000geopandas-0.10.2/doc/source/getting_started/install.rst000066400000000000000000000203461413247402600231770ustar00rootroot00000000000000Installation ============ GeoPandas depends for its spatial functionality on a large geospatial, open source stack of libraries (`GEOS`_, `GDAL`_, `PROJ`_). See the :ref:`dependencies` section below for more details. Those base C libraries can sometimes be a challenge to install. Therefore, we advise you to closely follow the recommendations below to avoid installation problems. .. _install-conda: Installing with Anaconda / conda -------------------------------- To install GeoPandas and all its dependencies, we recommend to use the `conda`_ package manager. This can be obtained by installing the `Anaconda Distribution`_ (a free Python distribution for data science), or through `miniconda`_ (minimal distribution only containing Python and the `conda`_ package manager). See also the `installation docs `__ for more information on how to install Anaconda or miniconda locally. The advantage of using the `conda`_ package manager is that it provides pre-built binaries for all the required and optional dependencies of GeoPandas for all platforms (Windows, Mac, Linux). To install the latest version of GeoPandas, you can then do:: conda install geopandas Using the conda-forge channel ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `conda-forge`_ is a community effort that provides conda packages for a wide range of software. It provides the *conda-forge* package channel for conda from which packages can be installed, in addition to the "*defaults*" channel provided by Anaconda. Depending on what other packages you are working with, the *defaults* channel or *conda-forge* channel may be better for your needs (e.g. some packages are available on *conda-forge* and not on *defaults*). GeoPandas and all its dependencies are available on the *conda-forge* channel, and can be installed as:: conda install --channel conda-forge geopandas .. note:: We strongly recommend to either install everything from the *defaults* channel, or everything from the *conda-forge* channel. Ending up with a mixture of packages from both channels for the dependencies of GeoPandas can lead to import problems. See the `conda-forge section on using multiple channels `__ for more details. Creating a new environment ^^^^^^^^^^^^^^^^^^^^^^^^^^ Creating a new environment is not strictly necessary, but given that installing other geospatial packages from different channels may cause dependency conflicts (as mentioned in the note above), it can be good practice to install the geospatial stack in a clean environment starting fresh. The following commands create a new environment with the name ``geo_env``, configures it to install packages always from conda-forge, and installs GeoPandas in it:: conda create -n geo_env conda activate geo_env conda config --env --add channels conda-forge conda config --env --set channel_priority strict conda install python=3 geopandas .. _install-pip: Installing with pip ------------------- GeoPandas can also be installed with pip, if all dependencies can be installed as well:: pip install geopandas .. _install-deps: .. warning:: When using pip to install GeoPandas, you need to make sure that all dependencies are installed correctly. - `fiona`_ provides binary wheels with the dependencies included for Mac and Linux, but not for Windows. - `pyproj`_, `rtree`_, and `shapely`_ provide binary wheels with dependencies included for Mac, Linux, and Windows. - Windows wheels for `shapely`, `fiona`, `pyproj` and `rtree` can be found at `Christopher Gohlke's website `_. Depending on your platform, you might need to compile and install their C dependencies manually. We refer to the individual packages for more details on installing those. Using conda (see above) avoids the need to compile the dependencies yourself. Installing from source ---------------------- You may install the latest development version by cloning the `GitHub` repository and using pip to install from the local directory:: git clone https://github.com/geopandas/geopandas.git cd geopandas pip install . It is also possible to install the latest development version directly from the GitHub repository with:: pip install git+git://github.com/geopandas/geopandas.git For installing GeoPandas from source, the same :ref:`note ` on the need to have all dependencies correctly installed applies. But, those dependencies can also be installed independently with conda before installing GeoPandas from source:: conda install pandas fiona shapely pyproj rtree See the :ref:`section on conda ` above for more details on getting running with Anaconda. .. _dependencies: Dependencies ------------ Required dependencies: - `numpy`_ - `pandas`_ (version 0.25 or later) - `shapely`_ (interface to `GEOS`_) - `fiona`_ (interface to `GDAL`_) - `pyproj`_ (interface to `PROJ`_; version 2.2.0 or later) Further, optional dependencies are: - `rtree`_ (optional; spatial index to improve performance and required for overlay operations; interface to `libspatialindex`_) - `psycopg2`_ (optional; for PostGIS connection) - `GeoAlchemy2`_ (optional; for writing to PostGIS) - `geopy`_ (optional; for geocoding) For plotting, these additional packages may be used: - `matplotlib`_ (>= 3.1.0) - `mapclassify`_ (>= 2.4.0) Using the optional PyGEOS dependency ------------------------------------ Work is ongoing to improve the performance of GeoPandas. Currently, the fast implementations of basic spatial operations live in the `PyGEOS`_ package (but work is under way to contribute those improvements to Shapely). Starting with GeoPandas 0.8, it is possible to optionally use those experimental speedups by installing PyGEOS. This can be done with conda (using the conda-forge channel) or pip:: # conda conda install pygeos --channel conda-forge # pip pip install pygeos More specifically, whether the speedups are used or not is determined by: - If PyGEOS >= 0.8 is installed, it will be used by default (but installing GeoPandas will not yet automatically install PyGEOS as dependency, you need to do this manually). - You can still toggle the use of PyGEOS when it is available, by: - Setting an environment variable (``USE_PYGEOS=0/1``). Note this variable is only checked at first import of GeoPandas. - Setting an option: ``geopandas.options.use_pygeos = True/False``. Note, although this variable can be set during an interactive session, it will only work if the GeoDataFrames you use are created (e.g. reading a file with ``read_file``) after changing this value. .. warning:: The use of PyGEOS is experimental! Although it is passing all tests, there might still be issues and not all functions of GeoPandas will already benefit from speedups (one known issue: the `to_crs` coordinate transformations lose the z coordinate). But trying this out is very welcome! Any issues you encounter (but also reports of successful usage are interesting!) can be reported at https://gitter.im/geopandas/geopandas or https://github.com/geopandas/geopandas/issues .. _PyPI: https://pypi.python.org/pypi/geopandas .. _GitHub: https://github.com/geopandas/geopandas .. _numpy: http://www.numpy.org .. _pandas: http://pandas.pydata.org .. _shapely: https://shapely.readthedocs.io .. _fiona: https://fiona.readthedocs.io .. _matplotlib: http://matplotlib.org .. _geopy: https://github.com/geopy/geopy .. _psycopg2: https://pypi.python.org/pypi/psycopg2 .. _GeoAlchemy2: https://geoalchemy-2.readthedocs.io/ .. _mapclassify: http://pysal.org/mapclassify .. _pyproj: https://github.com/pyproj4/pyproj .. _rtree: https://github.com/Toblerity/rtree .. _libspatialindex: https://github.com/libspatialindex/libspatialindex .. _conda: https://conda.io/en/latest/ .. _Anaconda distribution: https://www.anaconda.com/distribution/ .. _miniconda: https://docs.conda.io/en/latest/miniconda.html .. _conda-forge: https://conda-forge.org/ .. _GDAL: https://www.gdal.org/ .. _GEOS: https://geos.osgeo.org .. _PROJ: https://proj.org/ .. _PyGEOS: https://github.com/pygeos/pygeos/ geopandas-0.10.2/doc/source/getting_started/introduction.ipynb000066400000000000000000000426531413247402600245700ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Introduction to GeoPandas\n", "\n", "This quick tutorial introduces the key concepts and basic features of GeoPandas to help you get started with your projects.\n", "\n", "## Concepts\n", "\n", "GeoPandas, as the name suggests, extends the popular data science library [pandas](https://pandas.pydata.org) by adding support for geospatial data. If you are not familiar with `pandas`, we recommend taking a quick look at its [Getting started documentation](https://pandas.pydata.org/docs/getting_started/index.html#getting-started) before proceeding.\n", "\n", "The core data structure in GeoPandas is the `geopandas.GeoDataFrame`, a subclass of `pandas.DataFrame`, that can store geometry columns and perform spatial operations. The `geopandas.GeoSeries`, a subclass of `pandas.Series`, handles the geometries. Therefore, your `GeoDataFrame` is a combination of `pandas.Series`, with traditional data (numerical, boolean, text etc.), and `geopandas.GeoSeries`, with geometries (points, polygons etc.). You can have as many columns with geometries as you wish; there's no limit typical for desktop GIS software.\n", "\n", "![geodataframe schema](../_static/dataframe.svg)\n", "\n", "Each `GeoSeries` can contain any geometry type (you can even mix them within a single array) and has a `GeoSeries.crs` attribute, which stores information about the projection (CRS stands for Coordinate Reference System). Therefore, each `GeoSeries` in a `GeoDataFrame` can be in a different projection, allowing you to have, for example, multiple versions (different projections) of the same geometry.\n", "\n", "Only one `GeoSeries` in a `GeoDataFrame` is considered the _active_ geometry, which means that all geometric operations applied to a `GeoDataFrame` operate on this _active_ column.\n", "\n", "\n", "
\n", "User Guide\n", " \n", "See more on [data structures in the User Guide](../docs/user_guide/data_structures.rst).\n", "
\n", "\n", "\n", "Let's see how some of these concepts work in practice.\n", "\n", "## Reading and writing files\n", "\n", "First, we need to read some data.\n", "\n", "### Reading files\n", "\n", "Assuming you have a file containing both data and geometry (e.g. GeoPackage, GeoJSON, Shapefile), you can read it using `geopandas.read_file()`, which automatically detects the filetype and creates a `GeoDataFrame`. This tutorial uses the `\"nybb\"` dataset, a map of New York boroughs, which is part of the GeoPandas installation. Therefore, we use `geopandas.datasets.get_path()` to retrieve the path to the dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "\n", "path_to_data = geopandas.datasets.get_path(\"nybb\")\n", "gdf = geopandas.read_file(path_to_data)\n", "\n", "gdf" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### Writing files\n", "\n", "To write a `GeoDataFrame` back to file use `GeoDataFrame.to_file()`. The default file format is Shapefile, but you can specify your own with the `driver` keyword." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.to_file(\"my_file.geojson\", driver=\"GeoJSON\")" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "
\n", "User Guide\n", " \n", "See more on [reading and writing data in the User Guide](../docs/user_guide/io.rst).\n", "
\n", "\n", "\n", "\n", "## Simple accessors and methods\n", "\n", "Now we have our `GeoDataFrame` and can start working with its geometry. \n", "\n", "Since there was only one geometry column in the New York Boroughs dataset, this column automatically becomes the _active_ geometry and spatial methods used on the `GeoDataFrame` will be applied to the `\"geometry\"` column.\n", "\n", "### Measuring area\n", "\n", "To measure the area of each polygon (or MultiPolygon in this specific case), access the `GeoDataFrame.area` attribute, which returns a `pandas.Series`. Note that `GeoDataFrame.area` is just `GeoSeries.area` applied to the _active_ geometry column.\n", "\n", "But first, to make the results easier to read, set the names of the boroughs as the index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_index(\"BoroName\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"area\"] = gdf.area\n", "gdf[\"area\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting polygon boundary and centroid\n", "\n", "To get the boundary of each polygon (LineString), access the `GeoDataFrame.boundary`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf['boundary'] = gdf.boundary\n", "gdf['boundary']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have saved boundary as a new column, we now have two geometry columns in the same `GeoDataFrame`.\n", "\n", "We can also create new geometries, which could be, for example, a buffered version of the original one (i.e., `GeoDataFrame.buffer(10)`) or its centroid:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf['centroid'] = gdf.centroid\n", "gdf['centroid']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measuring distance\n", "\n", "We can also measure how far each centroid is from the first centroid location." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "first_point = gdf['centroid'].iloc[0]\n", "gdf['distance'] = gdf['centroid'].distance(first_point)\n", "gdf['distance']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `geopandas.GeoDataFrame` is a subclass of `pandas.DataFrame`, so we have all the pandas functionality available to use on the geospatial dataset — we can even perform data manipulations with the attributes and geometry information together.\n", "\n", "For example, to calculate the average of the distances measured above, access the 'distance' column and call the mean() method on it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf['distance'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making maps\n", "\n", "GeoPandas can also plot maps, so we can check how the geometries appear in space. To plot the active geometry, call `GeoDataFrame.plot()`. To color code by another column, pass in that column as the first argument. In the example below, we plot the active geometry column and color code by the `\"area\"` column. We also want to show a legend (`legend=True`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.plot(\"area\", legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also explore your data interactively using `GeoDataFrame.explore()`, which behaves in the same way `plot()` does but returns an interactive map instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.explore(\"area\", legend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Switching the active geometry (`GeoDataFrame.set_geometry`) to centroids, we can plot the same data using point geometry." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"centroid\")\n", "gdf.plot(\"area\", legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can also layer both `GeoSeries` on top of each other. We just need to use one plot as an axis for the other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = gdf[\"geometry\"].plot()\n", "gdf[\"centroid\"].plot(ax=ax, color=\"black\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we set the active geometry back to the original `GeoSeries`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"geometry\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "User Guide\n", " \n", "See more on [mapping in the User Guide](../docs/user_guide/mapping.rst).\n", "
\n", "\n", "## Geometry creation\n", "\n", "We can further work with the geometry and create new shapes based on those we already have. \n", "\n", "### Convex hull\n", "\n", "If we are interested in the convex hull of our polygons, we can access `GeoDataFrame.convex_hull`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"convex_hull\"] = gdf.convex_hull" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = gdf[\"convex_hull\"].plot(alpha=.5) # saving the first plot as an axis and setting alpha (transparency) to 0.5\n", "gdf[\"boundary\"].plot(ax=ax, color=\"white\", linewidth=.5) # passing the first plot and setting linewitdth to 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Buffer\n", "\n", "In other cases, we may need to buffer the geometry using `GeoDataFrame.buffer()`. Geometry methods are automatically applied to the active geometry, but we can apply them directly to any `GeoSeries` as well. Let's buffer the boroughs and their centroids and plot both on top of each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# buffering the active geometry by 10 000 feet (geometry is already in feet)\n", "gdf[\"buffered\"] = gdf.buffer(10000)\n", "\n", "# buffering the centroid geometry by 10 000 feet (geometry is already in feet)\n", "gdf[\"buffered_centroid\"] = gdf[\"centroid\"].buffer(10000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = gdf[\"buffered\"].plot(alpha=.5) # saving the first plot as an axis and setting alpha (transparency) to 0.5\n", "gdf[\"buffered_centroid\"].plot(ax=ax, color=\"red\", alpha=.5) # passing the first plot as an axis to the second\n", "gdf[\"boundary\"].plot(ax=ax, color=\"white\", linewidth=.5) # passing the first plot and setting linewitdth to 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "User Guide\n", " \n", "See more on [geometry creation and manipulation in the User Guide](../docs/user_guide/geometric_manipulations.rst).\n", "
\n", "\n", "## Geometry relations\n", "\n", "We can also ask about the spatial relations of different geometries. Using the geometries above, we can check which of the buffered boroughs intersect the original geometry of Brooklyn, i.e., is within 10 000 feet from Brooklyn.\n", "\n", "First, we get a polygon of Brooklyn." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "brooklyn = gdf.loc[\"Brooklyn\", \"geometry\"]\n", "brooklyn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The polygon is a [shapely geometry object](https://shapely.readthedocs.io/en/stable/manual.html#geometric-objects), as any other geometry used in GeoPandas." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(brooklyn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can check which of the geometries in `gdf[\"buffered\"]` intersects it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"buffered\"].intersects(brooklyn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only Bronx (on the north) is more than 10 000 feet away from Brooklyn. All the others are closer and intersect our polygon.\n", "\n", "Alternatively, we can check which buffered centroids are entirely within the original boroughs polygons. In this case, both `GeoSeries` are aligned, and the check is performed for each row." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf[\"within\"] = gdf[\"buffered_centroid\"].within(gdf)\n", "gdf[\"within\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the results on the map to confirm the finding." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"buffered_centroid\")\n", "ax = gdf.plot(\"within\", legend=True, categorical=True, legend_kwds={'loc': \"upper left\"}) # using categorical plot and setting the position of the legend\n", "gdf[\"boundary\"].plot(ax=ax, color=\"black\", linewidth=.5) # passing the first plot and setting linewitdth to 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Projections\n", "\n", "Each `GeoSeries` has its Coordinate Reference System (CRS) accessible at `GeoSeries.crs`. The CRS tells GeoPandas where the coordinates of the geometries are located on the earth's surface. In some cases, the CRS is geographic, which means that the coordinates are in latitude and longitude. In those cases, its CRS is WGS84, with the authority code `EPSG:4326`. Let's see the projection of our NY boroughs `GeoDataFrame`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geometries are in `EPSG:2263` with coordinates in feet. We can easily re-project a `GeoSeries` to another CRS, like `EPSG:4326` using `GeoSeries.to_crs()`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_geometry(\"geometry\")\n", "boroughs_4326 = gdf.to_crs(\"EPSG:4326\")\n", "boroughs_4326.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "boroughs_4326.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the difference in coordinates along the axes of the plot. Where we had 120 000 - 280 000 (feet) before, we now have 40.5 - 40.9 (degrees). In this case, `boroughs_4326` has a `\"geometry\"` column in WGS84 but all the other (with centroids etc.) remain in the original CRS.\n", "\n", "
\n", "Warning\n", " \n", "For operations that rely on distance or area, you always need to use a projected CRS (in meters, feet, kilometers etc.) not a geographic one (in degrees). GeoPandas operations are planar, whereas degrees reflect the position on a sphere. Therefore, spatial operations using degrees may not yield correct results. For example, the result of `gdf.area.sum()` (projected CRS) is 8 429 911 572 ft2 but the result of `boroughs_4326.area.sum()` (geographic CRS) is 0.083.\n", "
\n", "\n", "
\n", "User Guide\n", " \n", "See more on [projections in the User Guide](../docs/user_guide/projections.rst).\n", "
\n", "\n", "## What next?\n", "\n", "With GeoPandas we can do much more than what has been introduced so far, from [aggregations](../docs/user_guide/aggregation_with_dissolve.rst), to [spatial joins](../docs/user_guide/mergingdata.rst), to [geocoding](../docs/user_guide/geocoding.rst), and [much more](../gallery/index.rst).\n", "\n", "Head over to the [User Guide](../docs/user_guide.rst) to learn more about the different features of GeoPandas, the [Examples](../gallery/index.rst) to see how they can be used, or to the [API reference](../docs/reference.rst) for the details." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 } geopandas-0.10.2/doc/source/index.rst000066400000000000000000000034011413247402600174420ustar00rootroot00000000000000GeoPandas |version| =================== GeoPandas is an open source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by `pandas`_ to allow spatial operations on geometric types. Geometric operations are performed by `shapely`_. Geopandas further depends on `fiona`_ for file access and `matplotlib`_ for plotting. .. _pandas: http://pandas.pydata.org .. _shapely: https://shapely.readthedocs.io .. _fiona: https://fiona.readthedocs.io .. _matplotlib: http://matplotlib.org Description ----------- The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. .. toctree:: :hidden: Home About Getting started Documentation Community .. container:: button :doc:`Getting started ` :doc:`Documentation ` :doc:`About GeoPandas ` :doc:`Community ` Useful links ------------ `Binary Installers (PyPI) `_ | `Source Repository (GitHub) `_ | `Issues & Ideas `_ | `Q&A Support `_ Supported by ------------ .. image:: https://numfocus.org/wp-content/uploads/2017/07/NumFocus_LRG.png :alt: numfocus :width: 400 :target: https://numfocus.org Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search` geopandas-0.10.2/environment-dev.yml000066400000000000000000000007221413247402600174020ustar00rootroot00000000000000name: geopandas-dev channels: - conda-forge dependencies: # required - fiona>=1.8 - pandas>=0.25 - pyproj>=2.2.0 - shapely>=1.6 # geodatabase access - psycopg2>=2.5.1 - SQLAlchemy>=0.8.3 # geocoding - geopy # plotting - matplotlib>=2.2 - mapclassify # testing - pytest>=3.1.0 - pytest-cov - codecov # spatial access methods - rtree>=0.8 # styling - black - pre-commit geopandas-0.10.2/environment.yml000066400000000000000000000003741413247402600166310ustar00rootroot00000000000000name: geopandas channels: - conda-forge dependencies: - geopandas - shapely - fiona - pyproj - pygeos - rtree - mapclassify - libpysal - matplotlib - geopy - cartopy - pyepsg - contextily - rasterio - geoplot - folium geopandas-0.10.2/examples/000077500000000000000000000000001413247402600153545ustar00rootroot00000000000000geopandas-0.10.2/examples/README.md000066400000000000000000000004011413247402600166260ustar00rootroot00000000000000# Examples Gallery Examples are available in the [documentation](https://geopandas.readthedocs.io/en/latest/gallery/index.html). Source Jupyter notebooks are in [`doc/source/gallery`](https://github.com/geopandas/geopandas/tree/master/doc/source/gallery). geopandas-0.10.2/geopandas/000077500000000000000000000000001413247402600154775ustar00rootroot00000000000000geopandas-0.10.2/geopandas/__init__.py000066400000000000000000000017301413247402600176110ustar00rootroot00000000000000from geopandas._config import options # noqa from geopandas.geoseries import GeoSeries # noqa from geopandas.geodataframe import GeoDataFrame # noqa from geopandas.array import points_from_xy # noqa from geopandas.io.file import _read_file as read_file # noqa from geopandas.io.arrow import _read_parquet as read_parquet # noqa from geopandas.io.arrow import _read_feather as read_feather # noqa from geopandas.io.sql import _read_postgis as read_postgis # noqa from geopandas.tools import sjoin, sjoin_nearest # noqa from geopandas.tools import overlay # noqa from geopandas.tools._show_versions import show_versions # noqa from geopandas.tools import clip # noqa import geopandas.datasets # noqa # make the interactive namespace easier to use # for `from geopandas import *` demos. import geopandas as gpd # noqa import pandas as pd # noqa import numpy as np # noqa from ._version import get_versions __version__ = get_versions()["version"] del get_versions geopandas-0.10.2/geopandas/_compat.py000066400000000000000000000154351413247402600175030ustar00rootroot00000000000000import contextlib from distutils.version import LooseVersion import importlib import os import warnings import numpy as np import pandas as pd import pyproj import shapely import shapely.geos # ----------------------------------------------------------------------------- # pandas compat # ----------------------------------------------------------------------------- PANDAS_GE_10 = str(pd.__version__) >= LooseVersion("1.0.0") PANDAS_GE_11 = str(pd.__version__) >= LooseVersion("1.1.0") PANDAS_GE_115 = str(pd.__version__) >= LooseVersion("1.1.5") PANDAS_GE_12 = str(pd.__version__) >= LooseVersion("1.2.0") # ----------------------------------------------------------------------------- # Shapely / PyGEOS compat # ----------------------------------------------------------------------------- SHAPELY_GE_17 = str(shapely.__version__) >= LooseVersion("1.7.0") SHAPELY_GE_18 = str(shapely.__version__) >= LooseVersion("1.8") SHAPELY_GE_20 = str(shapely.__version__) >= LooseVersion("2.0") GEOS_GE_390 = shapely.geos.geos_version >= (3, 9, 0) HAS_PYGEOS = None USE_PYGEOS = None PYGEOS_SHAPELY_COMPAT = None PYGEOS_GE_09 = None PYGEOS_GE_010 = None INSTALL_PYGEOS_ERROR = "To use PyGEOS within GeoPandas, you need to install PyGEOS: \ 'conda install pygeos' or 'pip install pygeos'" try: import pygeos # noqa # only automatically use pygeos if version is high enough if str(pygeos.__version__) >= LooseVersion("0.8"): HAS_PYGEOS = True PYGEOS_GE_09 = str(pygeos.__version__) >= LooseVersion("0.9") PYGEOS_GE_010 = str(pygeos.__version__) >= LooseVersion("0.10") else: warnings.warn( "The installed version of PyGEOS is too old ({0} installed, 0.8 required)," " and thus GeoPandas will not use PyGEOS.".format(pygeos.__version__), UserWarning, ) HAS_PYGEOS = False except ImportError: HAS_PYGEOS = False def set_use_pygeos(val=None): """ Set the global configuration on whether to use PyGEOS or not. The default is use PyGEOS if it is installed. This can be overridden with an environment variable USE_PYGEOS (this is only checked at first import, cannot be changed during interactive session). Alternatively, pass a value here to force a True/False value. """ global USE_PYGEOS global PYGEOS_SHAPELY_COMPAT if val is not None: USE_PYGEOS = bool(val) else: if USE_PYGEOS is None: USE_PYGEOS = HAS_PYGEOS env_use_pygeos = os.getenv("USE_PYGEOS", None) if env_use_pygeos is not None: USE_PYGEOS = bool(int(env_use_pygeos)) # validate the pygeos version if USE_PYGEOS: try: import pygeos # noqa # validate the pygeos version if not str(pygeos.__version__) >= LooseVersion("0.8"): raise ImportError( "PyGEOS >= 0.8 is required, version {0} is installed".format( pygeos.__version__ ) ) # Check whether Shapely and PyGEOS use the same GEOS version. # Based on PyGEOS from_shapely implementation. from shapely.geos import geos_version_string as shapely_geos_version from pygeos import geos_capi_version_string # shapely has something like: "3.6.2-CAPI-1.10.2 4d2925d6" # pygeos has something like: "3.6.2-CAPI-1.10.2" if not shapely_geos_version.startswith(geos_capi_version_string): warnings.warn( "The Shapely GEOS version ({}) is incompatible with the GEOS " "version PyGEOS was compiled with ({}). Conversions between both " "will be slow.".format( shapely_geos_version, geos_capi_version_string ) ) PYGEOS_SHAPELY_COMPAT = False else: PYGEOS_SHAPELY_COMPAT = True except ImportError: raise ImportError(INSTALL_PYGEOS_ERROR) set_use_pygeos() # compat related to deprecation warnings introduced in Shapely 1.8 # -> creating a numpy array from a list-like of Multi-part geometries, # although doing the correct thing (not expanding in its parts), still raises # the warning about iteration being deprecated # This adds a context manager to explicitly ignore this warning try: from shapely.errors import ShapelyDeprecationWarning as shapely_warning except ImportError: shapely_warning = None if shapely_warning is not None and not SHAPELY_GE_20: @contextlib.contextmanager def ignore_shapely2_warnings(): with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "Iteration|The array interface|__len__", shapely_warning ) yield elif (str(np.__version__) >= LooseVersion("1.21")) and not SHAPELY_GE_20: @contextlib.contextmanager def ignore_shapely2_warnings(): with warnings.catch_warnings(): # warning from numpy for existing Shapely releases (this is fixed # with Shapely 1.8) warnings.filterwarnings( "ignore", "An exception was ignored while fetching", DeprecationWarning ) yield else: @contextlib.contextmanager def ignore_shapely2_warnings(): yield def import_optional_dependency(name: str, extra: str = ""): """ Import an optional dependency. Adapted from pandas.compat._optional::import_optional_dependency Raises a formatted ImportError if the module is not present. Parameters ---------- name : str The module name. extra : str Additional text to include in the ImportError message. Returns ------- module """ msg = """Missing optional dependency '{name}'. {extra} " "Use pip or conda to install {name}.""".format( name=name, extra=extra ) if not isinstance(name, str): raise ValueError( "Invalid module name: '{name}'; must be a string".format(name=name) ) try: module = importlib.import_module(name) except ImportError: raise ImportError(msg) from None return module # ----------------------------------------------------------------------------- # RTree compat # ----------------------------------------------------------------------------- HAS_RTREE = None RTREE_GE_094 = False try: import rtree # noqa HAS_RTREE = True except ImportError: HAS_RTREE = False # ----------------------------------------------------------------------------- # pyproj compat # ----------------------------------------------------------------------------- PYPROJ_LT_3 = LooseVersion(pyproj.__version__) < LooseVersion("3") PYPROJ_GE_31 = LooseVersion(pyproj.__version__) >= LooseVersion("3.1") geopandas-0.10.2/geopandas/_config.py000066400000000000000000000071101413247402600174540ustar00rootroot00000000000000""" Lightweight options machinery. Based on https://github.com/topper-123/optioneer, but simplified (don't deal with nested options, deprecated options, ..), just the attribute-style dict like holding the options and giving a nice repr. """ from collections import namedtuple import textwrap Option = namedtuple("Option", "key default_value doc validator callback") class Options(object): """Provide attribute-style access to configuration dict.""" def __init__(self, options): super().__setattr__("_options", options) # populate with default values config = {} for key, option in options.items(): config[key] = option.default_value super().__setattr__("_config", config) def __setattr__(self, key, value): # you can't set new keys if key in self._config: option = self._options[key] if option.validator: option.validator(value) self._config[key] = value if option.callback: option.callback(key, value) else: msg = "You can only set the value of existing options" raise AttributeError(msg) def __getattr__(self, key): try: return self._config[key] except KeyError: raise AttributeError("No such option") def __dir__(self): return list(self._config.keys()) def __repr__(self): cls = self.__class__.__name__ description = "" for key, option in self._options.items(): descr = u"{key}: {cur!r} [default: {default!r}]\n".format( key=key, cur=self._config[key], default=option.default_value ) description += descr if option.doc: doc_text = "\n".join(textwrap.wrap(option.doc, width=70)) else: doc_text = u"No description available." doc_text = textwrap.indent(doc_text, prefix=" ") description += doc_text + "\n" space = "\n " description = description.replace("\n", space) return "{}({}{})".format(cls, space, description) def _validate_display_precision(value): if value is not None: if not isinstance(value, int) or not (0 <= value <= 16): raise ValueError("Invalid value, needs to be an integer [0-16]") display_precision = Option( key="display_precision", default_value=None, doc=( "The precision (maximum number of decimals) of the coordinates in " "the WKT representation in the Series/DataFrame display. " "By default (None), it tries to infer and use 3 decimals for projected " "coordinates and 5 decimals for geographic coordinates." ), validator=_validate_display_precision, callback=None, ) def _validate_bool(value): if not isinstance(value, bool): raise TypeError("Expected bool value, got {0}".format(type(value))) def _default_use_pygeos(): import geopandas._compat as compat return compat.USE_PYGEOS def _callback_use_pygeos(key, value): assert key == "use_pygeos" import geopandas._compat as compat compat.set_use_pygeos(value) use_pygeos = Option( key="use_pygeos", default_value=_default_use_pygeos(), doc=( "Whether to use PyGEOS to speed up spatial operations. The default is True " "if PyGEOS is installed, and follows the USE_PYGEOS environment variable " "if set." ), validator=_validate_bool, callback=_callback_use_pygeos, ) options = Options({"display_precision": display_precision, "use_pygeos": use_pygeos}) geopandas-0.10.2/geopandas/_decorator.py000066400000000000000000000036451413247402600202020ustar00rootroot00000000000000from textwrap import dedent from typing import Callable, Union # doc decorator function ported with modifications from Pandas # https://github.com/pandas-dev/pandas/blob/master/pandas/util/_decorators.py def doc(*docstrings: Union[str, Callable], **params) -> Callable: """ A decorator take docstring templates, concatenate them and perform string substitution on it. This decorator will add a variable "_docstring_components" to the wrapped callable to keep track the original docstring template for potential usage. If it should be consider as a template, it will be saved as a string. Otherwise, it will be saved as callable, and later user __doc__ and dedent to get docstring. Parameters ---------- *docstrings : str or callable The string / docstring / docstring template to be appended in order after default docstring under callable. **params The string which would be used to format docstring template. """ def decorator(decorated: Callable) -> Callable: # collecting docstring and docstring templates docstring_components: list[Union[str, Callable]] = [] if decorated.__doc__: docstring_components.append(dedent(decorated.__doc__)) for docstring in docstrings: if hasattr(docstring, "_docstring_components"): docstring_components.extend(docstring._docstring_components) elif isinstance(docstring, str) or docstring.__doc__: docstring_components.append(docstring) # formatting templates and concatenating docstring decorated.__doc__ = "".join( component.format(**params) if isinstance(component, str) else dedent(component.__doc__ or "") for component in docstring_components ) decorated._docstring_components = docstring_components return decorated return decorator geopandas-0.10.2/geopandas/_vectorized.py000066400000000000000000000667761413247402600204140ustar00rootroot00000000000000""" Compatibility shim for the vectorized geometry operations. Uses PyGEOS if available/set, otherwise loops through Shapely geometries. """ import warnings import numpy as np import pandas as pd import shapely.geometry import shapely.geos import shapely.wkb import shapely.wkt from shapely.geometry.base import BaseGeometry from . import _compat as compat try: import pygeos except ImportError: geos = None _names = { "MISSING": None, "NAG": None, "POINT": "Point", "LINESTRING": "LineString", "LINEARRING": "LinearRing", "POLYGON": "Polygon", "MULTIPOINT": "MultiPoint", "MULTILINESTRING": "MultiLineString", "MULTIPOLYGON": "MultiPolygon", "GEOMETRYCOLLECTION": "GeometryCollection", } if compat.USE_PYGEOS: type_mapping = {p.value: _names[p.name] for p in pygeos.GeometryType} geometry_type_ids = list(type_mapping.keys()) geometry_type_values = np.array(list(type_mapping.values()), dtype=object) else: type_mapping, geometry_type_ids, geometry_type_values = None, None, None def isna(value): """ Check if scalar value is NA-like (None, np.nan or pd.NA). Custom version that only works for scalars (returning True or False), as `pd.isna` also works for array-like input returning a boolean array. """ if value is None: return True elif isinstance(value, float) and np.isnan(value): return True elif compat.PANDAS_GE_10 and value is pd.NA: return True else: return False def _pygeos_to_shapely(geom): if geom is None: return None if compat.PYGEOS_SHAPELY_COMPAT: geom = shapely.geos.lgeos.GEOSGeom_clone(geom._ptr) return shapely.geometry.base.geom_factory(geom) # fallback going through WKB if pygeos.is_empty(geom) and pygeos.get_type_id(geom) == 0: # empty point does not roundtrip through WKB return shapely.wkt.loads("POINT EMPTY") else: return shapely.wkb.loads(pygeos.to_wkb(geom)) def _shapely_to_pygeos(geom): if geom is None: return None if compat.PYGEOS_SHAPELY_COMPAT: return pygeos.from_shapely(geom) # fallback going through WKB if geom.is_empty and geom.geom_type == "Point": # empty point does not roundtrip through WKB return pygeos.from_wkt("POINT EMPTY") else: return pygeos.from_wkb(geom.wkb) def from_shapely(data): """ Convert a list or array of shapely objects to an object-dtype numpy array of validated geometry elements. """ # First try a fast path for pygeos if possible, but do this in a try-except # block because pygeos.from_shapely only handles Shapely objects, while # the rest of this function is more forgiving (also __geo_interface__). if compat.USE_PYGEOS and compat.PYGEOS_SHAPELY_COMPAT: if not isinstance(data, np.ndarray): arr = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): arr[:] = data else: arr = data try: return pygeos.from_shapely(arr) except TypeError: pass out = [] for geom in data: if compat.USE_PYGEOS and isinstance(geom, pygeos.Geometry): out.append(geom) elif isinstance(geom, BaseGeometry): if compat.USE_PYGEOS: out.append(_shapely_to_pygeos(geom)) else: out.append(geom) elif hasattr(geom, "__geo_interface__"): geom = shapely.geometry.shape(geom) if compat.USE_PYGEOS: out.append(_shapely_to_pygeos(geom)) else: out.append(geom) elif isna(geom): out.append(None) else: raise TypeError("Input must be valid geometry objects: {0}".format(geom)) if compat.USE_PYGEOS: return np.array(out, dtype=object) else: # numpy can expand geometry collections into 2D arrays, use this # two-step construction to avoid this aout = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): aout[:] = out return aout def to_shapely(data): if compat.USE_PYGEOS: out = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): out[:] = [_pygeos_to_shapely(geom) for geom in data] return out else: return data def from_wkb(data): """ Convert a list or array of WKB objects to a np.ndarray[geoms]. """ if compat.USE_PYGEOS: return pygeos.from_wkb(data) import shapely.wkb out = [] for geom in data: if not isna(geom) and len(geom): geom = shapely.wkb.loads(geom, hex=isinstance(geom, str)) else: geom = None out.append(geom) aout = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): aout[:] = out return aout def to_wkb(data, hex=False, **kwargs): if compat.USE_PYGEOS: return pygeos.to_wkb(data, hex=hex, **kwargs) else: if hex: out = [geom.wkb_hex if geom is not None else None for geom in data] else: out = [geom.wkb if geom is not None else None for geom in data] return np.array(out, dtype=object) def from_wkt(data): """ Convert a list or array of WKT objects to a np.ndarray[geoms]. """ if compat.USE_PYGEOS: return pygeos.from_wkt(data) import shapely.wkt out = [] for geom in data: if not isna(geom) and len(geom): if isinstance(geom, bytes): geom = geom.decode("utf-8") geom = shapely.wkt.loads(geom) else: geom = None out.append(geom) aout = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): aout[:] = out return aout def to_wkt(data, **kwargs): if compat.USE_PYGEOS: return pygeos.to_wkt(data, **kwargs) else: out = [geom.wkt if geom is not None else None for geom in data] return np.array(out, dtype=object) def _points_from_xy(x, y, z=None): # helper method for shapely-based function if not len(x) == len(y): raise ValueError("x and y arrays must be equal length.") if z is not None: if not len(z) == len(x): raise ValueError("z array must be same length as x and y.") geom = [shapely.geometry.Point(i, j, k) for i, j, k in zip(x, y, z)] else: geom = [shapely.geometry.Point(i, j) for i, j in zip(x, y)] return geom def points_from_xy(x, y, z=None): x = np.asarray(x, dtype="float64") y = np.asarray(y, dtype="float64") if z is not None: z = np.asarray(z, dtype="float64") if compat.USE_PYGEOS: return pygeos.points(x, y, z) else: out = _points_from_xy(x, y, z) aout = np.empty(len(x), dtype=object) with compat.ignore_shapely2_warnings(): aout[:] = out return aout # ----------------------------------------------------------------------------- # Helper methods for the vectorized operations # ----------------------------------------------------------------------------- def _binary_method(op, left, right, **kwargs): # type: (str, np.array[geoms], [np.array[geoms]/BaseGeometry]) -> array-like if isinstance(right, BaseGeometry): right = from_shapely([right])[0] return getattr(pygeos, op)(left, right, **kwargs) def _binary_geo(op, left, right): # type: (str, np.array[geoms], [np.array[geoms]/BaseGeometry]) -> np.array[geoms] """Apply geometry-valued operation Supports: - difference - symmetric_difference - intersection - union Parameters ---------- op: string right: np.array[geoms] or single shapely BaseGeoemtry """ if isinstance(right, BaseGeometry): # intersection can return empty GeometryCollections, and if the # result are only those, numpy will coerce it to empty 2D array data = np.empty(len(left), dtype=object) with compat.ignore_shapely2_warnings(): data[:] = [ getattr(s, op)(right) if s is not None and right is not None else None for s in left ] return data elif isinstance(right, np.ndarray): if len(left) != len(right): msg = "Lengths of inputs do not match. Left: {0}, Right: {1}".format( len(left), len(right) ) raise ValueError(msg) data = np.empty(len(left), dtype=object) with compat.ignore_shapely2_warnings(): data[:] = [ getattr(this_elem, op)(other_elem) if this_elem is not None and other_elem is not None else None for this_elem, other_elem in zip(left, right) ] return data else: raise TypeError("Type not known: {0} vs {1}".format(type(left), type(right))) def _binary_predicate(op, left, right, *args, **kwargs): # type: (str, np.array[geoms], np.array[geoms]/BaseGeometry, args/kwargs) # -> array[bool] """Binary operation on np.array[geoms] that returns a boolean ndarray Supports: - contains - disjoint - intersects - touches - crosses - within - overlaps - covers - covered_by - equals Parameters ---------- op: string right: np.array[geoms] or single shapely BaseGeoemtry """ # empty geometries are handled by shapely (all give False except disjoint) if isinstance(right, BaseGeometry): data = [ getattr(s, op)(right, *args, **kwargs) if s is not None else False for s in left ] return np.array(data, dtype=bool) elif isinstance(right, np.ndarray): data = [ getattr(this_elem, op)(other_elem, *args, **kwargs) if not (this_elem is None or other_elem is None) else False for this_elem, other_elem in zip(left, right) ] return np.array(data, dtype=bool) else: raise TypeError("Type not known: {0} vs {1}".format(type(left), type(right))) def _binary_op_float(op, left, right, *args, **kwargs): # type: (str, np.array[geoms], np.array[geoms]/BaseGeometry, args/kwargs) # -> array """Binary operation on np.array[geoms] that returns a ndarray""" # used for distance -> check for empty as we want to return np.nan instead 0.0 # as shapely does currently (https://github.com/Toblerity/Shapely/issues/498) if isinstance(right, BaseGeometry): data = [ getattr(s, op)(right, *args, **kwargs) if not (s is None or s.is_empty or right.is_empty) else np.nan for s in left ] return np.array(data, dtype=float) elif isinstance(right, np.ndarray): if len(left) != len(right): msg = "Lengths of inputs do not match. Left: {0}, Right: {1}".format( len(left), len(right) ) raise ValueError(msg) data = [ getattr(this_elem, op)(other_elem, *args, **kwargs) if not (this_elem is None or this_elem.is_empty) | (other_elem is None or other_elem.is_empty) else np.nan for this_elem, other_elem in zip(left, right) ] return np.array(data, dtype=float) else: raise TypeError("Type not known: {0} vs {1}".format(type(left), type(right))) def _binary_op(op, left, right, *args, **kwargs): # type: (str, np.array[geoms], np.array[geoms]/BaseGeometry, args/kwargs) # -> array """Binary operation on np.array[geoms] that returns a ndarray""" # pass empty to shapely (relate handles this correctly, project only # for linestrings and points) if op == "project": null_value = np.nan dtype = float elif op == "relate": null_value = None dtype = object else: raise AssertionError("wrong op") if isinstance(right, BaseGeometry): data = [ getattr(s, op)(right, *args, **kwargs) if s is not None else null_value for s in left ] return np.array(data, dtype=dtype) elif isinstance(right, np.ndarray): if len(left) != len(right): msg = "Lengths of inputs do not match. Left: {0}, Right: {1}".format( len(left), len(right) ) raise ValueError(msg) data = [ getattr(this_elem, op)(other_elem, *args, **kwargs) if not (this_elem is None or other_elem is None) else null_value for this_elem, other_elem in zip(left, right) ] return np.array(data, dtype=dtype) else: raise TypeError("Type not known: {0} vs {1}".format(type(left), type(right))) def _affinity_method(op, left, *args, **kwargs): # type: (str, np.array[geoms], ...) -> np.array[geoms] # not all shapely.affinity methods can handle empty geometries: # affine_transform itself works (as well as translate), but rotate, scale # and skew fail (they try to unpack the bounds). # Here: consistently returning empty geom for input empty geom left = to_shapely(left) out = [] for geom in left: if geom is None or geom.is_empty: res = geom else: res = getattr(shapely.affinity, op)(geom, *args, **kwargs) out.append(res) data = np.empty(len(left), dtype=object) with compat.ignore_shapely2_warnings(): data[:] = out return from_shapely(data) # ----------------------------------------------------------------------------- # Vectorized operations # ----------------------------------------------------------------------------- # # Unary operations that return non-geometry (bool or float) # def _unary_op(op, left, null_value=False): # type: (str, np.array[geoms], Any) -> np.array """Unary operation that returns a Series""" data = [getattr(geom, op, null_value) for geom in left] return np.array(data, dtype=np.dtype(type(null_value))) def is_valid(data): if compat.USE_PYGEOS: return pygeos.is_valid(data) else: return _unary_op("is_valid", data, null_value=False) def is_empty(data): if compat.USE_PYGEOS: return pygeos.is_empty(data) else: return _unary_op("is_empty", data, null_value=False) def is_simple(data): if compat.USE_PYGEOS: return pygeos.is_simple(data) else: return _unary_op("is_simple", data, null_value=False) def is_ring(data): if "Polygon" in geom_type(data): warnings.warn( "is_ring currently returns True for Polygons, which is not correct. " "This will be corrected to False in a future release.", FutureWarning, stacklevel=3, ) if compat.USE_PYGEOS: return pygeos.is_ring(data) | pygeos.is_ring(pygeos.get_exterior_ring(data)) else: # for polygons operates on the exterior, so can't use _unary_op() results = [] for geom in data: if geom is None: results.append(False) elif geom.type == "Polygon": results.append(geom.exterior.is_ring) elif geom.type in ["LineString", "LinearRing"]: results.append(geom.is_ring) else: results.append(False) return np.array(results, dtype=bool) def is_closed(data): if compat.USE_PYGEOS: return pygeos.is_closed(data) else: return _unary_op("is_closed", data, null_value=False) def has_z(data): if compat.USE_PYGEOS: return pygeos.has_z(data) else: return _unary_op("has_z", data, null_value=False) def geom_type(data): if compat.USE_PYGEOS: res = pygeos.get_type_id(data) return geometry_type_values[np.searchsorted(geometry_type_ids, res)] else: return _unary_op("geom_type", data, null_value=None) def area(data): if compat.USE_PYGEOS: return pygeos.area(data) else: return _unary_op("area", data, null_value=np.nan) def length(data): if compat.USE_PYGEOS: return pygeos.length(data) else: return _unary_op("length", data, null_value=np.nan) # # Unary operations that return new geometries # def _unary_geo(op, left, *args, **kwargs): # type: (str, np.array[geoms]) -> np.array[geoms] """Unary operation that returns new geometries""" # ensure 1D output, see note above data = np.empty(len(left), dtype=object) with compat.ignore_shapely2_warnings(): data[:] = [getattr(geom, op, None) for geom in left] return data def boundary(data): if compat.USE_PYGEOS: return pygeos.boundary(data) else: return _unary_geo("boundary", data) def centroid(data): if compat.USE_PYGEOS: return pygeos.centroid(data) else: return _unary_geo("centroid", data) def convex_hull(data): if compat.USE_PYGEOS: return pygeos.convex_hull(data) else: return _unary_geo("convex_hull", data) def envelope(data): if compat.USE_PYGEOS: return pygeos.envelope(data) else: return _unary_geo("envelope", data) def exterior(data): if compat.USE_PYGEOS: return pygeos.get_exterior_ring(data) else: return _unary_geo("exterior", data) def interiors(data): data = to_shapely(data) has_non_poly = False inner_rings = [] for geom in data: interior_ring_seq = getattr(geom, "interiors", None) # polygon case if interior_ring_seq is not None: inner_rings.append(list(interior_ring_seq)) # non-polygon case else: has_non_poly = True inner_rings.append(None) if has_non_poly: warnings.warn( "Only Polygon objects have interior rings. For other " "geometry types, None is returned." ) data = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): data[:] = inner_rings return data def representative_point(data): if compat.USE_PYGEOS: return pygeos.point_on_surface(data) else: # method and not a property -> can't use _unary_geo out = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): out[:] = [ geom.representative_point() if geom is not None else None for geom in data ] return out # # Binary predicates # def covers(data, other): if compat.USE_PYGEOS: return _binary_method("covers", data, other) else: return _binary_predicate("covers", data, other) def covered_by(data, other): if compat.USE_PYGEOS: return _binary_method("covered_by", data, other) else: raise NotImplementedError( "covered_by is only implemented for pygeos, not shapely" ) def contains(data, other): if compat.USE_PYGEOS: return _binary_method("contains", data, other) else: return _binary_predicate("contains", data, other) def crosses(data, other): if compat.USE_PYGEOS: return _binary_method("crosses", data, other) else: return _binary_predicate("crosses", data, other) def disjoint(data, other): if compat.USE_PYGEOS: return _binary_method("disjoint", data, other) else: return _binary_predicate("disjoint", data, other) def equals(data, other): if compat.USE_PYGEOS: return _binary_method("equals", data, other) else: return _binary_predicate("equals", data, other) def intersects(data, other): if compat.USE_PYGEOS: return _binary_method("intersects", data, other) else: return _binary_predicate("intersects", data, other) def overlaps(data, other): if compat.USE_PYGEOS: return _binary_method("overlaps", data, other) else: return _binary_predicate("overlaps", data, other) def touches(data, other): if compat.USE_PYGEOS: return _binary_method("touches", data, other) else: return _binary_predicate("touches", data, other) def within(data, other): if compat.USE_PYGEOS: return _binary_method("within", data, other) else: return _binary_predicate("within", data, other) def equals_exact(data, other, tolerance): if compat.USE_PYGEOS: return _binary_method("equals_exact", data, other, tolerance=tolerance) else: return _binary_predicate("equals_exact", data, other, tolerance=tolerance) def almost_equals(self, other, decimal): if compat.USE_PYGEOS: return self.equals_exact(other, 0.5 * 10 ** (-decimal)) else: return _binary_predicate("almost_equals", self, other, decimal=decimal) # # Binary operations that return new geometries # def difference(data, other): if compat.USE_PYGEOS: return _binary_method("difference", data, other) else: return _binary_geo("difference", data, other) def intersection(data, other): if compat.USE_PYGEOS: return _binary_method("intersection", data, other) else: return _binary_geo("intersection", data, other) def symmetric_difference(data, other): if compat.USE_PYGEOS: return _binary_method("symmetric_difference", data, other) else: return _binary_geo("symmetric_difference", data, other) def union(data, other): if compat.USE_PYGEOS: return _binary_method("union", data, other) else: return _binary_geo("union", data, other) # # Other operations # def distance(data, other): if compat.USE_PYGEOS: return _binary_method("distance", data, other) else: return _binary_op_float("distance", data, other) def buffer(data, distance, resolution=16, **kwargs): if compat.USE_PYGEOS: return pygeos.buffer(data, distance, quadsegs=resolution, **kwargs) else: out = np.empty(len(data), dtype=object) if isinstance(distance, np.ndarray): if len(distance) != len(data): raise ValueError( "Length of distance sequence does not match " "length of the GeoSeries" ) with compat.ignore_shapely2_warnings(): out[:] = [ geom.buffer(dist, resolution, **kwargs) if geom is not None else None for geom, dist in zip(data, distance) ] return out with compat.ignore_shapely2_warnings(): out[:] = [ geom.buffer(distance, resolution, **kwargs) if geom is not None else None for geom in data ] return out def interpolate(data, distance, normalized=False): if compat.USE_PYGEOS: try: return pygeos.line_interpolate_point(data, distance, normalized=normalized) except TypeError: # support for pygeos<0.9 return pygeos.line_interpolate_point(data, distance, normalize=normalized) else: out = np.empty(len(data), dtype=object) if isinstance(distance, np.ndarray): if len(distance) != len(data): raise ValueError( "Length of distance sequence does not match " "length of the GeoSeries" ) with compat.ignore_shapely2_warnings(): out[:] = [ geom.interpolate(dist, normalized=normalized) for geom, dist in zip(data, distance) ] return out with compat.ignore_shapely2_warnings(): out[:] = [ geom.interpolate(distance, normalized=normalized) for geom in data ] return out def simplify(data, tolerance, preserve_topology=True): if compat.USE_PYGEOS: # preserve_topology has different default as pygeos! return pygeos.simplify(data, tolerance, preserve_topology=preserve_topology) else: # method and not a property -> can't use _unary_geo out = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): out[:] = [ geom.simplify(tolerance, preserve_topology=preserve_topology) for geom in data ] return out def _shapely_normalize(geom): """ Small helper function for now because it is not yet available in Shapely. """ from shapely.geos import lgeos from shapely.geometry.base import geom_factory from ctypes import c_void_p, c_int lgeos._lgeos.GEOSNormalize_r.restype = c_int lgeos._lgeos.GEOSNormalize_r.argtypes = [c_void_p, c_void_p] geom_cloned = lgeos.GEOSGeom_clone(geom._geom) lgeos._lgeos.GEOSNormalize_r(lgeos.geos_handle, geom_cloned) return geom_factory(geom_cloned) def normalize(data): if compat.USE_PYGEOS: return pygeos.normalize(data) else: out = np.empty(len(data), dtype=object) with compat.ignore_shapely2_warnings(): out[:] = [ _shapely_normalize(geom) if geom is not None else None for geom in data ] return out def project(data, other, normalized=False): if compat.USE_PYGEOS: try: return pygeos.line_locate_point(data, other, normalized=normalized) except TypeError: # support for pygeos<0.9 return pygeos.line_locate_point(data, other, normalize=normalized) else: return _binary_op("project", data, other, normalized=normalized) def relate(data, other): data = to_shapely(data) if isinstance(other, np.ndarray): other = to_shapely(other) return _binary_op("relate", data, other) def unary_union(data): if compat.USE_PYGEOS: return _pygeos_to_shapely(pygeos.union_all(data)) else: data = [g for g in data if g is not None] if data: return shapely.ops.unary_union(data) else: return None # # Coordinate related properties # def get_x(data): if compat.USE_PYGEOS: return pygeos.get_x(data) else: return _unary_op("x", data, null_value=np.nan) def get_y(data): if compat.USE_PYGEOS: return pygeos.get_y(data) else: return _unary_op("y", data, null_value=np.nan) def get_z(data): if compat.USE_PYGEOS: return pygeos.get_z(data) else: data = [geom.z if geom.has_z else np.nan for geom in data] return np.array(data, dtype=np.dtype(float)) def bounds(data): if compat.USE_PYGEOS: return pygeos.bounds(data) # ensure that for empty arrays, the result has the correct shape if len(data) == 0: return np.empty((0, 4), dtype="float64") # need to explicitly check for empty (in addition to missing) geometries, # as those return an empty tuple, not resulting in a 2D array bounds = np.array( [ geom.bounds if not (geom is None or geom.is_empty) else (np.nan, np.nan, np.nan, np.nan) for geom in data ] ) return bounds # # Coordinate transformation # def transform(data, func): if compat.USE_PYGEOS: coords = pygeos.get_coordinates(data) new_coords = func(coords[:, 0], coords[:, 1]) result = pygeos.set_coordinates(data.copy(), np.array(new_coords).T) return result else: from shapely.ops import transform n = len(data) result = np.empty(n, dtype=object) for i in range(n): geom = data[i] if isna(geom): result[i] = geom else: result[i] = transform(func, geom) return result geopandas-0.10.2/geopandas/_version.py000066400000000000000000000407231413247402600177030ustar00rootroot00000000000000# This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.16 (https://github.com/warner/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = " (HEAD -> master, tag: v0.10.2)" git_full = "04d377f321972801888381356cb6259766eb63b6" keywords = {"refnames": git_refnames, "full": git_full} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "pep440" cfg.tag_prefix = "v" cfg.parentdir_prefix = "geopandas-" cfg.versionfile_source = "geopandas/_version.py" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen( [c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None), ) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) return None return stdout def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print( "guessing rootdir is '%s', but '%s' doesn't start with " "prefix '%s'" % (root, dirname, parentdir_prefix) ) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return { "version": dirname[len(parentdir_prefix) :], "full-revisionid": None, "dirty": False, "error": None, } @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r"\d", r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix) :] if verbose: print("picking %s" % r) return { "version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return { "version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", } @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %s" % root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out = run_command( GITS, [ "describe", "--tags", "--dirty", "--always", "--long", "--match", "%s*" % tag_prefix, ], cwd=root, ) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[: git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % ( full_tag, tag_prefix, ) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix) :] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%d" % pieces["distance"] else: # exception #1 rendered = "0.post.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return { "version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], } if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return { "version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, } def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for i in cfg.versionfile_source.split("/"): root = os.path.dirname(root) except NameError: return { "version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree", } try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return { "version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", } geopandas-0.10.2/geopandas/array.py000066400000000000000000001277361413247402600172070ustar00rootroot00000000000000from collections.abc import Iterable import numbers import operator import warnings import inspect import numpy as np import pandas as pd from pandas.api.extensions import ( ExtensionArray, ExtensionDtype, register_extension_dtype, ) import shapely import shapely.affinity import shapely.geometry from shapely.geometry.base import BaseGeometry import shapely.ops import shapely.wkt from pyproj import CRS, Transformer try: import pygeos except ImportError: geos = None from . import _compat as compat from . import _vectorized as vectorized from .sindex import _get_sindex_class class GeometryDtype(ExtensionDtype): type = BaseGeometry name = "geometry" na_value = np.nan @classmethod def construct_from_string(cls, string): if not isinstance(string, str): raise TypeError( "'construct_from_string' expects a string, got {}".format(type(string)) ) elif string == cls.name: return cls() else: raise TypeError( "Cannot construct a '{}' from '{}'".format(cls.__name__, string) ) @classmethod def construct_array_type(cls): return GeometryArray register_extension_dtype(GeometryDtype) def _check_crs(left, right, allow_none=False): """ Check if the projection of both arrays is the same. If allow_none is True, empty CRS is treated as the same. """ if allow_none: if not left.crs or not right.crs: return True if not left.crs == right.crs: return False return True def _crs_mismatch_warn(left, right, stacklevel=3): """ Raise a CRS mismatch warning with the information on the assigned CRS. """ if left.crs: left_srs = left.crs.to_string() left_srs = left_srs if len(left_srs) <= 50 else " ".join([left_srs[:50], "..."]) else: left_srs = None if right.crs: right_srs = right.crs.to_string() right_srs = ( right_srs if len(right_srs) <= 50 else " ".join([right_srs[:50], "..."]) ) else: right_srs = None warnings.warn( "CRS mismatch between the CRS of left geometries " "and the CRS of right geometries.\n" "Use `to_crs()` to reproject one of " "the input geometries to match the CRS of the other.\n\n" "Left CRS: {0}\n" "Right CRS: {1}\n".format(left_srs, right_srs), UserWarning, stacklevel=stacklevel, ) # ----------------------------------------------------------------------------- # Constructors / converters to other formats # ----------------------------------------------------------------------------- def _geom_to_shapely(geom): """ Convert internal representation (PyGEOS or Shapely) to external Shapely object. """ if not compat.USE_PYGEOS: return geom else: return vectorized._pygeos_to_shapely(geom) def _shapely_to_geom(geom): """ Convert external Shapely object to internal representation (PyGEOS or Shapely). """ if not compat.USE_PYGEOS: return geom else: return vectorized._shapely_to_pygeos(geom) def _is_scalar_geometry(geom): if compat.USE_PYGEOS: return isinstance(geom, (pygeos.Geometry, BaseGeometry)) else: return isinstance(geom, BaseGeometry) def from_shapely(data, crs=None): """ Convert a list or array of shapely objects to a GeometryArray. Validates the elements. Parameters ---------- data : array-like list or array of shapely objects crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. """ return GeometryArray(vectorized.from_shapely(data), crs=crs) def to_shapely(geoms): """ Convert GeometryArray to numpy object array of shapely objects. """ if not isinstance(geoms, GeometryArray): raise ValueError("'geoms' must be a GeometryArray") return vectorized.to_shapely(geoms.data) def from_wkb(data, crs=None): """ Convert a list or array of WKB objects to a GeometryArray. Parameters ---------- data : array-like list or array of WKB objects crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. """ return GeometryArray(vectorized.from_wkb(data), crs=crs) def to_wkb(geoms, hex=False, **kwargs): """ Convert GeometryArray to a numpy object array of WKB objects. """ if not isinstance(geoms, GeometryArray): raise ValueError("'geoms' must be a GeometryArray") return vectorized.to_wkb(geoms.data, hex=hex, **kwargs) def from_wkt(data, crs=None): """ Convert a list or array of WKT objects to a GeometryArray. Parameters ---------- data : array-like list or array of WKT objects crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. """ return GeometryArray(vectorized.from_wkt(data), crs=crs) def to_wkt(geoms, **kwargs): """ Convert GeometryArray to a numpy object array of WKT objects. """ if not isinstance(geoms, GeometryArray): raise ValueError("'geoms' must be a GeometryArray") return vectorized.to_wkt(geoms.data, **kwargs) def points_from_xy(x, y, z=None, crs=None): """ Generate GeometryArray of shapely Point geometries from x, y(, z) coordinates. In case of geographic coordinates, it is assumed that longitude is captured by ``x`` coordinates and latitude by ``y``. Parameters ---------- x, y, z : iterable crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. Examples -------- >>> import pandas as pd >>> df = pd.DataFrame({'x': [0, 1, 2], 'y': [0, 1, 2], 'z': [0, 1, 2]}) >>> df x y z 0 0 0 0 1 1 1 1 2 2 2 2 >>> geometry = geopandas.points_from_xy(x=[1, 0], y=[0, 1]) >>> geometry = geopandas.points_from_xy(df['x'], df['y'], df['z']) >>> gdf = geopandas.GeoDataFrame( ... df, geometry=geopandas.points_from_xy(df['x'], df['y'])) Having geographic coordinates: >>> df = pd.DataFrame({'longitude': [-140, 0, 123], 'latitude': [-65, 1, 48]}) >>> df longitude latitude 0 -140 -65 1 0 1 2 123 48 >>> geometry = geopandas.points_from_xy(df.longitude, df.latitude, crs="EPSG:4326") Returns ------- output : GeometryArray """ return GeometryArray(vectorized.points_from_xy(x, y, z), crs=crs) class GeometryArray(ExtensionArray): """ Class wrapping a numpy array of Shapely objects and holding the array-based implementations. """ _dtype = GeometryDtype() def __init__(self, data, crs=None): if isinstance(data, self.__class__): if not crs: crs = data.crs data = data.data elif not isinstance(data, np.ndarray): raise TypeError( "'data' should be array of geometry objects. Use from_shapely, " "from_wkb, from_wkt functions to construct a GeometryArray." ) elif not data.ndim == 1: raise ValueError( "'data' should be a 1-dimensional array of geometry objects." ) self.data = data self._crs = None self.crs = crs self._sindex = None @property def sindex(self): if self._sindex is None: self._sindex = _get_sindex_class()(self.data) return self._sindex @property def has_sindex(self): """Check the existence of the spatial index without generating it. Use the `.sindex` attribute on a GeoDataFrame or GeoSeries to generate a spatial index if it does not yet exist, which may take considerable time based on the underlying index implementation. Note that the underlying spatial index may not be fully initialized until the first use. See Also --------- GeoDataFrame.has_sindex Returns ------- bool `True` if the spatial index has been generated or `False` if not. """ return self._sindex is not None @property def crs(self): """ The Coordinate Reference System (CRS) represented as a ``pyproj.CRS`` object. Returns None if the CRS is not set, and to set the value it :getter: Returns a ``pyproj.CRS`` or None. When setting, the value Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. """ return self._crs @crs.setter def crs(self, value): """Sets the value of the crs""" self._crs = None if not value else CRS.from_user_input(value) def check_geographic_crs(self, stacklevel): """Check CRS and warn if the planar operation is done in a geographic CRS""" if self.crs and self.crs.is_geographic: warnings.warn( "Geometry is in a geographic CRS. Results from '{}' are likely " "incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a " "projected CRS before this operation.\n".format( inspect.stack()[1].function ), UserWarning, stacklevel=stacklevel, ) @property def dtype(self): return self._dtype def __len__(self): return self.shape[0] def __getitem__(self, idx): if isinstance(idx, numbers.Integral): return _geom_to_shapely(self.data[idx]) # array-like, slice if compat.PANDAS_GE_10: # for pandas >= 1.0, validate and convert IntegerArray/BooleanArray # to numpy array, pass-through non-array-like indexers idx = pd.api.indexers.check_array_indexer(self, idx) if isinstance(idx, (Iterable, slice)): return GeometryArray(self.data[idx], crs=self.crs) else: raise TypeError("Index type not supported", idx) def __setitem__(self, key, value): if compat.PANDAS_GE_10: # for pandas >= 1.0, validate and convert IntegerArray/BooleanArray # keys to numpy array, pass-through non-array-like indexers key = pd.api.indexers.check_array_indexer(self, key) if isinstance(value, pd.Series): value = value.values if isinstance(value, (list, np.ndarray)): value = from_shapely(value) if isinstance(value, GeometryArray): if isinstance(key, numbers.Integral): raise ValueError("cannot set a single element with an array") self.data[key] = value.data elif isinstance(value, BaseGeometry) or vectorized.isna(value): if vectorized.isna(value): # internally only use None as missing value indicator # but accept others value = None elif isinstance(value, BaseGeometry): value = from_shapely([value]).data[0] else: raise TypeError("should be valid geometry") if isinstance(key, (slice, list, np.ndarray)): value_array = np.empty(1, dtype=object) with compat.ignore_shapely2_warnings(): value_array[:] = [value] self.data[key] = value_array else: self.data[key] = value else: raise TypeError( "Value should be either a BaseGeometry or None, got %s" % str(value) ) # invalidate spatial index self._sindex = None # TODO: use this once pandas-dev/pandas#33457 is fixed # if hasattr(value, "crs"): # if value.crs and (value.crs != self.crs): # raise ValueError( # "CRS mismatch between CRS of the passed geometries " # "and CRS of existing geometries." # ) def __getstate__(self): if compat.USE_PYGEOS: return (pygeos.to_wkb(self.data), self._crs) else: return self.__dict__ def __setstate__(self, state): if compat.USE_PYGEOS: geoms = pygeos.from_wkb(state[0]) self._crs = state[1] self._sindex = None # pygeos.STRtree could not be pickled yet self.data = geoms self.base = None else: if "_crs" not in state: state["_crs"] = None self.__dict__.update(state) # ------------------------------------------------------------------------- # Geometry related methods # ------------------------------------------------------------------------- @property def is_valid(self): return vectorized.is_valid(self.data) @property def is_empty(self): return vectorized.is_empty(self.data) @property def is_simple(self): return vectorized.is_simple(self.data) @property def is_ring(self): return vectorized.is_ring(self.data) @property def is_closed(self): return vectorized.is_closed(self.data) @property def has_z(self): return vectorized.has_z(self.data) @property def geom_type(self): return vectorized.geom_type(self.data) @property def area(self): self.check_geographic_crs(stacklevel=5) return vectorized.area(self.data) @property def length(self): self.check_geographic_crs(stacklevel=5) return vectorized.length(self.data) # # Unary operations that return new geometries # @property def boundary(self): return GeometryArray(vectorized.boundary(self.data), crs=self.crs) @property def centroid(self): self.check_geographic_crs(stacklevel=5) return GeometryArray(vectorized.centroid(self.data), crs=self.crs) @property def convex_hull(self): return GeometryArray(vectorized.convex_hull(self.data), crs=self.crs) @property def envelope(self): return GeometryArray(vectorized.envelope(self.data), crs=self.crs) @property def exterior(self): return GeometryArray(vectorized.exterior(self.data), crs=self.crs) @property def interiors(self): # no GeometryArray as result return vectorized.interiors(self.data) def representative_point(self): return GeometryArray(vectorized.representative_point(self.data), crs=self.crs) # # Binary predicates # @staticmethod def _binary_method(op, left, right, **kwargs): if isinstance(right, GeometryArray): if len(left) != len(right): msg = "Lengths of inputs do not match. Left: {0}, Right: {1}".format( len(left), len(right) ) raise ValueError(msg) if not _check_crs(left, right): _crs_mismatch_warn(left, right, stacklevel=7) right = right.data return getattr(vectorized, op)(left.data, right, **kwargs) def covers(self, other): return self._binary_method("covers", self, other) def covered_by(self, other): return self._binary_method("covered_by", self, other) def contains(self, other): return self._binary_method("contains", self, other) def crosses(self, other): return self._binary_method("crosses", self, other) def disjoint(self, other): return self._binary_method("disjoint", self, other) def geom_equals(self, other): return self._binary_method("equals", self, other) def intersects(self, other): return self._binary_method("intersects", self, other) def overlaps(self, other): return self._binary_method("overlaps", self, other) def touches(self, other): return self._binary_method("touches", self, other) def within(self, other): return self._binary_method("within", self, other) def geom_equals_exact(self, other, tolerance): return self._binary_method("equals_exact", self, other, tolerance=tolerance) def geom_almost_equals(self, other, decimal): return self.geom_equals_exact(other, 0.5 * 10 ** (-decimal)) # return _binary_predicate("almost_equals", self, other, decimal=decimal) def equals_exact(self, other, tolerance): warnings.warn( "GeometryArray.equals_exact() is now GeometryArray.geom_equals_exact(). " "GeometryArray.equals_exact() will be deprecated in the future.", FutureWarning, stacklevel=2, ) return self._binary_method("equals_exact", self, other, tolerance=tolerance) def almost_equals(self, other, decimal): warnings.warn( "GeometryArray.almost_equals() is now GeometryArray.geom_almost_equals(). " "GeometryArray.almost_equals() will be deprecated in the future.", FutureWarning, stacklevel=2, ) return self.geom_equals_exact(other, 0.5 * 10 ** (-decimal)) # # Binary operations that return new geometries # def difference(self, other): return GeometryArray( self._binary_method("difference", self, other), crs=self.crs ) def intersection(self, other): return GeometryArray( self._binary_method("intersection", self, other), crs=self.crs ) def symmetric_difference(self, other): return GeometryArray( self._binary_method("symmetric_difference", self, other), crs=self.crs ) def union(self, other): return GeometryArray(self._binary_method("union", self, other), crs=self.crs) # # Other operations # def distance(self, other): self.check_geographic_crs(stacklevel=6) return self._binary_method("distance", self, other) def buffer(self, distance, resolution=16, **kwargs): if not (isinstance(distance, (int, float)) and distance == 0): self.check_geographic_crs(stacklevel=5) return GeometryArray( vectorized.buffer(self.data, distance, resolution=resolution, **kwargs), crs=self.crs, ) def interpolate(self, distance, normalized=False): self.check_geographic_crs(stacklevel=5) return GeometryArray( vectorized.interpolate(self.data, distance, normalized=normalized), crs=self.crs, ) def simplify(self, tolerance, preserve_topology=True): return GeometryArray( vectorized.simplify( self.data, tolerance, preserve_topology=preserve_topology ), crs=self.crs, ) def project(self, other, normalized=False): if isinstance(other, BaseGeometry): other = _shapely_to_geom(other) elif isinstance(other, GeometryArray): other = other.data return vectorized.project(self.data, other, normalized=normalized) def relate(self, other): if isinstance(other, GeometryArray): other = other.data return vectorized.relate(self.data, other) # # Reduction operations that return a Shapely geometry # def unary_union(self): return vectorized.unary_union(self.data) # # Affinity operations # def affine_transform(self, matrix): return GeometryArray( vectorized._affinity_method("affine_transform", self.data, matrix), crs=self.crs, ) def translate(self, xoff=0.0, yoff=0.0, zoff=0.0): return GeometryArray( vectorized._affinity_method("translate", self.data, xoff, yoff, zoff), crs=self.crs, ) def rotate(self, angle, origin="center", use_radians=False): return GeometryArray( vectorized._affinity_method( "rotate", self.data, angle, origin=origin, use_radians=use_radians ), crs=self.crs, ) def scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin="center"): return GeometryArray( vectorized._affinity_method( "scale", self.data, xfact, yfact, zfact, origin=origin ), crs=self.crs, ) def skew(self, xs=0.0, ys=0.0, origin="center", use_radians=False): return GeometryArray( vectorized._affinity_method( "skew", self.data, xs, ys, origin=origin, use_radians=use_radians ), crs=self.crs, ) def to_crs(self, crs=None, epsg=None): """Returns a ``GeometryArray`` with all geometries transformed to a new coordinate reference system. Transform all geometries in a GeometryArray to a different coordinate reference system. The ``crs`` attribute on the current GeometryArray must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. Returns ------- GeometryArray Examples -------- >>> from shapely.geometry import Point >>> from geopandas.array import from_shapely, to_wkt >>> a = from_shapely([Point(1, 1), Point(2, 2), Point(3, 3)], crs=4326) >>> to_wkt(a) array(['POINT (1 1)', 'POINT (2 2)', 'POINT (3 3)'], dtype=object) >>> a.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> a = a.to_crs(3857) >>> to_wkt(a) array(['POINT (111319 111325)', 'POINT (222639 222684)', 'POINT (333958 334111)'], dtype=object) >>> a.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ if self.crs is None: raise ValueError( "Cannot transform naive geometries. " "Please set a crs on the object first." ) if crs is not None: crs = CRS.from_user_input(crs) elif epsg is not None: crs = CRS.from_epsg(epsg) else: raise ValueError("Must pass either crs or epsg.") # skip if the input CRS and output CRS are the exact same if self.crs.is_exact_same(crs): return self transformer = Transformer.from_crs(self.crs, crs, always_xy=True) new_data = vectorized.transform(self.data, transformer.transform) return GeometryArray(new_data, crs=crs) def estimate_utm_crs(self, datum_name="WGS 84"): """Returns the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 .. note:: Requires pyproj 3+ Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> world = geopandas.read_file( ... geopandas.datasets.get_path("naturalearth_lowres") ... ) >>> germany = world.loc[world.name == "Germany"] >>> germany.geometry.values.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 32N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: World - N hemisphere - 6°E to 12°E - by country - bounds: (6.0, 0.0, 12.0, 84.0) Coordinate Operation: - name: UTM zone 32N - method: Transverse Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ try: from pyproj.aoi import AreaOfInterest from pyproj.database import query_utm_crs_info except ImportError: raise RuntimeError("pyproj 3+ required for estimate_utm_crs.") if not self.crs: raise RuntimeError("crs must be set to estimate UTM CRS.") minx, miny, maxx, maxy = self.total_bounds if self.crs.is_geographic: x_center = np.mean([minx, maxx]) y_center = np.mean([miny, maxy]) # ensure using geographic coordinates else: transformer = Transformer.from_crs(self.crs, "EPSG:4326", always_xy=True) if compat.PYPROJ_GE_31: minx, miny, maxx, maxy = transformer.transform_bounds( minx, miny, maxx, maxy ) y_center = np.mean([miny, maxy]) # crossed the antimeridian if minx > maxx: # shift maxx from [-180,180] to [0,360] # so both numbers are positive for center calculation # Example: -175 to 185 maxx += 360 x_center = np.mean([minx, maxx]) # shift back to [-180,180] x_center = ((x_center + 180) % 360) - 180 else: x_center = np.mean([minx, maxx]) else: lon, lat = transformer.transform( (minx, maxx, minx, maxx), (miny, miny, maxy, maxy) ) x_center = np.mean(lon) y_center = np.mean(lat) utm_crs_list = query_utm_crs_info( datum_name=datum_name, area_of_interest=AreaOfInterest( west_lon_degree=x_center, south_lat_degree=y_center, east_lon_degree=x_center, north_lat_degree=y_center, ), ) try: return CRS.from_epsg(utm_crs_list[0].code) except IndexError: raise RuntimeError("Unable to determine UTM CRS") # # Coordinate related properties # @property def x(self): """Return the x location of point geometries in a GeoSeries""" if (self.geom_type[~self.isna()] == "Point").all(): return vectorized.get_x(self.data) else: message = "x attribute access only provided for Point geometries" raise ValueError(message) @property def y(self): """Return the y location of point geometries in a GeoSeries""" if (self.geom_type[~self.isna()] == "Point").all(): return vectorized.get_y(self.data) else: message = "y attribute access only provided for Point geometries" raise ValueError(message) @property def z(self): """Return the z location of point geometries in a GeoSeries""" if (self.geom_type[~self.isna()] == "Point").all(): return vectorized.get_z(self.data) else: message = "z attribute access only provided for Point geometries" raise ValueError(message) @property def bounds(self): return vectorized.bounds(self.data) @property def total_bounds(self): if len(self) == 0: # numpy 'min' cannot handle empty arrays # TODO with numpy >= 1.15, the 'initial' argument can be used return np.array([np.nan, np.nan, np.nan, np.nan]) b = self.bounds return np.array( ( np.nanmin(b[:, 0]), # minx np.nanmin(b[:, 1]), # miny np.nanmax(b[:, 2]), # maxx np.nanmax(b[:, 3]), # maxy ) ) # ------------------------------------------------------------------------- # general array like compat # ------------------------------------------------------------------------- @property def size(self): return self.data.size @property def shape(self): return (self.size,) @property def ndim(self): return len(self.shape) def copy(self, *args, **kwargs): # still taking args/kwargs for compat with pandas 0.24 return GeometryArray(self.data.copy(), crs=self._crs) def take(self, indices, allow_fill=False, fill_value=None): from pandas.api.extensions import take if allow_fill: if fill_value is None or pd.isna(fill_value): fill_value = None elif isinstance(fill_value, BaseGeometry): fill_value = _shapely_to_geom(fill_value) elif not _is_scalar_geometry(fill_value): raise TypeError("provide geometry or None as fill value") result = take(self.data, indices, allow_fill=allow_fill, fill_value=fill_value) if allow_fill and fill_value is None: result[pd.isna(result)] = None return GeometryArray(result, crs=self.crs) def _fill(self, idx, value): """Fill index locations with value Value should be a BaseGeometry """ if not (_is_scalar_geometry(value) or value is None): raise TypeError( "Value should be either a BaseGeometry or None, got %s" % str(value) ) # self.data[idx] = value value_arr = np.empty(1, dtype=object) with compat.ignore_shapely2_warnings(): value_arr[:] = [value] self.data[idx] = value_arr return self def fillna(self, value=None, method=None, limit=None): """Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, array-like If a scalar value is passed it is used to fill all missing values. Alternatively, an array-like 'value' can be given. It's expected that the array-like have the same length as 'self'. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation to fill gap limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Returns ------- filled : ExtensionArray with NA/NaN filled """ if method is not None: raise NotImplementedError("fillna with a method is not yet supported") mask = self.isna() new_values = self.copy() if mask.any(): # fill with value if vectorized.isna(value): value = None elif not isinstance(value, BaseGeometry): raise NotImplementedError( "fillna currently only supports filling with a scalar geometry" ) value = _shapely_to_geom(value) new_values = new_values._fill(mask, value) return new_values def astype(self, dtype, copy=True): """ Cast to a NumPy array with 'dtype'. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. copy : bool, default True Whether to copy the data, even if not necessary. If False, a copy is made only if the old dtype does not match the new dtype. Returns ------- array : ndarray NumPy ndarray with 'dtype' for its dtype. """ if isinstance(dtype, GeometryDtype): if copy: return self.copy() else: return self elif pd.api.types.is_string_dtype(dtype) and not pd.api.types.is_object_dtype( dtype ): string_values = to_wkt(self) if compat.PANDAS_GE_10: pd_dtype = pd.api.types.pandas_dtype(dtype) if isinstance(pd_dtype, pd.StringDtype): # ensure to return a pandas string array instead of numpy array return pd.array(string_values, dtype=pd_dtype) return string_values.astype(dtype, copy=False) else: return np.array(self, dtype=dtype, copy=copy) def isna(self): """ Boolean NumPy array indicating if each value is missing """ if compat.USE_PYGEOS: return pygeos.is_missing(self.data) else: return np.array([g is None for g in self.data], dtype="bool") def value_counts( self, dropna: bool = True, ): """ Compute a histogram of the counts of non-null values. Parameters ---------- dropna : bool, default True Don't include counts of NaN Returns ------- pd.Series """ # note ExtensionArray usage of value_counts only specifies dropna, # so sort, normalize and bins are not arguments values = to_wkb(self) from pandas import Series, Index result = Series(values).value_counts(dropna=dropna) # value_counts converts None to nan, need to convert back for from_wkb to work # note result.index already has object dtype, not geometry # Can't use fillna(None) or Index.putmask, as this gets converted back to nan # for object dtypes result.index = Index( from_wkb(np.where(result.index.isna(), None, result.index)) ) return result def unique(self): """Compute the ExtensionArray of unique values. Returns ------- uniques : ExtensionArray """ from pandas import factorize _, uniques = factorize(self) return uniques @property def nbytes(self): return self.data.nbytes def shift(self, periods=1, fill_value=None): """ Shift values by desired number. Newly introduced missing values are filled with ``self.dtype.na_value``. Parameters ---------- periods : int, default 1 The number of periods to shift. Negative values are allowed for shifting backwards. fill_value : object, optional (default None) The scalar value to use for newly introduced missing values. The default is ``self.dtype.na_value``. Returns ------- GeometryArray Shifted. Notes ----- If ``self`` is empty or ``periods`` is 0, a copy of ``self`` is returned. If ``periods > len(self)``, then an array of size len(self) is returned, with all values filled with ``self.dtype.na_value``. """ shifted = super().shift(periods, fill_value) shifted.crs = self.crs return shifted # ------------------------------------------------------------------------- # ExtensionArray specific # ------------------------------------------------------------------------- @classmethod def _from_sequence(cls, scalars, dtype=None, copy=False): """ Construct a new ExtensionArray from a sequence of scalars. Parameters ---------- scalars : Sequence Each element will be an instance of the scalar type for this array, ``cls.dtype.type``. dtype : dtype, optional Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : boolean, default False If True, copy the underlying data. Returns ------- ExtensionArray """ # GH 1413 if isinstance(scalars, BaseGeometry): scalars = [scalars] return from_shapely(scalars) def _values_for_factorize(self): # type: () -> Tuple[np.ndarray, Any] """Return an array and missing value suitable for factorization. Returns ------- values : ndarray An array suitable for factoraization. This should maintain order and be a supported dtype (Float64, Int64, UInt64, String, Object). By default, the extension array is cast to object dtype. na_value : object The value in `values` to consider missing. This will be treated as NA in the factorization routines, so it will be coded as `na_sentinal` and not included in `uniques`. By default, ``np.nan`` is used. """ vals = to_wkb(self) return vals, None @classmethod def _from_factorized(cls, values, original): """ Reconstruct an ExtensionArray after factorization. Parameters ---------- values : ndarray An integer ndarray with the factorized values. original : ExtensionArray The original ExtensionArray that factorize was called on. See Also -------- pandas.factorize ExtensionArray.factorize """ return from_wkb(values, crs=original.crs) def _values_for_argsort(self): # type: () -> np.ndarray """Return values for sorting. Returns ------- ndarray The transformed values should maintain the ordering between values within the array. See Also -------- ExtensionArray.argsort """ # Note: this is used in `ExtensionArray.argsort`. raise TypeError("geometries are not orderable") def _formatter(self, boxed=False): """Formatting function for scalar values. This is used in the default '__repr__'. The returned formatting function receives instances of your scalar type. Parameters ---------- boxed: bool, default False An indicated for whether or not your array is being printed within a Series, DataFrame, or Index (True), or just by itself (False). This may be useful if you want scalar values to appear differently within a Series versus on its own (e.g. quoted or not). Returns ------- Callable[[Any], str] A callable that gets instances of the scalar type and returns a string. By default, :func:`repr` is used when ``boxed=False`` and :func:`str` is used when ``boxed=True``. """ if boxed: import geopandas precision = geopandas.options.display_precision if precision is None: if self.crs: if self.crs.is_projected: precision = 3 else: precision = 5 else: # fallback # dummy heuristic based on 10 first geometries that should # work in most cases with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) xmin, ymin, xmax, ymax = self[~self.isna()][:10].total_bounds if ( (-180 <= xmin <= 180) and (-180 <= xmax <= 180) and (-90 <= ymin <= 90) and (-90 <= ymax <= 90) ): # geographic coordinates precision = 5 else: # typically projected coordinates # (in case of unit meter: mm precision) precision = 3 return lambda geom: shapely.wkt.dumps(geom, rounding_precision=precision) return repr @classmethod def _concat_same_type(cls, to_concat): """ Concatenate multiple array Parameters ---------- to_concat : sequence of this type Returns ------- ExtensionArray """ data = np.concatenate([ga.data for ga in to_concat]) return GeometryArray(data, crs=to_concat[0].crs) def _reduce(self, name, skipna=True, **kwargs): # including the base class version here (that raises by default) # because this was not yet defined in pandas 0.23 if name == "any" or name == "all": # TODO(pygeos) return getattr(to_shapely(self), name)() raise TypeError( "cannot perform {name} with type {dtype}".format( name=name, dtype=self.dtype ) ) def __array__(self, dtype=None): """ The numpy array interface. Returns ------- values : numpy array """ return to_shapely(self) def _binop(self, other, op): def convert_values(param): if not _is_scalar_geometry(param) and ( isinstance(param, ExtensionArray) or pd.api.types.is_list_like(param) ): ovalues = param else: # Assume its an object ovalues = [param] * len(self) return ovalues if isinstance(other, (pd.Series, pd.Index, pd.DataFrame)): # rely on pandas to unbox and dispatch to us return NotImplemented lvalues = self rvalues = convert_values(other) if len(lvalues) != len(rvalues): raise ValueError("Lengths must match to compare") # If the operator is not defined for the underlying objects, # a TypeError should be raised res = [op(a, b) for (a, b) in zip(lvalues, rvalues)] res = np.asarray(res, dtype=bool) return res def __eq__(self, other): return self._binop(other, operator.eq) def __ne__(self, other): return self._binop(other, operator.ne) def __contains__(self, item): """ Return for `item in self`. """ if vectorized.isna(item): if ( item is self.dtype.na_value or isinstance(item, self.dtype.type) or item is None ): return self.isna().any() else: return False return (self == item).any() geopandas-0.10.2/geopandas/base.py000066400000000000000000003340341413247402600167720ustar00rootroot00000000000000from warnings import warn import numpy as np import pandas as pd from pandas import DataFrame, Series from shapely.geometry import box from shapely.geometry.base import BaseGeometry from .array import GeometryArray, GeometryDtype def is_geometry_type(data): """ Check if the data is of geometry dtype. Does not include object array of shapely scalars. """ if isinstance(getattr(data, "dtype", None), GeometryDtype): # GeometryArray, GeoSeries and Series[GeometryArray] return True else: return False def _delegate_binary_method(op, this, other, align, *args, **kwargs): # type: (str, GeoSeries, GeoSeries) -> GeoSeries/Series this = this.geometry if isinstance(other, GeoPandasBase): if align and not this.index.equals(other.index): warn("The indices of the two GeoSeries are different.") this, other = this.align(other.geometry) else: other = other.geometry a_this = GeometryArray(this.values) other = GeometryArray(other.values) elif isinstance(other, BaseGeometry): a_this = GeometryArray(this.values) else: raise TypeError(type(this), type(other)) data = getattr(a_this, op)(other, *args, **kwargs) return data, this.index def _binary_geo(op, this, other, align): # type: (str, GeoSeries, GeoSeries) -> GeoSeries """Binary operation on GeoSeries objects that returns a GeoSeries""" from .geoseries import GeoSeries geoms, index = _delegate_binary_method(op, this, other, align) return GeoSeries(geoms.data, index=index, crs=this.crs) def _binary_op(op, this, other, align, *args, **kwargs): # type: (str, GeoSeries, GeoSeries, args/kwargs) -> Series[bool/float] """Binary operation on GeoSeries objects that returns a Series""" data, index = _delegate_binary_method(op, this, other, align, *args, **kwargs) return Series(data, index=index) def _delegate_property(op, this): # type: (str, GeoSeries) -> GeoSeries/Series a_this = GeometryArray(this.geometry.values) data = getattr(a_this, op) if isinstance(data, GeometryArray): from .geoseries import GeoSeries return GeoSeries(data.data, index=this.index, crs=this.crs) else: return Series(data, index=this.index) def _delegate_geo_method(op, this, *args, **kwargs): # type: (str, GeoSeries) -> GeoSeries """Unary operation that returns a GeoSeries""" from .geoseries import GeoSeries a_this = GeometryArray(this.geometry.values) data = getattr(a_this, op)(*args, **kwargs).data return GeoSeries(data, index=this.index, crs=this.crs) class GeoPandasBase(object): @property def area(self): """Returns a ``Series`` containing the area of each geometry in the ``GeoSeries`` expressed in the units of the CRS. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(10, 0), (10, 5), (0, 0)]), ... Polygon([(0, 0), (2, 2), (2, 0)]), ... LineString([(0, 0), (1, 1), (0, 1)]), ... Point(0, 1) ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 POLYGON ((10.00000 0.00000, 10.00000 5.00000, ... 2 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 2.... 3 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 4 POINT (0.00000 1.00000) dtype: geometry >>> s.area 0 0.5 1 25.0 2 2.0 3 0.0 4 0.0 dtype: float64 See also -------- GeoSeries.length : measure length Notes ----- Area may be invalid for a geographic CRS using degrees as units; use :meth:`GeoSeries.to_crs` to project geometries to a planar CRS before using this function. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return _delegate_property("area", self) @property def crs(self): """ The Coordinate Reference System (CRS) represented as a ``pyproj.CRS`` object. Returns None if the CRS is not set, and to set the value it :getter: Returns a ``pyproj.CRS`` or None. When setting, the value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. Examples -------- >>> s.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See also -------- GeoSeries.set_crs : assign CRS GeoSeries.to_crs : re-project to another CRS """ return self.geometry.values.crs @crs.setter def crs(self, value): """Sets the value of the crs""" self.geometry.values.crs = value @property def geom_type(self): """ Returns a ``Series`` of strings specifying the `Geometry Type` of each object. Examples -------- >>> from shapely.geometry import Point, Polygon, LineString >>> d = {'geometry': [Point(2, 1), Polygon([(0, 0), (1, 1), (1, 0)]), ... LineString([(0, 0), (1, 1)])]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf.geom_type 0 Point 1 Polygon 2 LineString dtype: object """ return _delegate_property("geom_type", self) @property def type(self): """Return the geometry type of each geometry in the GeoSeries""" return self.geom_type @property def length(self): """Returns a ``Series`` containing the length of each geometry expressed in the units of the CRS. In the case of a (Multi)Polygon it measures the length of its exterior (i.e. perimeter). Examples -------- >>> from shapely.geometry import Polygon, LineString, MultiLineString, Point, \ GeometryCollection >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (0, 1)]), ... LineString([(10, 0), (10, 5), (0, 0)]), ... MultiLineString([((0, 0), (1, 0)), ((-1, 0), (1, 0))]), ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Point(0, 1), ... GeometryCollection([Point(1, 0), LineString([(10, 0), (10, 5), (0,\ 0)])]) ... ] ... ) >>> s 0 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 1 LINESTRING (10.00000 0.00000, 10.00000 5.00000... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 0.0... 3 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 4 POINT (0.00000 1.00000) 5 GEOMETRYCOLLECTION (POINT (1.00000 0.00000), L... dtype: geometry >>> s.length 0 2.414214 1 16.180340 2 3.000000 3 3.414214 4 0.000000 5 16.180340 dtype: float64 See also -------- GeoSeries.area : measure area of a polygon Notes ----- Length may be invalid for a geographic CRS using degrees as units; use :meth:`GeoSeries.to_crs` to project geometries to a planar CRS before using this function. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return _delegate_property("length", self) @property def is_valid(self): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for geometries that are valid. Examples -------- An example with one invalid polygon (a bowtie geometry crossing itself) and one missing geometry: >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(0,0), (1, 1), (1, 0), (0, 1)]), # bowtie geometry ... Polygon([(0, 0), (2, 2), (2, 0)]), ... None ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 1.... 2 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 2.... 3 None dtype: geometry >>> s.is_valid 0 True 1 False 2 True 3 False dtype: bool """ return _delegate_property("is_valid", self) @property def is_empty(self): """ Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for empty geometries. Examples -------- An example of a GeoDataFrame with one empty point, one point and one missing value: >>> from shapely.geometry import Point >>> d = {'geometry': [Point(), Point(2, 1), None]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf geometry 0 GEOMETRYCOLLECTION EMPTY 1 POINT (2.00000 1.00000) 2 None >>> gdf.is_empty 0 True 1 False 2 False dtype: bool See Also -------- GeoSeries.isna : detect missing values """ return _delegate_property("is_empty", self) @property def is_simple(self): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for geometries that do not cross themselves. This is meaningful only for `LineStrings` and `LinearRings`. Examples -------- >>> from shapely.geometry import LineString >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (1, -1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, -1)]), ... ] ... ) >>> s 0 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... dtype: geometry >>> s.is_simple 0 False 1 True dtype: bool """ return _delegate_property("is_simple", self) @property def is_ring(self): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for features that are closed. When constructing a LinearRing, the sequence of coordinates may be explicitly closed by passing identical values in the first and last indices. Otherwise, the sequence will be implicitly closed by copying the first tuple to the last index. Examples -------- >>> from shapely.geometry import LineString, LinearRing >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (1, 1), (1, -1)]), ... LineString([(0, 0), (1, 1), (1, -1), (0, 0)]), ... LinearRing([(0, 0), (1, 1), (1, -1)]), ... ] ... ) >>> s 0 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 2 LINEARRING (0.00000 0.00000, 1.00000 1.00000, ... dtype: geometry >>> s.is_ring 0 False 1 True 2 True dtype: bool """ return _delegate_property("is_ring", self) @property def has_z(self): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for features that have a z-component. Notes ------ Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1), ... Point(0, 1, 2), ... ] ... ) >>> s 0 POINT (0.00000 1.00000) 1 POINT Z (0.00000 1.00000 2.00000) dtype: geometry >>> s.has_z 0 False 1 True dtype: bool """ return _delegate_property("has_z", self) # # Unary operations that return a GeoSeries # @property def boundary(self): """Returns a ``GeoSeries`` of lower dimensional objects representing each geometries's set-theoretic `boundary`. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 2 POINT (0.00000 0.00000) dtype: geometry >>> s.boundary 0 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 1 MULTIPOINT (0.00000 0.00000, 1.00000 0.00000) 2 GEOMETRYCOLLECTION EMPTY dtype: geometry See also -------- GeoSeries.exterior : outer boundary (without interior rings) """ return _delegate_property("boundary", self) @property def centroid(self): """Returns a ``GeoSeries`` of points representing the centroid of each geometry. Note that centroid does not have to be on or within original geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 2 POINT (0.00000 0.00000) dtype: geometry >>> s.centroid 0 POINT (0.33333 0.66667) 1 POINT (0.70711 0.50000) 2 POINT (0.00000 0.00000) dtype: geometry See also -------- GeoSeries.representative_point : point guaranteed to be within each geometry """ return _delegate_property("centroid", self) @property def convex_hull(self): """Returns a ``GeoSeries`` of geometries representing the convex hull of each geometry. The convex hull of a geometry is the smallest convex `Polygon` containing all the points in each geometry, unless the number of points in the geometric object is less than three. For two points, the convex hull collapses to a `LineString`; for 1, a `Point`. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point, MultiPoint >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... MultiPoint([(0, 0), (1, 1), (0, 1), (1, 0), (0.5, 0.5)]), ... MultiPoint([(0, 0), (1, 1)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 2 MULTIPOINT (0.00000 0.00000, 1.00000 1.00000, ... 3 MULTIPOINT (0.00000 0.00000, 1.00000 1.00000) 4 POINT (0.00000 0.00000) dtype: geometry >>> s.convex_hull 0 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 1.... 2 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... 3 LINESTRING (0.00000 0.00000, 1.00000 1.00000) 4 POINT (0.00000 0.00000) dtype: geometry See also -------- GeoSeries.envelope : bounding rectangle geometry """ return _delegate_property("convex_hull", self) @property def envelope(self): """Returns a ``GeoSeries`` of geometries representing the envelope of each geometry. The envelope of a geometry is the bounding rectangle. That is, the point or smallest rectangular polygon (with sides parallel to the coordinate axes) that contains the geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point, MultiPoint >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... MultiPoint([(0, 0), (1, 1)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 2 MULTIPOINT (0.00000 0.00000, 1.00000 1.00000) 3 POINT (0.00000 0.00000) dtype: geometry >>> s.envelope 0 POLYGON ((0.00000 0.00000, 1.00000 0.00000, 1.... 1 POLYGON ((0.00000 0.00000, 1.00000 0.00000, 1.... 2 POLYGON ((0.00000 0.00000, 1.00000 0.00000, 1.... 3 POINT (0.00000 0.00000) dtype: geometry See also -------- GeoSeries.convex_hull : convex hull geometry """ return _delegate_property("envelope", self) @property def exterior(self): """Returns a ``GeoSeries`` of LinearRings representing the outer boundary of each polygon in the GeoSeries. Applies to GeoSeries containing only Polygons. Returns ``None``` for other geometry types. Examples -------- >>> from shapely.geometry import Polygon, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... Polygon([(1, 0), (2, 1), (0, 0)]), ... Point(0, 1) ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 POLYGON ((1.00000 0.00000, 2.00000 1.00000, 0.... 2 POINT (0.00000 1.00000) dtype: geometry >>> s.exterior 0 LINEARRING (0.00000 0.00000, 1.00000 1.00000, ... 1 LINEARRING (1.00000 0.00000, 2.00000 1.00000, ... 2 None dtype: geometry See also -------- GeoSeries.boundary : complete set-theoretic boundary GeoSeries.interiors : list of inner rings of each polygon """ # TODO: return empty geometry for non-polygons return _delegate_property("exterior", self) @property def interiors(self): """Returns a ``Series`` of List representing the inner rings of each polygon in the GeoSeries. Applies to GeoSeries containing only Polygons. Returns ---------- inner_rings: Series of List Inner rings of each polygon in the GeoSeries. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon( ... [(0, 0), (0, 5), (5, 5), (5, 0)], ... [[(1, 1), (2, 1), (1, 2)], [(1, 4), (2, 4), (2, 3)]], ... ), ... Polygon([(1, 0), (2, 1), (0, 0)]), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 0.00000 5.00000, 5.... 1 POLYGON ((1.00000 0.00000, 2.00000 1.00000, 0.... dtype: geometry >>> s.interiors 0 [LINEARRING (1 1, 2 1, 1 2, 1 1), LINEARRING (... 1 [] dtype: object See also -------- GeoSeries.exterior : outer boundary """ return _delegate_property("interiors", self) def representative_point(self): """Returns a ``GeoSeries`` of (cheaply computed) points that are guaranteed to be within each geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (1, 1), (1, 0)]), ... Point(0, 0), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 LINESTRING (0.00000 0.00000, 1.00000 1.00000, ... 2 POINT (0.00000 0.00000) dtype: geometry >>> s.representative_point() 0 POINT (0.25000 0.50000) 1 POINT (1.00000 1.00000) 2 POINT (0.00000 0.00000) dtype: geometry See also -------- GeoSeries.centroid : geometric centroid """ return _delegate_geo_method("representative_point", self) # # Reduction operations that return a Shapely geometry # @property def cascaded_union(self): """Deprecated: use `unary_union` instead""" warn( "The 'cascaded_union' attribute is deprecated, use 'unary_union' instead", FutureWarning, stacklevel=2, ) return self.geometry.values.unary_union() @property def unary_union(self): """Returns a geometry containing the union of all geometries in the ``GeoSeries``. Examples -------- >>> from shapely.geometry import box >>> s = geopandas.GeoSeries([box(0,0,1,1), box(0,0,2,2)]) >>> s 0 POLYGON ((1.00000 0.00000, 1.00000 1.00000, 0.... 1 POLYGON ((2.00000 0.00000, 2.00000 2.00000, 0.... dtype: geometry >>> union = s.unary_union >>> print(union) POLYGON ((0 1, 0 2, 2 2, 2 0, 1 0, 0 0, 0 1)) """ return self.geometry.values.unary_union() # # Binary operations that return a pandas Series # def contains(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that contains `other`. An object is said to contain `other` if at least one point of `other` lies in the interior and no points of `other` lie in the exterior of the object. (Therefore, any given polygon does not contain its own boundary – there is not any point that lies in the interior.) If either object is empty, this operation returns ``False``. This is the inverse of :meth:`within` in the sense that the expression ``a.contains(b) == b.within(a)`` always evaluates to ``True``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if it is contained. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (0, 2)]), ... LineString([(0, 0), (0, 1)]), ... Point(0, 1), ... ], ... index=range(0, 4), ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 LINESTRING (0.00000 0.00000, 0.00000 2.00000) 2 LINESTRING (0.00000 0.00000, 0.00000 1.00000) 3 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 POLYGON ((0.00000 0.00000, 1.00000 2.00000, 0.... 3 LINESTRING (0.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check if each geometry of GeoSeries contains a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> point = Point(0, 1) >>> s.contains(point) 0 False 1 True 2 False 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2.contains(s, align=True) 0 False 1 False 2 False 3 True 4 False dtype: bool >>> s2.contains(s, align=False) 1 True 2 False 3 True 4 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``contains`` *any* element of the other one. See also -------- GeoSeries.within """ return _binary_op("contains", self, other, align) def geom_equals(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry equal to `other`. An object is said to be equal to `other` if its set-theoretic `boundary`, `interior`, and `exterior` coincides with those of the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test for equality. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... Point(0, 1), ... LineString([(0, 0), (0, 2)]), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 1.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 0.00000 2.00000) 3 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 POLYGON ((0.00000 0.00000, 1.00000 2.00000, 0.... 3 POINT (0.00000 1.00000) 4 LINESTRING (0.00000 0.00000, 0.00000 2.00000) dtype: geometry We can check if each geometry of GeoSeries contains a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> polygon = Polygon([(0, 0), (2, 2), (0, 2)]) >>> s.geom_equals(polygon) 0 True 1 False 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.geom_equals(s2) 0 False 1 False 2 False 3 True 4 False dtype: bool >>> s.geom_equals(s2, align=False) 0 True 1 True 2 False 3 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See also -------- GeoSeries.geom_almost_equals GeoSeries.geom_equals_exact """ return _binary_op("geom_equals", self, other, align) def geom_almost_equals(self, other, decimal=6, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if each aligned geometry is approximately equal to `other`. Approximate equality is tested at all points to the specified `decimal` place precision. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to compare to. decimal : int Decimal place presion used when testing for approximate equality. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1.1), ... Point(0, 1.01), ... Point(0, 1.001), ... ], ... ) >>> s 0 POINT (0.00000 1.10000) 1 POINT (0.00000 1.01000) 2 POINT (0.00000 1.00100) dtype: geometry >>> s.geom_almost_equals(Point(0, 1), decimal=2) 0 False 1 False 2 True dtype: bool >>> s.geom_almost_equals(Point(0, 1), decimal=1) 0 False 1 True 2 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See also -------- GeoSeries.geom_equals GeoSeries.geom_equals_exact """ return _binary_op( "geom_almost_equals", self, other, decimal=decimal, align=align ) def geom_equals_exact(self, other, tolerance, align=True): """Return True for all geometries that equal aligned *other* to a given tolerance, else False. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to compare to. tolerance : float Decimal place presion used when testing for approximate equality. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries( ... [ ... Point(0, 1.1), ... Point(0, 1.0), ... Point(0, 1.2), ... ] ... ) >>> s 0 POINT (0.00000 1.10000) 1 POINT (0.00000 1.00000) 2 POINT (0.00000 1.20000) dtype: geometry >>> s.geom_equals_exact(Point(0, 1), tolerance=0.1) 0 False 1 True 2 False dtype: bool >>> s.geom_equals_exact(Point(0, 1), tolerance=0.15) 0 True 1 True 2 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See also -------- GeoSeries.geom_equals GeoSeries.geom_almost_equals """ return _binary_op( "geom_equals_exact", self, other, tolerance=tolerance, align=align ) def crosses(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that cross `other`. An object is said to cross `other` if its `interior` intersects the `interior` of the other but does not contain it, and the dimension of the intersection is less than the dimension of the one or the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is crossed. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 2 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 3 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 2 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 3 POINT (1.00000 1.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check if each geometry of GeoSeries crosses a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(-1, 1), (3, 1)]) >>> s.crosses(line) 0 True 1 True 2 True 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.crosses(s2, align=True) 0 False 1 True 2 False 3 False 4 False dtype: bool >>> s.crosses(s2, align=False) 0 True 1 True 2 False 3 False dtype: bool Notice that a line does not cross a point that it contains. Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``crosses`` *any* element of the other one. See also -------- GeoSeries.disjoint GeoSeries.intersects """ return _binary_op("crosses", self, other, align) def disjoint(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry disjoint to `other`. An object is said to be disjoint to `other` if its `boundary` and `interior` does not intersect at all with those of the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is disjoint. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(-1, 0), (-1, 2), (0, -2)]), ... LineString([(0, 0), (0, 1)]), ... Point(1, 1), ... Point(0, 0), ... ], ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 2 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 3 POINT (0.00000 1.00000) dtype: geometry >>> s2 0 POLYGON ((-1.00000 0.00000, -1.00000 2.00000, ... 1 LINESTRING (0.00000 0.00000, 0.00000 1.00000) 2 POINT (1.00000 1.00000) 3 POINT (0.00000 0.00000) dtype: geometry We can check each geometry of GeoSeries to a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(0, 0), (2, 0)]) >>> s.disjoint(line) 0 False 1 False 2 False 3 True dtype: bool We can also check two GeoSeries against each other, row by row. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.disjoint(s2) 0 True 1 False 2 False 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is equal to *any* element of the other one. See also -------- GeoSeries.intersects GeoSeries.touches """ return _binary_op("disjoint", self, other, align) def intersects(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that intersects `other`. An object is said to intersect `other` if its `boundary` and `interior` intersects in any way with those of the other. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is intersected. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 2 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 3 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 2 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 3 POINT (1.00000 1.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check if each geometry of GeoSeries crosses a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(-1, 1), (3, 1)]) >>> s.intersects(line) 0 True 1 True 2 True 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.intersects(s2, align=True) 0 False 1 True 2 True 3 False 4 False dtype: bool >>> s.intersects(s2, align=False) 0 True 1 True 2 True 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``crosses`` *any* element of the other one. See also -------- GeoSeries.disjoint GeoSeries.crosses GeoSeries.touches GeoSeries.intersection """ return _binary_op("intersects", self, other, align) def overlaps(self, other, align=True): """Returns True for all aligned geometries that overlap *other*, else False. Geometries overlaps if they have more than one but not all points in common, have the same dimension, and the intersection of the interiors of the geometries has the same dimension as the geometries themselves. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if overlaps. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, MultiPoint, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... MultiPoint([(0, 0), (0, 1)]), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 0), (0, 2)]), ... LineString([(0, 1), (1, 1)]), ... LineString([(1, 1), (3, 3)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 MULTIPOINT (0.00000 0.00000, 0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 2.00000 0.00000, 0.... 2 LINESTRING (0.00000 1.00000, 1.00000 1.00000) 3 LINESTRING (1.00000 1.00000, 3.00000 3.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check if each geometry of GeoSeries overlaps a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> polygon = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> s.overlaps(polygon) 0 True 1 True 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.overlaps(s2) 0 False 1 True 2 False 3 False 4 False dtype: bool >>> s.overlaps(s2, align=False) 0 True 1 False 2 True 3 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``overlaps`` *any* element of the other one. See also -------- GeoSeries.crosses GeoSeries.intersects """ return _binary_op("overlaps", self, other, align) def touches(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that touches `other`. An object is said to touch `other` if it has at least one point in common with `other` and its interior does not intersect with any part of the other. Overlapping features therefore do not touch. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if is touched. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, MultiPoint, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... MultiPoint([(0, 0), (0, 1)]), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (-2, 0), (0, -2)]), ... LineString([(0, 1), (1, 1)]), ... LineString([(1, 1), (3, 0)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 MULTIPOINT (0.00000 0.00000, 0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, -2.00000 0.00000, 0... 2 LINESTRING (0.00000 1.00000, 1.00000 1.00000) 3 LINESTRING (1.00000 1.00000, 3.00000 0.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check if each geometry of GeoSeries touches a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> line = LineString([(0, 0), (-1, -2)]) >>> s.touches(line) 0 True 1 True 2 True 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.touches(s2, align=True) 0 False 1 True 2 True 3 False 4 False dtype: bool >>> s.touches(s2, align=False) 0 True 1 False 2 True 3 False dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``touches`` *any* element of the other one. See also -------- GeoSeries.overlaps GeoSeries.intersects """ return _binary_op("touches", self, other, align) def within(self, other, align=True): """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is within `other`. An object is said to be within `other` if at least one of its points is located in the `interior` and no points are located in the `exterior` of the other. If either object is empty, this operation returns ``False``. This is the inverse of :meth:`contains` in the sense that the expression ``a.within(b) == b.contains(a)`` always evaluates to ``True``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : GeoSeries or geometric object The GeoSeries (elementwise) or geometric object to test if each geometry is within. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (1, 2), (0, 2)]), ... LineString([(0, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(0, 0), (0, 2)]), ... LineString([(0, 0), (0, 1)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 1.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 0.00000 2.00000) 3 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 2 LINESTRING (0.00000 0.00000, 0.00000 2.00000) 3 LINESTRING (0.00000 0.00000, 0.00000 1.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check if each geometry of GeoSeries is within a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> polygon = Polygon([(0, 0), (2, 2), (0, 2)]) >>> s.within(polygon) 0 True 1 True 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s2.within(s) 0 False 1 False 2 True 3 False 4 False dtype: bool >>> s2.within(s, align=False) 1 True 2 False 3 True 4 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is ``within`` *any* element of the other one. See also -------- GeoSeries.contains """ return _binary_op("within", self, other, align) def covers(self, other, align=True): """ Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is entirely covering `other`. An object A is said to cover another object B if no points of B lie in the exterior of A. If either object is empty, this operation returns ``False``. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center See https://lin-ear-th-inking.blogspot.com/2007/06/subtleties-of-ogc-covers-spatial.html for reference. Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to check is being covered. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... LineString([(1, 1), (1.5, 1.5)]), ... Point(0, 0), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 0.00000, 2.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 POINT (0.00000 0.00000) dtype: geometry >>> s2 1 POLYGON ((0.50000 0.50000, 1.50000 0.50000, 1.... 2 POLYGON ((0.00000 0.00000, 2.00000 0.00000, 2.... 3 LINESTRING (1.00000 1.00000, 1.50000 1.50000) 4 POINT (0.00000 0.00000) dtype: geometry We can check if each geometry of GeoSeries covers a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> poly = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) >>> s.covers(poly) 0 True 1 False 2 False 3 False dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.covers(s2, align=True) 0 False 1 False 2 False 3 False 4 False dtype: bool >>> s.covers(s2, align=False) 0 True 1 False 2 True 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries ``covers`` *any* element of the other one. See also -------- GeoSeries.covered_by GeoSeries.overlaps """ return _binary_op("covers", self, other, align) def covered_by(self, other, align=True): """ Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for each aligned geometry that is entirely covered by `other`. An object A is said to cover another object B if no points of B lie in the exterior of A. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center See https://lin-ear-th-inking.blogspot.com/2007/06/subtleties-of-ogc-covers-spatial.html for reference. Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to check is being covered. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (bool) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... LineString([(1, 1), (1.5, 1.5)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... Point(0, 0), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.50000 0.50000, 1.50000 0.50000, 1.... 1 POLYGON ((0.00000 0.00000, 2.00000 0.00000, 2.... 2 LINESTRING (1.00000 1.00000, 1.50000 1.50000) 3 POINT (0.00000 0.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 2.00000 0.00000, 2.... 2 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 3 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 4 POINT (0.00000 0.00000) dtype: geometry We can check if each geometry of GeoSeries is covered by a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> poly = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) >>> s.covered_by(poly) 0 True 1 True 2 True 3 True dtype: bool We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.covered_by(s2, align=True) 0 False 1 True 2 True 3 True 4 False dtype: bool >>> s.covered_by(s2, align=False) 0 True 1 False 2 True 3 True dtype: bool Notes ----- This method works in a row-wise manner. It does not check if an element of one GeoSeries is ``covered_by`` *any* element of the other one. See also -------- GeoSeries.covers GeoSeries.overlaps """ return _binary_op("covered_by", self, other, align) def distance(self, other, align=True): """Returns a ``Series`` containing the distance to aligned `other`. The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the distance to. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series (float) Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 0), (1, 1)]), ... Polygon([(0, 0), (-1, 0), (-1, 1)]), ... LineString([(1, 1), (0, 0)]), ... Point(0, 0), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]), ... Point(3, 1), ... LineString([(1, 0), (2, 0)]), ... Point(0, 1), ... ], ... index=range(1, 5), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 0.00000, 1.... 1 POLYGON ((0.00000 0.00000, -1.00000 0.00000, -... 2 LINESTRING (1.00000 1.00000, 0.00000 0.00000) 3 POINT (0.00000 0.00000) dtype: geometry >>> s2 1 POLYGON ((0.50000 0.50000, 1.50000 0.50000, 1.... 2 POINT (3.00000 1.00000) 3 LINESTRING (1.00000 0.00000, 2.00000 0.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can check the distance of each geometry of GeoSeries to a single geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> point = Point(-1, 0) >>> s.distance(point) 0 1.0 1 0.0 2 1.0 3 1.0 dtype: float64 We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and use elements with the same index using ``align=True`` or ignore index and use elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.distance(s2, align=True) 0 NaN 1 0.707107 2 2.000000 3 1.000000 4 NaN dtype: float64 >>> s.distance(s2, align=False) 0 0.000000 1 3.162278 2 0.707107 3 1.000000 dtype: float64 """ return _binary_op("distance", self, other, align) # # Binary operations that return a GeoSeries # def difference(self, other, align=True): """Returns a ``GeoSeries`` of the points in each aligned geometry that are not in `other`. .. image:: ../../../_static/binary_geo-difference.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the difference to. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 2 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (1.00000 1.00000) 5 POINT (0.00000 1.00000) dtype: geometry We can do difference of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.difference(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 1 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 2 LINESTRING (1.00000 1.00000, 2.00000 2.00000) 3 MULTILINESTRING ((2.00000 0.00000, 1.00000 1.0... 4 POINT EMPTY dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.difference(s2, align=True) 0 None 1 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 1.0... 3 LINESTRING EMPTY 4 POINT (0.00000 1.00000) 5 None dtype: geometry >>> s.difference(s2, align=False) 0 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 1 POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1.... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 1.0... 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT EMPTY dtype: geometry See Also -------- GeoSeries.symmetric_difference GeoSeries.union GeoSeries.intersection """ return _binary_geo("difference", self, other, align) def symmetric_difference(self, other, align=True): """Returns a ``GeoSeries`` of the symmetric difference of points in each aligned geometry with `other`. For each geometry, the symmetric difference consists of points in the geometry not in `other`, and points in `other` not in the geometry. .. image:: ../../../_static/binary_geo-symm_diff.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the symmetric difference to. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 2 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (1.00000 1.00000) 5 POINT (0.00000 1.00000) dtype: geometry We can do symmetric difference of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.symmetric_difference(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 1 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 2 GEOMETRYCOLLECTION (POLYGON ((0.00000 0.00000,... 3 GEOMETRYCOLLECTION (POLYGON ((0.00000 0.00000,... 4 POLYGON ((0.00000 1.00000, 1.00000 1.00000, 0.... dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.symmetric_difference(s2, align=True) 0 None 1 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 1.0... 3 LINESTRING EMPTY 4 MULTIPOINT (0.00000 1.00000, 1.00000 1.00000) 5 None dtype: geometry >>> s.symmetric_difference(s2, align=False) 0 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 1.... 1 GEOMETRYCOLLECTION (POLYGON ((0.00000 0.00000,... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 1.0... 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT EMPTY dtype: geometry See Also -------- GeoSeries.difference GeoSeries.union GeoSeries.intersection """ return _binary_geo("symmetric_difference", self, other, align) def union(self, other, align=True): """Returns a ``GeoSeries`` of the union of points in each aligned geometry with `other`. .. image:: ../../../_static/binary_geo-union.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the union with. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 2 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (1.00000 1.00000) 5 POINT (0.00000 1.00000) dtype: geometry We can do union of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.union(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 0.... 1 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 0.... 2 GEOMETRYCOLLECTION (POLYGON ((0.00000 0.00000,... 3 GEOMETRYCOLLECTION (POLYGON ((0.00000 0.00000,... 4 POLYGON ((0.00000 1.00000, 1.00000 1.00000, 0.... dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.union(s2, align=True) 0 None 1 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 0.... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 1.0... 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 MULTIPOINT (0.00000 1.00000, 1.00000 1.00000) 5 None dtype: geometry >>> s.union(s2, align=False) 0 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 0.... 1 GEOMETRYCOLLECTION (POLYGON ((0.00000 0.00000,... 2 MULTILINESTRING ((0.00000 0.00000, 1.00000 1.0... 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry See Also -------- GeoSeries.symmetric_difference GeoSeries.difference GeoSeries.intersection """ return _binary_geo("union", self, other, align) def intersection(self, other, align=True): """Returns a ``GeoSeries`` of the intersection of points in each aligned geometry with `other`. .. image:: ../../../_static/binary_geo-intersection.svg :align: center The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : Geoseries or geometric object The Geoseries (elementwise) or geometric object to find the intersection with. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 2 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (1.00000 1.00000) 5 POINT (0.00000 1.00000) dtype: geometry We can also do intersection of each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.intersection(Polygon([(0, 0), (1, 1), (0, 1)])) 0 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... 1 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... 2 LINESTRING (0.00000 0.00000, 1.00000 1.00000) 3 POINT (1.00000 1.00000) 4 POINT (0.00000 1.00000) dtype: geometry We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.intersection(s2, align=True) 0 None 1 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... 2 POINT (1.00000 1.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT EMPTY 5 None dtype: geometry >>> s.intersection(s2, align=False) 0 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... 1 LINESTRING (1.00000 1.00000, 1.00000 2.00000) 2 POINT (1.00000 1.00000) 3 POINT (1.00000 1.00000) 4 POINT (0.00000 1.00000) dtype: geometry See Also -------- GeoSeries.difference GeoSeries.symmetric_difference GeoSeries.union """ return _binary_geo("intersection", self, other, align) # # Other operations # @property def bounds(self): """Returns a ``DataFrame`` with columns ``minx``, ``miny``, ``maxx``, ``maxy`` values containing the bounds for each geometry. See ``GeoSeries.total_bounds`` for the limits of the entire series. Examples -------- >>> from shapely.geometry import Point, Polygon, LineString >>> d = {'geometry': [Point(2, 1), Polygon([(0, 0), (1, 1), (1, 0)]), ... LineString([(0, 1), (1, 2)])]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf.bounds minx miny maxx maxy 0 2.0 1.0 2.0 1.0 1 0.0 0.0 1.0 1.0 2 0.0 1.0 1.0 2.0 """ bounds = GeometryArray(self.geometry.values).bounds return DataFrame( bounds, columns=["minx", "miny", "maxx", "maxy"], index=self.index ) @property def total_bounds(self): """Returns a tuple containing ``minx``, ``miny``, ``maxx``, ``maxy`` values for the bounds of the series as a whole. See ``GeoSeries.bounds`` for the bounds of the geometries contained in the series. Examples -------- >>> from shapely.geometry import Point, Polygon, LineString >>> d = {'geometry': [Point(3, -1), Polygon([(0, 0), (1, 1), (1, 0)]), ... LineString([(0, 1), (1, 2)])]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf.total_bounds array([ 0., -1., 3., 2.]) """ return GeometryArray(self.geometry.values).total_bounds @property def sindex(self): """Generate the spatial index Creates R-tree spatial index based on ``pygeos.STRtree`` or ``rtree.index.Index``. Note that the spatial index may not be fully initialized until the first use. Examples -------- >>> from shapely.geometry import box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(5), range(5))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) dtype: geometry Query the spatial index with a single geometry based on the bounding box: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) Query the spatial index with a single geometry based on the predicate: >>> s.sindex.query(box(1, 1, 3, 3), predicate="contains") array([2]) Query the spatial index with an array of geometries based on the bounding box: >>> s2 = geopandas.GeoSeries([box(1, 1, 3, 3), box(4, 4, 5, 5)]) >>> s2 0 POLYGON ((3.00000 1.00000, 3.00000 3.00000, 1.... 1 POLYGON ((5.00000 4.00000, 5.00000 5.00000, 4.... dtype: geometry >>> s.sindex.query_bulk(s2) array([[0, 0, 0, 1], [1, 2, 3, 4]]) Query the spatial index with an array of geometries based on the predicate: >>> s.sindex.query_bulk(s2, predicate="contains") array([[0], [2]]) """ return self.geometry.values.sindex @property def has_sindex(self): """Check the existence of the spatial index without generating it. Use the `.sindex` attribute on a GeoDataFrame or GeoSeries to generate a spatial index if it does not yet exist, which may take considerable time based on the underlying index implementation. Note that the underlying spatial index may not be fully initialized until the first use. Examples -------- >>> from shapely.geometry import Point >>> d = {'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d) >>> gdf.has_sindex False >>> index = gdf.sindex >>> gdf.has_sindex True Returns ------- bool `True` if the spatial index has been generated or `False` if not. """ return self.geometry.values.has_sindex def buffer(self, distance, resolution=16, **kwargs): """Returns a ``GeoSeries`` of geometries representing all points within a given ``distance`` of each geometric object. See http://shapely.readthedocs.io/en/latest/manual.html#object.buffer for details. Parameters ---------- distance : float, np.array, pd.Series The radius of the buffer. If np.array or pd.Series are used then it must have same length as the GeoSeries. resolution : int (optional, default 16) The resolution of the buffer around each vertex. Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(0, 0), ... LineString([(1, -1), (1, 0), (2, 0), (2, 1)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (0.00000 0.00000) 1 LINESTRING (1.00000 -1.00000, 1.00000 0.00000,... 2 POLYGON ((3.00000 -1.00000, 4.00000 0.00000, 3... dtype: geometry >>> s.buffer(0.2) 0 POLYGON ((0.20000 0.00000, 0.19904 -0.01960, 0... 1 POLYGON ((0.80000 0.00000, 0.80096 0.01960, 0.... 2 POLYGON ((2.80000 -1.00000, 2.80000 1.00000, 2... dtype: geometry ``**kwargs`` accept further specification as ``join_style`` and ``cap_style``. See the following illustration of different options. .. plot:: _static/code/buffer.py """ # TODO: update docstring based on pygeos after shapely 2.0 if isinstance(distance, pd.Series): if not self.index.equals(distance.index): raise ValueError( "Index values of distance sequence does " "not match index values of the GeoSeries" ) distance = np.asarray(distance) return _delegate_geo_method( "buffer", self, distance, resolution=resolution, **kwargs ) def simplify(self, *args, **kwargs): """Returns a ``GeoSeries`` containing a simplified representation of each geometry. The algorithm (Douglas-Peucker) recursively splits the original line into smaller parts and connects these parts’ endpoints by a straight line. Then, it removes all points whose distance to the straight line is smaller than `tolerance`. It does not move any points and it always preserves endpoints of the original line or polygon. See http://shapely.readthedocs.io/en/latest/manual.html#object.simplify for details Parameters ---------- tolerance : float All parts of a simplified geometry will be no more than `tolerance` distance from the original. It has the same units as the coordinate reference system of the GeoSeries. For example, using `tolerance=100` in a projected CRS with meters as units means a distance of 100 meters in reality. preserve_topology: bool (default True) False uses a quicker algorithm, but may produce self-intersecting or otherwise invalid geometries. Notes ----- Invalid geometric objects may result from simplification that does not preserve topology and simplification may be sensitive to the order of coordinates: two geometries differing only in order of coordinates may be simplified differently. Examples -------- >>> from shapely.geometry import Point, LineString >>> s = geopandas.GeoSeries( ... [Point(0, 0).buffer(1), LineString([(0, 0), (1, 10), (0, 20)])] ... ) >>> s 0 POLYGON ((1.00000 0.00000, 0.99518 -0.09802, 0... 1 LINESTRING (0.00000 0.00000, 1.00000 10.00000,... dtype: geometry >>> s.simplify(1) 0 POLYGON ((1.00000 0.00000, 0.00000 -1.00000, -... 1 LINESTRING (0.00000 0.00000, 0.00000 20.00000) dtype: geometry """ return _delegate_geo_method("simplify", self, *args, **kwargs) def relate(self, other, align=True): """ Returns the DE-9IM intersection matrices for the geometries The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center Parameters ---------- other : BaseGeometry or GeoSeries The other geometry to computed the DE-9IM intersection matrices from. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ---------- spatial_relations: Series of strings The DE-9IM intersection matrices which describe the spatial relations of the other geometry. Examples -------- >>> from shapely.geometry import Polygon, LineString, Point >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (2, 2), (0, 2)]), ... Polygon([(0, 0), (2, 2), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... Point(0, 1), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... LineString([(1, 0), (1, 3)]), ... LineString([(2, 0), (0, 2)]), ... Point(1, 1), ... Point(0, 1), ... ], ... index=range(1, 6), ... ) >>> s 0 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 1 POLYGON ((0.00000 0.00000, 2.00000 2.00000, 0.... 2 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (0.00000 1.00000) dtype: geometry >>> s2 1 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 2 LINESTRING (1.00000 0.00000, 1.00000 3.00000) 3 LINESTRING (2.00000 0.00000, 0.00000 2.00000) 4 POINT (1.00000 1.00000) 5 POINT (0.00000 1.00000) dtype: geometry We can relate each geometry and a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.relate(Polygon([(0, 0), (1, 1), (0, 1)])) 0 212F11FF2 1 212F11FF2 2 F11F00212 3 F01FF0212 4 F0FFFF212 dtype: object We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and compare elements with the same index using ``align=True`` or ignore index and compare elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.relate(s2, align=True) 0 None 1 212F11FF2 2 0F1FF0102 3 1FFF0FFF2 4 FF0FFF0F2 5 None dtype: object >>> s.relate(s2, align=False) 0 212F11FF2 1 1F20F1102 2 0F1FF0102 3 0F1FF0FF2 4 0FFFFFFF2 dtype: object """ return _binary_op("relate", self, other, align) def project(self, other, normalized=False, align=True): """ Return the distance along each geometry nearest to *other* The operation works on a 1-to-1 row-wise manner: .. image:: ../../../_static/binary_op-01.svg :align: center The project method is the inverse of interpolate. Parameters ---------- other : BaseGeometry or GeoSeries The *other* geometry to computed projected point from. normalized : boolean If normalized is True, return the distance normalized to the length of the object. align : bool (default True) If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved. Returns ------- Series Examples -------- >>> from shapely.geometry import LineString, Point >>> s = geopandas.GeoSeries( ... [ ... LineString([(0, 0), (2, 0), (0, 2)]), ... LineString([(0, 0), (2, 2)]), ... LineString([(2, 0), (0, 2)]), ... ], ... ) >>> s2 = geopandas.GeoSeries( ... [ ... Point(1, 0), ... Point(1, 0), ... Point(2, 1), ... ], ... index=range(1, 4), ... ) >>> s 0 LINESTRING (0.00000 0.00000, 2.00000 0.00000, ... 1 LINESTRING (0.00000 0.00000, 2.00000 2.00000) 2 LINESTRING (2.00000 0.00000, 0.00000 2.00000) dtype: geometry >>> s2 1 POINT (1.00000 0.00000) 2 POINT (1.00000 0.00000) 3 POINT (2.00000 1.00000) dtype: geometry We can project each geometry on a single shapely geometry: .. image:: ../../../_static/binary_op-03.svg :align: center >>> s.project(Point(1, 0)) 0 1.000000 1 0.707107 2 0.707107 dtype: float64 We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and project elements with the same index using ``align=True`` or ignore index and project elements based on their matching order using ``align=False``: .. image:: ../../../_static/binary_op-02.svg >>> s.project(s2, align=True) 0 NaN 1 0.707107 2 0.707107 3 NaN dtype: float64 >>> s.project(s2, align=False) 0 1.000000 1 0.707107 2 0.707107 dtype: float64 See also -------- GeoSeries.interpolate """ return _binary_op("project", self, other, normalized=normalized, align=align) def interpolate(self, distance, normalized=False): """ Return a point at the specified distance along each geometry Parameters ---------- distance : float or Series of floats Distance(s) along the geometries at which a point should be returned. If np.array or pd.Series are used then it must have same length as the GeoSeries. normalized : boolean If normalized is True, distance will be interpreted as a fraction of the geometric object's length. """ if isinstance(distance, pd.Series): if not self.index.equals(distance.index): raise ValueError( "Index values of distance sequence does " "not match index values of the GeoSeries" ) distance = np.asarray(distance) return _delegate_geo_method( "interpolate", self, distance, normalized=normalized ) def affine_transform(self, matrix): """Return a ``GeoSeries`` with translated geometries. See http://shapely.readthedocs.io/en/stable/manual.html#shapely.affinity.affine_transform for details. Parameters ---------- matrix: List or tuple 6 or 12 items for 2D or 3D transformations respectively. For 2D affine transformations, the 6 parameter matrix is ``[a, b, d, e, xoff, yoff]`` For 3D affine transformations, the 12 parameter matrix is ``[a, b, c, d, e, f, g, h, i, xoff, yoff, zoff]`` Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1.00000 1.00000) 1 LINESTRING (1.00000 -1.00000, 1.00000 0.00000) 2 POLYGON ((3.00000 -1.00000, 4.00000 0.00000, 3... dtype: geometry >>> s.affine_transform([2, 3, 2, 4, 5, 2]) 0 POINT (10.00000 8.00000) 1 LINESTRING (4.00000 0.00000, 7.00000 4.00000) 2 POLYGON ((8.00000 4.00000, 13.00000 10.00000, ... dtype: geometry """ # noqa (E501 link is longer than max line length) return _delegate_geo_method("affine_transform", self, matrix) def translate(self, xoff=0.0, yoff=0.0, zoff=0.0): """Returns a ``GeoSeries`` with translated geometries. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.translate for details. Parameters ---------- xoff, yoff, zoff : float, float, float Amount of offset along each dimension. xoff, yoff, and zoff for translation along the x, y, and z dimensions respectively. Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1.00000 1.00000) 1 LINESTRING (1.00000 -1.00000, 1.00000 0.00000) 2 POLYGON ((3.00000 -1.00000, 4.00000 0.00000, 3... dtype: geometry >>> s.translate(2, 3) 0 POINT (3.00000 4.00000) 1 LINESTRING (3.00000 2.00000, 3.00000 3.00000) 2 POLYGON ((5.00000 2.00000, 6.00000 3.00000, 5.... dtype: geometry """ # noqa (E501 link is longer than max line length) return _delegate_geo_method("translate", self, xoff, yoff, zoff) def rotate(self, angle, origin="center", use_radians=False): """Returns a ``GeoSeries`` with rotated geometries. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.rotate for details. Parameters ---------- angle : float The angle of rotation can be specified in either degrees (default) or radians by setting use_radians=True. Positive angles are counter-clockwise and negative are clockwise rotations. origin : string, Point, or tuple (x, y) The point of origin can be a keyword 'center' for the bounding box center (default), 'centroid' for the geometry's centroid, a Point object or a coordinate tuple (x, y). use_radians : boolean Whether to interpret the angle of rotation as degrees or radians Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1.00000 1.00000) 1 LINESTRING (1.00000 -1.00000, 1.00000 0.00000) 2 POLYGON ((3.00000 -1.00000, 4.00000 0.00000, 3... dtype: geometry >>> s.rotate(90) 0 POINT (1.00000 1.00000) 1 LINESTRING (1.50000 -0.50000, 0.50000 -0.50000) 2 POLYGON ((4.50000 -0.50000, 3.50000 0.50000, 2... dtype: geometry >>> s.rotate(90, origin=(0, 0)) 0 POINT (-1.00000 1.00000) 1 LINESTRING (1.00000 1.00000, 0.00000 1.00000) 2 POLYGON ((1.00000 3.00000, 0.00000 4.00000, -1... dtype: geometry """ return _delegate_geo_method( "rotate", self, angle, origin=origin, use_radians=use_radians ) def scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin="center"): """Returns a ``GeoSeries`` with scaled geometries. The geometries can be scaled by different factors along each dimension. Negative scale factors will mirror or reflect coordinates. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.scale for details. Parameters ---------- xfact, yfact, zfact : float, float, float Scaling factors for the x, y, and z dimensions respectively. origin : string, Point, or tuple The point of origin can be a keyword 'center' for the 2D bounding box center (default), 'centroid' for the geometry's 2D centroid, a Point object or a coordinate tuple (x, y, z). Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1.00000 1.00000) 1 LINESTRING (1.00000 -1.00000, 1.00000 0.00000) 2 POLYGON ((3.00000 -1.00000, 4.00000 0.00000, 3... dtype: geometry >>> s.scale(2, 3) 0 POINT (1.00000 1.00000) 1 LINESTRING (1.00000 -2.00000, 1.00000 1.00000) 2 POLYGON ((2.50000 -3.00000, 4.50000 0.00000, 2... dtype: geometry >>> s.scale(2, 3, origin=(0, 0)) 0 POINT (2.00000 3.00000) 1 LINESTRING (2.00000 -3.00000, 2.00000 0.00000) 2 POLYGON ((6.00000 -3.00000, 8.00000 0.00000, 6... dtype: geometry """ return _delegate_geo_method("scale", self, xfact, yfact, zfact, origin=origin) def skew(self, xs=0.0, ys=0.0, origin="center", use_radians=False): """Returns a ``GeoSeries`` with skewed geometries. The geometries are sheared by angles along the x and y dimensions. See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.skew for details. Parameters ---------- xs, ys : float, float The shear angle(s) for the x and y axes respectively. These can be specified in either degrees (default) or radians by setting use_radians=True. origin : string, Point, or tuple (x, y) The point of origin can be a keyword 'center' for the bounding box center (default), 'centroid' for the geometry's centroid, a Point object or a coordinate tuple (x, y). use_radians : boolean Whether to interpret the shear angle(s) as degrees or radians Examples -------- >>> from shapely.geometry import Point, LineString, Polygon >>> s = geopandas.GeoSeries( ... [ ... Point(1, 1), ... LineString([(1, -1), (1, 0)]), ... Polygon([(3, -1), (4, 0), (3, 1)]), ... ] ... ) >>> s 0 POINT (1.00000 1.00000) 1 LINESTRING (1.00000 -1.00000, 1.00000 0.00000) 2 POLYGON ((3.00000 -1.00000, 4.00000 0.00000, 3... dtype: geometry >>> s.skew(45, 30) 0 POINT (1.00000 1.00000) 1 LINESTRING (0.50000 -1.00000, 1.50000 0.00000) 2 POLYGON ((2.00000 -1.28868, 4.00000 0.28868, 4... dtype: geometry >>> s.skew(45, 30, origin=(0, 0)) 0 POINT (2.00000 1.57735) 1 LINESTRING (0.00000 -0.42265, 1.00000 0.57735) 2 POLYGON ((2.00000 0.73205, 4.00000 2.30940, 4.... dtype: geometry """ return _delegate_geo_method( "skew", self, xs, ys, origin=origin, use_radians=use_radians ) @property def cx(self): """ Coordinate based indexer to select by intersection with bounding box. Format of input should be ``.cx[xmin:xmax, ymin:ymax]``. Any of ``xmin``, ``xmax``, ``ymin``, and ``ymax`` can be provided, but input must include a comma separating x and y slices. That is, ``.cx[:, :]`` will return the full series/frame, but ``.cx[:]`` is not implemented. Examples -------- >>> from shapely.geometry import LineString, Point >>> s = geopandas.GeoSeries( ... [Point(0, 0), Point(1, 2), Point(3, 3), LineString([(0, 0), (3, 3)])] ... ) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 2.00000) 2 POINT (3.00000 3.00000) 3 LINESTRING (0.00000 0.00000, 3.00000 3.00000) dtype: geometry >>> s.cx[0:1, 0:1] 0 POINT (0.00000 0.00000) 3 LINESTRING (0.00000 0.00000, 3.00000 3.00000) dtype: geometry >>> s.cx[:, 1:] 1 POINT (1.00000 2.00000) 2 POINT (3.00000 3.00000) 3 LINESTRING (0.00000 0.00000, 3.00000 3.00000) dtype: geometry """ return _CoordinateIndexer(self) def equals(self, other): """ Test whether two objects contain the same elements. This function allows two GeoSeries or GeoDataFrames to be compared against each other to see if they have the same shape and elements. Missing values in the same location are considered equal. The row/column index do not need to have the same type (as long as the values are still considered equal), but the dtypes of the respective columns must be the same. Parameters ---------- other : GeoSeries or GeoDataFrame The other GeoSeries or GeoDataFrame to be compared with the first. Returns ------- bool True if all elements are the same in both objects, False otherwise. """ # we override this because pandas is using `self._constructor` in the # isinstance check (https://github.com/geopandas/geopandas/issues/1420) if not isinstance(other, type(self)): return False return self._data.equals(other._data) class _CoordinateIndexer(object): # see docstring GeoPandasBase.cx property above def __init__(self, obj): self.obj = obj def __getitem__(self, key): obj = self.obj xs, ys = key # handle numeric values as x and/or y coordinate index if type(xs) is not slice: xs = slice(xs, xs) if type(ys) is not slice: ys = slice(ys, ys) # don't know how to handle step; should this raise? if xs.step is not None or ys.step is not None: warn("Ignoring step - full interval is used.") if xs.start is None or xs.stop is None or ys.start is None or ys.stop is None: xmin, ymin, xmax, ymax = obj.total_bounds bbox = box( xs.start if xs.start is not None else xmin, ys.start if ys.start is not None else ymin, xs.stop if xs.stop is not None else xmax, ys.stop if ys.stop is not None else ymax, ) idx = obj.intersects(bbox) return obj[idx] geopandas-0.10.2/geopandas/conftest.py000066400000000000000000000012661413247402600177030ustar00rootroot00000000000000import pytest import geopandas @pytest.fixture(autouse=True) def add_geopandas(doctest_namespace): doctest_namespace["geopandas"] = geopandas def pytest_configure(config): config.addinivalue_line( "markers", "skip_no_sindex: skips the tests if there is no spatial index backend", ) try: geopandas.sindex._get_sindex_class() has_sindex_backend = True except ImportError: has_sindex_backend = False def pytest_runtest_setup(item): skip_no_sindex = any(mark for mark in item.iter_markers(name="skip_no_sindex")) if skip_no_sindex and not has_sindex_backend: pytest.skip("Skipped because there is no spatial index backend available") geopandas-0.10.2/geopandas/datasets/000077500000000000000000000000001413247402600173075ustar00rootroot00000000000000geopandas-0.10.2/geopandas/datasets/README.md000066400000000000000000000026051413247402600205710ustar00rootroot00000000000000# Datasets included with geopandas - `'nybb'`: Borough boundaries of New York City, data provided Department of City Planning (DCP), https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm - `'naturalearth_cities'`: capital cities, based on http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ - `'naturalearth_lowres'`: country boundaries, based on http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/ ## Data sources and licenses ### `nybb` #### Summary These districts were created by the Department of City Planning to aid city agencies in administering public services. #### Description The borough boundaries of New York City clipped to the shoreline at mean high tide. #### Credits Department of City Planning. #### Use limitations This dataset is being provided by the Department of City Planning (DCP) on DCP’s website for informational purposes only. DCP does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of the dataset, nor are any such warranties to be implied or inferred with respect to the dataset as furnished on the website. DCP and the City are not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use the dataset, or applications utilizing the dataset, provided by any third party. geopandas-0.10.2/geopandas/datasets/__init__.py000066400000000000000000000022111413247402600214140ustar00rootroot00000000000000import os __all__ = ["available", "get_path"] _module_path = os.path.dirname(__file__) _available_dir = [p for p in next(os.walk(_module_path))[1] if not p.startswith("__")] _available_zip = {"nybb": "nybb_16a.zip"} available = _available_dir + list(_available_zip.keys()) def get_path(dataset): """ Get the path to the data file. Parameters ---------- dataset : str The name of the dataset. See ``geopandas.datasets.available`` for all options. Examples -------- >>> geopandas.datasets.get_path("naturalearth_lowres") # doctest: +SKIP '.../python3.8/site-packages/geopandas/datasets/\ naturalearth_lowres/naturalearth_lowres.shp' """ if dataset in _available_dir: return os.path.abspath(os.path.join(_module_path, dataset, dataset + ".shp")) elif dataset in _available_zip: fpath = os.path.abspath(os.path.join(_module_path, _available_zip[dataset])) return "zip://" + fpath else: msg = "The dataset '{data}' is not available. ".format(data=dataset) msg += "Available datasets are {}".format(", ".join(available)) raise ValueError(msg) geopandas-0.10.2/geopandas/datasets/naturalearth_cities/000077500000000000000000000000001413247402600233415ustar00rootroot00000000000000geopandas-0.10.2/geopandas/datasets/naturalearth_cities/naturalearth_cities.README.html000066400000000000000000000765571413247402600312410ustar00rootroot00000000000000 Populated Places | Natural Earth
« 1:10m Cultural Vectors 

Populated Places

pop_thumb
City and town points, from Tokyo to Wasilla, Cairo to Kandahar
Download populated places (1.51 MB) version 2.0.0
Download simple (less columns) (719.87 KB) version 2.0.0

About

Point symbols with name attributes. Includes all admin-0 and many admin-1 capitals, major cities and towns, plus a sampling of smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining our selection of places. Use the scale rankings to filter the number of towns that appear on your map.

pop_banner

LandScan derived population estimates are provided for 90% of our cities. Those lacking population estimates are often in sparsely inhabited areas. We provide a range of population values that account for the total “metropolitan” population rather than it’s administrative boundary population. Use the PopMax column to size your town labels. Starting in version 1.1, popMax has been throttled down to the UN estimated metro population for the ~500 largest urban areas in the world. This affects towns in China, India, and parts of Africa where our Landscan counting method usually over estimated.

Population estimates were derived from the LANDSCAN dataset maintained and distributed by the Oak Ridge National Laboratory. These data were converted from raster to vector and pixels with fewer than 200 persons per square kilometer were removed from the dataset as they were classified as rural. Once urban pixels were selected, these pixels were aggregated into contiguous units. Concurrently Thiessen polygons were created based on the selected city points. The Thiessen polygons were used to intersect the contiguous city boundaries to produce bounded areas for the cities. As a result, our estimates capture a metropolitan and micropolitan populations per city regardless of administrative units.

Once intersected, the contiguous polygons were recalculated, using aerial interpolation assuming uniform population distribution within each pixel, to determine the population total. This process was conducted multiple times, for each scale level, to produce population estimates for each city at nested scales of 1:300 million, 1:110 million, 1:50 million, 1:20 million, and 1:10 million.

Download landscan urban areas (13.01 MB) version 2.0.0

Population ranks

Are calculated as rank_max and rank_min using this general VB formula that can be pasted into ArcMap Field Calculator advanced area (set your output to x):

a = [pop_max]

if( a > 10000000 ) then x = 14 elseif( a > 5000000 ) then x = 13 elseif( a > 1000000 ) then x = 12 elseif( a > 500000 ) then x = 11 elseif( a > 200000 ) then x = 10 elseif( a > 100000 ) then x = 9 elseif( a > 50000 ) then x = 8 elseif( a > 20000 ) then x = 7 elseif( a > 10000 ) then x = 6 elseif( a > 5000 ) then x = 5 elseif( a > 2000 ) then x = 4 elseif( a > 1000 ) then x = 3 elseif( a > 200 ) then x = 2 elseif( a > 0 ) then x = 1 else x = 0 end if

Issues

While we don’t want to show every admin-1 capital, for those countries where we show most admin-1 capitals, we should have a complete set. If you find we are missing one, please log it in the Cx tool at right.

Version History

  • 2.0.0
  • 1.4.0
  • 1.3.0
  • 1.1.0
  • 0.9.0

The master changelog is available on Github »

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geopandas-0.10.2/geopandas/datasets/naturalearth_cities/naturalearth_cities.VERSION.txt000066400000000000000000000000051413247402600312530ustar00rootroot000000000000002.0.0geopandas-0.10.2/geopandas/datasets/naturalearth_cities/naturalearth_cities.cpg000066400000000000000000000000121413247402600300570ustar00rootroot00000000000000ISO-8859-1geopandas-0.10.2/geopandas/datasets/naturalearth_cities/naturalearth_cities.dbf000066400000000000000000000400531413247402600300520ustar00rootroot00000000000000_AQnameCP Vatican City San Marino Vaduz Luxembourg Palikir Majuro Funafuti Melekeok Monaco Tarawa Moroni Andorra Port-of-Spain Kigali Mbabane Juba Ljubljana Bratislava Doha Podgorica Bern Pristina Roseau Djibouti Banjul Skopje Bridgetown Bujumbura Kingstown Castries Basseterre Port Louis Saint George's Manama Saint John's Montevideo Lome Tunis Abu Dhabi Ashgabat Lusaka Harare Dili Port Vila Tegucigalpa Georgetown Reykjavk Port-au-Prince Kampala Paramaribo Niamey Dushanbe Asuncion Managua Freetown Islamabad Kathmandu Bloemfontein Pretoria Port Moresby Honiara Panama City Rabat Chisinau Maputo Mogadishu Muscat Colombo Ulaanbaatar Wellington Windhoek Abuja Bissau Amman Vilnius Riga Bishkek Maseru Antananarivo Quito San Jose San Salvador Kingston Ndjamena Malabo Asmara Zagreb Tallinn Lilongwe Guatemala Libreville Suva Nouakchott Bamako Beirut Tbilisi Astana Vientiane Brazzaville Conakry Yamoussoukro Ottawa Belgrade Bandar Seri Begawan Sucre Belmopan Bangui Yaounde Tirana Yerevan Baku Phnom Penh La Paz Cotonou Sofia Minsk Thimphu Gaborone Canberra Ouagadougou Sarajevo Naypyidaw Nukualofa Hargeysa Victoria Sao Tome Apia Valletta Male Jerusalem Praia Nassau Nicosia Hanoi Ankara Budapest Sanaa Bucharest Damascus Lisbon Khartoum Oslo Warsaw Pyongyang Dar es Salaam Dublin Monrovia Kuala Lumpur Havana Prague Kuwait Santo Domingo Accra Tripoli Helsinki Kbenhavn Abidjan Brasilia Brussels Dhaka Luanda Algiers Rangoon Caracas Kiev Tashkent Madrid Stockholm Bangkok Lima Dakar Johannesburg Amsterdam Seoul Manila Berlin Kinshasa New Delhi Athens Baghdad Addis Ababa Tehran Buenos Aires Kabul Vienna Taipei Washington, D.C. 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Raw data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/ Current version used: version 4.1.0 """ # noqa (E501 link is longer than max line length) import geopandas as gpd # assumes zipfile from naturalearthdata was downloaded to current directory world_raw = gpd.read_file("zip://./ne_110m_admin_0_countries.zip") # subsets columns of interest for geopandas examples world_df = world_raw[ ["POP_EST", "CONTINENT", "NAME", "ISO_A3", "GDP_MD_EST", "geometry"] ] world_df.columns = world_df.columns.str.lower() world_df.to_file( driver="ESRI Shapefile", filename="./naturalearth_lowres/naturalearth_lowres.shp" ) geopandas-0.10.2/geopandas/datasets/naturalearth_lowres/000077500000000000000000000000001413247402600233745ustar00rootroot00000000000000geopandas-0.10.2/geopandas/datasets/naturalearth_lowres/naturalearth_lowres.cpg000066400000000000000000000000121413247402600301450ustar00rootroot00000000000000ISO-8859-1geopandas-0.10.2/geopandas/datasets/naturalearth_lowres/naturalearth_lowres.dbf000066400000000000000000001373451413247402600301530ustar00rootroot00000000000000v pop_estN continentCPnameCPiso_a3CPgdp_md_estN 920938Oceania Fiji FJI 8374.000000000000000 53950935Africa Tanzania TZA 150600.000000000000000 603253Africa W. Sahara ESH 906.500000000000000 35623680North America Canada CAN 1674000.000000000000000 326625791North America United States of America USA 18560000.000000000000000 18556698Asia Kazakhstan KAZ 460700.000000000000000 29748859Asia Uzbekistan UZB 202300.000000000000000 6909701Oceania Papua New Guinea PNG 28020.000000000000000 260580739Asia Indonesia IDN 3028000.000000000000000 44293293South America Argentina ARG 879400.000000000000000 17789267South America Chile CHL 436100.000000000000000 83301151Africa Dem. Rep. Congo COD 66010.000000000000000 7531386Africa Somalia SOM 4719.000000000000000 47615739Africa Kenya KEN 152700.000000000000000 37345935Africa Sudan SDN 176300.000000000000000 12075985Africa Chad TCD 30590.000000000000000 10646714North America Haiti HTI 19340.000000000000000 10734247North America Dominican Rep. DOM 161900.000000000000000 142257519Europe Russia RUS 3745000.000000000000000 329988North America Bahamas BHS 9066.000000000000000 2931South America Falkland Is. FLK 281.800000000000011 5320045Europe Norway -99 364700.000000000000000 57713North America Greenland GRL 2173.000000000000000 140Seven seas (open ocean) Fr. S. Antarctic Lands ATF 16.000000000000000 1291358Asia Timor-Leste TLS 4975.000000000000000 54841552Africa South Africa ZAF 739100.000000000000000 1958042Africa Lesotho LSO 6019.000000000000000 124574795North America Mexico MEX 2307000.000000000000000 3360148South America Uruguay URY 73250.000000000000000 207353391South America Brazil BRA 3081000.000000000000000 11138234South America Bolivia BOL 78350.000000000000000 31036656South America Peru PER 410400.000000000000000 47698524South America Colombia COL 688000.000000000000000 3753142North America Panama PAN 93120.000000000000000 4930258North America Costa Rica CRI 79260.000000000000000 6025951North America Nicaragua NIC 33550.000000000000000 9038741North America Honduras HND 43190.000000000000000 6172011North America El Salvador SLV 54790.000000000000000 15460732North America Guatemala GTM 131800.000000000000000 360346North America Belize BLZ 3088.000000000000000 31304016South America Venezuela VEN 468600.000000000000000 737718South America Guyana GUY 6093.000000000000000 591919South America Suriname SUR 8547.000000000000000 67106161Europe France -99 2699000.000000000000000 16290913South America Ecuador ECU 182400.000000000000000 3351827North America Puerto Rico PRI 131000.000000000000000 2990561North America Jamaica JAM 25390.000000000000000 11147407North America Cuba CUB 132900.000000000000000 13805084Africa Zimbabwe ZWE 28330.000000000000000 2214858Africa Botswana BWA 35900.000000000000000 2484780Africa Namibia NAM 25990.000000000000000 14668522Africa Senegal SEN 39720.000000000000000 17885245Africa Mali MLI 38090.000000000000000 3758571Africa Mauritania MRT 16710.000000000000000 11038805Africa Benin BEN 24310.000000000000000 19245344Africa Niger NER 20150.000000000000000 190632261Africa Nigeria NGA 1089000.000000000000000 24994885Africa Cameroon CMR 77240.000000000000000 7965055Africa Togo TGO 11610.000000000000000 27499924Africa Ghana GHA 120800.000000000000000 24184810Africa Cte d'Ivoire CIV 87120.000000000000000 12413867Africa Guinea GIN 16080.000000000000000 1792338Africa Guinea-Bissau GNB 2851.000000000000000 4689021Africa Liberia LBR 3881.000000000000000 6163195Africa Sierra Leone SLE 10640.000000000000000 20107509Africa Burkina Faso BFA 32990.000000000000000 5625118Africa Central African Rep. CAF 3206.000000000000000 4954674Africa Congo COG 30270.000000000000000 1772255Africa Gabon GAB 35980.000000000000000 778358Africa Eq. Guinea GNQ 31770.000000000000000 15972000Africa Zambia ZMB 65170.000000000000000 19196246Africa Malawi MWI 21200.000000000000000 26573706Africa Mozambique MOZ 35010.000000000000000 1467152Africa eSwatini SWZ 11060.000000000000000 29310273Africa Angola AGO 189000.000000000000000 11466756Africa Burundi BDI 7892.000000000000000 8299706Asia Israel ISR 297000.000000000000000 6229794Asia Lebanon LBN 85160.000000000000000 25054161Africa Madagascar MDG 36860.000000000000000 4543126Asia Palestine PSE 21220.770000000000437 2051363Africa Gambia GMB 3387.000000000000000 11403800Africa Tunisia TUN 130800.000000000000000 40969443Africa Algeria DZA 609400.000000000000000 10248069Asia Jordan JOR 86190.000000000000000 6072475Asia United Arab Emirates ARE 667200.000000000000000 2314307Asia Qatar QAT 334500.000000000000000 2875422Asia Kuwait KWT 301100.000000000000000 39192111Asia Iraq IRQ 596700.000000000000000 3424386Asia Oman OMN 173100.000000000000000 282814Oceania Vanuatu VUT 723.000000000000000 16204486Asia Cambodia KHM 58940.000000000000000 68414135Asia Thailand THA 1161000.000000000000000 7126706Asia Laos LAO 40960.000000000000000 55123814Asia Myanmar MMR 311100.000000000000000 96160163Asia Vietnam VNM 594900.000000000000000 25248140Asia North Korea PRK 40000.000000000000000 51181299Asia South Korea KOR 1929000.000000000000000 3068243Asia Mongolia MNG 37000.000000000000000 1281935911Asia India IND 8721000.000000000000000 157826578Asia Bangladesh BGD 628400.000000000000000 758288Asia Bhutan BTN 6432.000000000000000 29384297Asia Nepal NPL 71520.000000000000000 204924861Asia Pakistan PAK 988200.000000000000000 34124811Asia Afghanistan AFG 64080.000000000000000 8468555Asia Tajikistan TJK 25810.000000000000000 5789122Asia Kyrgyzstan KGZ 21010.000000000000000 5351277Asia Turkmenistan TKM 94720.000000000000000 82021564Asia Iran IRN 1459000.000000000000000 18028549Asia Syria SYR 50280.000000000000000 3045191Asia Armenia ARM 26300.000000000000000 9960487Europe Sweden SWE 498100.000000000000000 9549747Europe Belarus BLR 165400.000000000000000 44033874Europe Ukraine UKR 352600.000000000000000 38476269Europe Poland POL 1052000.000000000000000 8754413Europe Austria AUT 416600.000000000000000 9850845Europe Hungary HUN 267600.000000000000000 3474121Europe Moldova MDA 18540.000000000000000 21529967Europe Romania ROU 441000.000000000000000 2823859Europe Lithuania LTU 85620.000000000000000 1944643Europe Latvia LVA 50650.000000000000000 1251581Europe Estonia EST 38700.000000000000000 80594017Europe Germany DEU 3979000.000000000000000 7101510Europe Bulgaria BGR 143100.000000000000000 10768477Europe Greece GRC 290500.000000000000000 80845215Asia Turkey TUR 1670000.000000000000000 3047987Europe Albania ALB 33900.000000000000000 4292095Europe Croatia HRV 94240.000000000000000 8236303Europe Switzerland CHE 496300.000000000000000 594130Europe Luxembourg LUX 58740.000000000000000 11491346Europe Belgium BEL 508600.000000000000000 17084719Europe Netherlands NLD 870800.000000000000000 10839514Europe Portugal PRT 297100.000000000000000 48958159Europe Spain ESP 1690000.000000000000000 5011102Europe Ireland IRL 322000.000000000000000 279070Oceania New Caledonia NCL 10770.000000000000000 647581Oceania Solomon Is. 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ӔD0f eL " hSz~˗ +ڸa$ugqJ́'(Qr#YyH[/ F>*qoaBdY\BAȅ\۞\%ͫ,8PӢgc*!ʡ“c_cUA[4|Q׵mr4cSI%{/d 7 Q{Ʒy4ǜdߜ_Ǭ6Qg#3ݲ {.TSEQ?t̆z,Y*8$ |LWh8!_A Ig̘FN(-N:1fU Ō- ۖK}=GC.0Flz0tơaT$%(%&cw!_;L',!* BJfA^ybNM H1a{dB8(8~yKS߆2^!a瑗u㏊M.`9TUI)z'b#xUHu$XZ TwM,wY)ul 9eIpB ׾DkuN ^eu^lN{\V`7;u`FOGFjFv 8,Iŭgy.zV%AFS$g[Ġ ȸ-]tD<7*ypdKc@ E _Ή}CCRTPR.NC WM |hD&/^0v_tza7Ճ9iWQԞ0Ŝ__?E.&c(,e90*|U~E9)KCp-G^Oo:ifivj.RU&WA"vP ʙ*,gczt t%ΨmШ흧SOJ`X 5;=Z&`SY)^hp==^,$,>웤cN(/'0^yz#q~PKVWH9Wnybb.shxUT ,V2lXux c`Pb^030i;Q'9/FS#8##FI1 a`jӹ9 N 58PKVWH{Ynybb.dbfUT,Vux PKVWHŅVI2:nybb.prjUT,Vux PKVWH= nybb.shpUT,Vux PKVWHq{T2  nybb.shp.xmlUT,Vux PKVWH9W nybb.shxUT,Vux PK geopandas-0.10.2/geopandas/explore.py000066400000000000000000000771421413247402600175420ustar00rootroot00000000000000from statistics import mean import geopandas from shapely.geometry import LineString import numpy as np import pandas as pd _MAP_KWARGS = [ "location", "prefer_canvas", "no_touch", "disable_3d", "png_enabled", "zoom_control", "crs", "zoom_start", "left", "top", "position", "min_zoom", "max_zoom", "min_lat", "max_lat", "min_lon", "max_lon", "max_bounds", ] def _explore( df, column=None, cmap=None, color=None, m=None, tiles="OpenStreetMap", attr=None, tooltip=True, popup=False, highlight=True, categorical=False, legend=True, scheme=None, k=5, vmin=None, vmax=None, width="100%", height="100%", categories=None, classification_kwds=None, control_scale=True, marker_type=None, marker_kwds={}, style_kwds={}, highlight_kwds={}, missing_kwds={}, tooltip_kwds={}, popup_kwds={}, legend_kwds={}, **kwargs, ): """Interactive map based on GeoPandas and folium/leaflet.js Generate an interactive leaflet map based on :class:`~geopandas.GeoDataFrame` Parameters ---------- column : str, np.array, pd.Series (default None) The name of the dataframe column, :class:`numpy.array`, or :class:`pandas.Series` to be plotted. If :class:`numpy.array` or :class:`pandas.Series` are used then it must have same length as dataframe. cmap : str, matplotlib.Colormap, branca.colormap or function (default None) The name of a colormap recognized by ``matplotlib``, a list-like of colors, :class:`matplotlib.colors.Colormap`, a :class:`branca.colormap.ColorMap` or function that returns a named color or hex based on the column value, e.g.:: def my_colormap(value): # scalar value defined in 'column' if value > 1: return "green" return "red" color : str, array-like (default None) Named color or a list-like of colors (named or hex). m : folium.Map (default None) Existing map instance on which to draw the plot. tiles : str, xyzservices.TileProvider (default 'OpenStreetMap Mapnik') Map tileset to use. Can choose from the list supported by folium, query a :class:`xyzservices.TileProvider` by a name from ``xyzservices.providers``, pass :class:`xyzservices.TileProvider` object or pass custom XYZ URL. The current list of built-in providers (when ``xyzservices`` is not available): ``["OpenStreetMap", "Stamen Terrain", “Stamen Toner", “Stamen Watercolor" "CartoDB positron", “CartoDB dark_matter"]`` You can pass a custom tileset to Folium by passing a Leaflet-style URL to the tiles parameter: ``http://{s}.yourtiles.com/{z}/{x}/{y}.png``. Be sure to check their terms and conditions and to provide attribution with the ``attr`` keyword. attr : str (default None) Map tile attribution; only required if passing custom tile URL. tooltip : bool, str, int, list (default True) Display GeoDataFrame attributes when hovering over the object. ``True`` includes all columns. ``False`` removes tooltip. Pass string or list of strings to specify a column(s). Integer specifies first n columns to be included. Defaults to ``True``. popup : bool, str, int, list (default False) Input GeoDataFrame attributes for object displayed when clicking. ``True`` includes all columns. ``False`` removes popup. Pass string or list of strings to specify a column(s). Integer specifies first n columns to be included. Defaults to ``False``. highlight : bool (default True) Enable highlight functionality when hovering over a geometry. categorical : bool (default False) If ``False``, ``cmap`` will reflect numerical values of the column being plotted. For non-numerical columns, this will be set to True. legend : bool (default True) Plot a legend in choropleth plots. Ignored if no ``column`` is given. scheme : str (default None) Name of a choropleth classification scheme (requires ``mapclassify`` >= 2.4.0). A :func:`mapclassify.classify` will be used under the hood. Supported are all schemes provided by ``mapclassify`` (e.g. ``'BoxPlot'``, ``'EqualInterval'``, ``'FisherJenks'``, ``'FisherJenksSampled'``, ``'HeadTailBreaks'``, ``'JenksCaspall'``, ``'JenksCaspallForced'``, ``'JenksCaspallSampled'``, ``'MaxP'``, ``'MaximumBreaks'``, ``'NaturalBreaks'``, ``'Quantiles'``, ``'Percentiles'``, ``'StdMean'``, ``'UserDefined'``). Arguments can be passed in ``classification_kwds``. k : int (default 5) Number of classes vmin : None or float (default None) Minimum value of ``cmap``. If ``None``, the minimum data value in the column to be plotted is used. vmax : None or float (default None) Maximum value of ``cmap``. If ``None``, the maximum data value in the column to be plotted is used. width : pixel int or percentage string (default: '100%') Width of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, width is ignored. height : pixel int or percentage string (default: '100%') Height of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, height is ignored. categories : list-like Ordered list-like object of categories to be used for categorical plot. classification_kwds : dict (default None) Keyword arguments to pass to mapclassify control_scale : bool, (default True) Whether to add a control scale on the map. marker_type : str, folium.Circle, folium.CircleMarker, folium.Marker (default None) Allowed string options are ('marker', 'circle', 'circle_marker'). Defaults to folium.CircleMarker. marker_kwds: dict (default {}) Additional keywords to be passed to the selected ``marker_type``, e.g.: radius : float (default 2 for ``circle_marker`` and 50 for ``circle``)) Radius of the circle, in meters (for ``circle``) or pixels (for ``circle_marker``). fill : bool (default True) Whether to fill the ``circle`` or ``circle_marker`` with color. icon : folium.map.Icon the :class:`folium.map.Icon` object to use to render the marker. draggable : bool (default False) Set to True to be able to drag the marker around the map. style_kwds : dict (default {}) Additional style to be passed to folium ``style_function``: stroke : bool (default True) Whether to draw stroke along the path. Set it to ``False`` to disable borders on polygons or circles. color : str Stroke color weight : int Stroke width in pixels opacity : float (default 1.0) Stroke opacity fill : boolean (default True) Whether to fill the path with color. Set it to ``False`` to disable filling on polygons or circles. fillColor : str Fill color. Defaults to the value of the color option fillOpacity : float (default 0.5) Fill opacity. Plus all supported by :func:`folium.vector_layers.path_options`. See the documentation of :class:`folium.features.GeoJson` for details. highlight_kwds : dict (default {}) Style to be passed to folium highlight_function. Uses the same keywords as ``style_kwds``. When empty, defaults to ``{"fillOpacity": 0.75}``. tooltip_kwds : dict (default {}) Additional keywords to be passed to :class:`folium.features.GeoJsonTooltip`, e.g. ``aliases``, ``labels``, or ``sticky``. popup_kwds : dict (default {}) Additional keywords to be passed to :class:`folium.features.GeoJsonPopup`, e.g. ``aliases`` or ``labels``. legend_kwds : dict (default {}) Additional keywords to be passed to the legend. Currently supported customisation: caption : string Custom caption of the legend. Defaults to the column name. Additional accepted keywords when ``scheme`` is specified: colorbar : bool (default True) An option to control the style of the legend. If True, continuous colorbar will be used. If False, categorical legend will be used for bins. scale : bool (default True) Scale bins along the colorbar axis according to the bin edges (True) or use the equal length for each bin (False) fmt : string (default "{:.2f}") A formatting specification for the bin edges of the classes in the legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``. Applies if ``colorbar=False``. labels : list-like A list of legend labels to override the auto-generated labels. Needs to have the same number of elements as the number of classes (`k`). Applies if ``colorbar=False``. interval : boolean (default False) An option to control brackets from mapclassify legend. If True, open/closed interval brackets are shown in the legend. Applies if ``colorbar=False``. max_labels : int, default 10 Maximum number of colorbar tick labels (requires branca>=0.5.0) **kwargs : dict Additional options to be passed on to the folium object. Returns ------- m : folium.folium.Map folium :class:`~folium.folium.Map` instance Examples -------- >>> df = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres")) >>> df.head(2) # doctest: +SKIP pop_est continent name iso_a3 \ gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 MULTIPOLY\ GON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 POLYGON (\ (33.90371 -0.95000, 34.07262 -1.05982... >>> df.explore("pop_est", cmap="Blues") # doctest: +SKIP """ try: import branca as bc import folium import matplotlib.cm as cm import matplotlib.colors as colors import matplotlib.pyplot as plt from mapclassify import classify except (ImportError, ModuleNotFoundError): raise ImportError( "The 'folium', 'matplotlib' and 'mapclassify' packages are required for " "'explore()'. You can install them using " "'conda install -c conda-forge folium matplotlib mapclassify' " "or 'pip install folium matplotlib mapclassify'." ) # xyservices is an optional dependency try: import xyzservices HAS_XYZSERVICES = True except (ImportError, ModuleNotFoundError): HAS_XYZSERVICES = False gdf = df.copy() # convert LinearRing to LineString rings_mask = df.geom_type == "LinearRing" if rings_mask.any(): gdf.geometry[rings_mask] = gdf.geometry[rings_mask].apply( lambda g: LineString(g) ) if gdf.crs is None: kwargs["crs"] = "Simple" tiles = None elif not gdf.crs.equals(4326): gdf = gdf.to_crs(4326) # create folium.Map object if m is None: # Get bounds to specify location and map extent bounds = gdf.total_bounds location = kwargs.pop("location", None) if location is None: x = mean([bounds[0], bounds[2]]) y = mean([bounds[1], bounds[3]]) location = (y, x) if "zoom_start" in kwargs.keys(): fit = False else: fit = True else: fit = False # get a subset of kwargs to be passed to folium.Map map_kwds = {i: kwargs[i] for i in kwargs.keys() if i in _MAP_KWARGS} if HAS_XYZSERVICES: # match provider name string to xyzservices.TileProvider if isinstance(tiles, str): try: tiles = xyzservices.providers.query_name(tiles) except ValueError: pass if isinstance(tiles, xyzservices.TileProvider): attr = attr if attr else tiles.html_attribution map_kwds["min_zoom"] = tiles.get("min_zoom", 0) map_kwds["max_zoom"] = tiles.get("max_zoom", 18) tiles = tiles.build_url(scale_factor="{r}") m = folium.Map( location=location, control_scale=control_scale, tiles=tiles, attr=attr, width=width, height=height, **map_kwds, ) # fit bounds to get a proper zoom level if fit: m.fit_bounds([[bounds[1], bounds[0]], [bounds[3], bounds[2]]]) for map_kwd in _MAP_KWARGS: kwargs.pop(map_kwd, None) nan_idx = None if column is not None: if pd.api.types.is_list_like(column): if len(column) != gdf.shape[0]: raise ValueError( "The GeoDataFrame and given column have different number of rows." ) else: column_name = "__plottable_column" gdf[column_name] = column column = column_name elif pd.api.types.is_categorical_dtype(gdf[column]): if categories is not None: raise ValueError( "Cannot specify 'categories' when column has categorical dtype" ) categorical = True elif gdf[column].dtype is np.dtype("O") or categories: categorical = True nan_idx = pd.isna(gdf[column]) if categorical: cat = pd.Categorical(gdf[column][~nan_idx], categories=categories) N = len(cat.categories) cmap = cmap if cmap else "tab20" # colormap exists in matplotlib if cmap in plt.colormaps(): color = np.apply_along_axis( colors.to_hex, 1, cm.get_cmap(cmap, N)(cat.codes) ) legend_colors = np.apply_along_axis( colors.to_hex, 1, cm.get_cmap(cmap, N)(range(N)) ) # colormap is matplotlib.Colormap elif isinstance(cmap, colors.Colormap): color = np.apply_along_axis(colors.to_hex, 1, cmap(cat.codes)) legend_colors = np.apply_along_axis(colors.to_hex, 1, cmap(range(N))) # custom list of colors elif pd.api.types.is_list_like(cmap): if N > len(cmap): cmap = cmap * (N // len(cmap) + 1) color = np.take(cmap, cat.codes) legend_colors = np.take(cmap, range(N)) else: raise ValueError( "'cmap' is invalid. For categorical plots, pass either valid " "named matplotlib colormap or a list-like of colors." ) elif callable(cmap): # List of colors based on Branca colormaps or self-defined functions color = list(map(lambda x: cmap(x), df[column])) else: vmin = gdf[column].min() if vmin is None else vmin vmax = gdf[column].max() if vmax is None else vmax # get bins if scheme is not None: if classification_kwds is None: classification_kwds = {} if "k" not in classification_kwds: classification_kwds["k"] = k binning = classify( np.asarray(gdf[column][~nan_idx]), scheme, **classification_kwds ) color = np.apply_along_axis( colors.to_hex, 1, cm.get_cmap(cmap, k)(binning.yb) ) else: bins = np.linspace(vmin, vmax, 257)[1:] binning = classify( np.asarray(gdf[column][~nan_idx]), "UserDefined", bins=bins ) color = np.apply_along_axis( colors.to_hex, 1, cm.get_cmap(cmap, 256)(binning.yb) ) # set default style if "fillOpacity" not in style_kwds: style_kwds["fillOpacity"] = 0.5 if "weight" not in style_kwds: style_kwds["weight"] = 2 # specify color if color is not None: if ( isinstance(color, str) and isinstance(gdf, geopandas.GeoDataFrame) and color in gdf.columns ): # use existing column def _style_color(x): return { "fillColor": x["properties"][color], **style_kwds, } style_function = _style_color else: # assign new column if isinstance(gdf, geopandas.GeoSeries): gdf = geopandas.GeoDataFrame(geometry=gdf) if nan_idx is not None and nan_idx.any(): nan_color = missing_kwds.pop("color", None) gdf["__folium_color"] = nan_color gdf.loc[~nan_idx, "__folium_color"] = color else: gdf["__folium_color"] = color stroke_color = style_kwds.pop("color", None) if not stroke_color: def _style_column(x): return { "fillColor": x["properties"]["__folium_color"], "color": x["properties"]["__folium_color"], **style_kwds, } style_function = _style_column else: def _style_stroke(x): return { "fillColor": x["properties"]["__folium_color"], "color": stroke_color, **style_kwds, } style_function = _style_stroke else: # use folium default def _style_default(x): return {**style_kwds} style_function = _style_default if highlight: if "fillOpacity" not in highlight_kwds: highlight_kwds["fillOpacity"] = 0.75 def _style_highlight(x): return {**highlight_kwds} highlight_function = _style_highlight else: highlight_function = None # define default for points if marker_type is None: marker_type = "circle_marker" marker = marker_type if isinstance(marker_type, str): if marker_type == "marker": marker = folium.Marker(**marker_kwds) elif marker_type == "circle": marker = folium.Circle(**marker_kwds) elif marker_type == "circle_marker": marker_kwds["radius"] = marker_kwds.get("radius", 2) marker_kwds["fill"] = marker_kwds.get("fill", True) marker = folium.CircleMarker(**marker_kwds) else: raise ValueError( "Only 'marker', 'circle', and 'circle_marker' are " "supported as marker values" ) # remove additional geometries if isinstance(gdf, geopandas.GeoDataFrame): non_active_geoms = [ name for name, val in (gdf.dtypes == "geometry").items() if val and name != gdf.geometry.name ] gdf = gdf.drop(columns=non_active_geoms) # preprare tooltip and popup if isinstance(gdf, geopandas.GeoDataFrame): # add named index to the tooltip if gdf.index.name is not None: gdf = gdf.reset_index() # specify fields to show in the tooltip tooltip = _tooltip_popup("tooltip", tooltip, gdf, **tooltip_kwds) popup = _tooltip_popup("popup", popup, gdf, **popup_kwds) else: tooltip = None popup = None # add dataframe to map folium.GeoJson( gdf.__geo_interface__, tooltip=tooltip, popup=popup, marker=marker, style_function=style_function, highlight_function=highlight_function, **kwargs, ).add_to(m) if legend: # NOTE: overlaps will be resolved in branca #88 caption = column if not column == "__plottable_column" else "" caption = legend_kwds.pop("caption", caption) if categorical: categories = cat.categories.to_list() legend_colors = legend_colors.tolist() if nan_idx.any() and nan_color: categories.append(missing_kwds.pop("label", "NaN")) legend_colors.append(nan_color) _categorical_legend(m, caption, categories, legend_colors) elif column is not None: cbar = legend_kwds.pop("colorbar", True) colormap_kwds = {} if "max_labels" in legend_kwds: colormap_kwds["max_labels"] = legend_kwds.pop("max_labels") if scheme: cb_colors = np.apply_along_axis( colors.to_hex, 1, cm.get_cmap(cmap, binning.k)(range(binning.k)) ) if cbar: if legend_kwds.pop("scale", True): index = [vmin] + binning.bins.tolist() else: index = None colorbar = bc.colormap.StepColormap( cb_colors, vmin=vmin, vmax=vmax, caption=caption, index=index, **colormap_kwds, ) else: fmt = legend_kwds.pop("fmt", "{:.2f}") if "labels" in legend_kwds: categories = legend_kwds["labels"] else: categories = binning.get_legend_classes(fmt) show_interval = legend_kwds.pop("interval", False) if not show_interval: categories = [c[1:-1] for c in categories] if nan_idx.any() and nan_color: categories.append(missing_kwds.pop("label", "NaN")) cb_colors = np.append(cb_colors, nan_color) _categorical_legend(m, caption, categories, cb_colors) else: if isinstance(cmap, bc.colormap.ColorMap): colorbar = cmap else: mp_cmap = cm.get_cmap(cmap) cb_colors = np.apply_along_axis( colors.to_hex, 1, mp_cmap(range(mp_cmap.N)) ) # linear legend if mp_cmap.N > 20: colorbar = bc.colormap.LinearColormap( cb_colors, vmin=vmin, vmax=vmax, caption=caption, **colormap_kwds, ) # steps else: colorbar = bc.colormap.StepColormap( cb_colors, vmin=vmin, vmax=vmax, caption=caption, **colormap_kwds, ) if cbar: if nan_idx.any() and nan_color: _categorical_legend( m, "", [missing_kwds.pop("label", "NaN")], [nan_color] ) m.add_child(colorbar) return m def _tooltip_popup(type, fields, gdf, **kwds): """get tooltip or popup""" import folium # specify fields to show in the tooltip if fields is False or fields is None or fields == 0: return None else: if fields is True: fields = gdf.columns.drop(gdf.geometry.name).to_list() elif isinstance(fields, int): fields = gdf.columns.drop(gdf.geometry.name).to_list()[:fields] elif isinstance(fields, str): fields = [fields] for field in ["__plottable_column", "__folium_color"]: if field in fields: fields.remove(field) # Cast fields to str fields = list(map(str, fields)) if type == "tooltip": return folium.GeoJsonTooltip(fields, **kwds) elif type == "popup": return folium.GeoJsonPopup(fields, **kwds) def _categorical_legend(m, title, categories, colors): """ Add categorical legend to a map The implementation is using the code originally written by Michel Metran (@michelmetran) and released on GitHub (https://github.com/michelmetran/package_folium) under MIT license. Copyright (c) 2020 Michel Metran Parameters ---------- m : folium.Map Existing map instance on which to draw the plot title : str title of the legend (e.g. column name) categories : list-like list of categories colors : list-like list of colors (in the same order as categories) """ # Header to Add head = """ {% macro header(this, kwargs) %} {% endmacro %} """ import branca as bc # Add CSS (on Header) macro = bc.element.MacroElement() macro._template = bc.element.Template(head) m.get_root().add_child(macro) body = f"""
{title}
    """ # Loop Categories for label, color in zip(categories, colors): body += f"""
  • {label}
    • """ body += """
""" # Add Body body = bc.element.Element(body, "legend") m.get_root().html.add_child(body) def _explore_geoseries( s, color=None, m=None, tiles="OpenStreetMap", attr=None, highlight=True, width="100%", height="100%", control_scale=True, marker_type=None, marker_kwds={}, style_kwds={}, highlight_kwds={}, **kwargs, ): """Interactive map based on GeoPandas and folium/leaflet.js Generate an interactive leaflet map based on :class:`~geopandas.GeoSeries` Parameters ---------- color : str, array-like (default None) Named color or a list-like of colors (named or hex). m : folium.Map (default None) Existing map instance on which to draw the plot. tiles : str, xyzservices.TileProvider (default 'OpenStreetMap Mapnik') Map tileset to use. Can choose from the list supported by folium, query a :class:`xyzservices.TileProvider` by a name from ``xyzservices.providers``, pass :class:`xyzservices.TileProvider` object or pass custom XYZ URL. The current list of built-in providers (when ``xyzservices`` is not available): ``["OpenStreetMap", "Stamen Terrain", “Stamen Toner", “Stamen Watercolor" "CartoDB positron", “CartoDB dark_matter"]`` You can pass a custom tileset to Folium by passing a Leaflet-style URL to the tiles parameter: ``http://{s}.yourtiles.com/{z}/{x}/{y}.png``. Be sure to check their terms and conditions and to provide attribution with the ``attr`` keyword. attr : str (default None) Map tile attribution; only required if passing custom tile URL. highlight : bool (default True) Enable highlight functionality when hovering over a geometry. width : pixel int or percentage string (default: '100%') Width of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, width is ignored. height : pixel int or percentage string (default: '100%') Height of the folium :class:`~folium.folium.Map`. If the argument m is given explicitly, height is ignored. control_scale : bool, (default True) Whether to add a control scale on the map. marker_type : str, folium.Circle, folium.CircleMarker, folium.Marker (default None) Allowed string options are ('marker', 'circle', 'circle_marker'). Defaults to folium.Marker. marker_kwds: dict (default {}) Additional keywords to be passed to the selected ``marker_type``, e.g.: radius : float Radius of the circle, in meters (for ``'circle'``) or pixels (for ``circle_marker``). icon : folium.map.Icon the :class:`folium.map.Icon` object to use to render the marker. draggable : bool (default False) Set to True to be able to drag the marker around the map. style_kwds : dict (default {}) Additional style to be passed to folium ``style_function``: stroke : bool (default True) Whether to draw stroke along the path. Set it to ``False`` to disable borders on polygons or circles. color : str Stroke color weight : int Stroke width in pixels opacity : float (default 1.0) Stroke opacity fill : boolean (default True) Whether to fill the path with color. Set it to ``False`` to disable filling on polygons or circles. fillColor : str Fill color. Defaults to the value of the color option fillOpacity : float (default 0.5) Fill opacity. Plus all supported by :func:`folium.vector_layers.path_options`. See the documentation of :class:`folium.features.GeoJson` for details. highlight_kwds : dict (default {}) Style to be passed to folium highlight_function. Uses the same keywords as ``style_kwds``. When empty, defaults to ``{"fillOpacity": 0.75}``. **kwargs : dict Additional options to be passed on to the folium. Returns ------- m : folium.folium.Map folium :class:`~folium.folium.Map` instance """ return _explore( s, color=color, m=m, tiles=tiles, attr=attr, highlight=highlight, width=width, height=height, control_scale=control_scale, marker_type=marker_type, marker_kwds=marker_kwds, style_kwds=style_kwds, highlight_kwds=highlight_kwds, **kwargs, ) geopandas-0.10.2/geopandas/geodataframe.py000066400000000000000000002431341413247402600204770ustar00rootroot00000000000000import json import warnings import numpy as np import pandas as pd from pandas import DataFrame, Series from pandas.core.accessor import CachedAccessor from shapely.geometry import mapping, shape from shapely.geometry.base import BaseGeometry from pyproj import CRS from geopandas.array import GeometryArray, GeometryDtype, from_shapely, to_wkb, to_wkt from geopandas.base import GeoPandasBase, is_geometry_type from geopandas.geoseries import GeoSeries import geopandas.io from geopandas.explore import _explore from . import _compat as compat from ._decorator import doc DEFAULT_GEO_COLUMN_NAME = "geometry" def _ensure_geometry(data, crs=None): """ Ensure the data is of geometry dtype or converted to it. If input is a (Geo)Series, output is a GeoSeries, otherwise output is GeometryArray. If the input is a GeometryDtype with a set CRS, `crs` is ignored. """ if is_geometry_type(data): if isinstance(data, Series): data = GeoSeries(data) if data.crs is None: data.crs = crs return data else: if isinstance(data, Series): out = from_shapely(np.asarray(data), crs=crs) return GeoSeries(out, index=data.index, name=data.name) else: out = from_shapely(data, crs=crs) return out def _crs_mismatch_warning(): # TODO: raise error in 0.9 or 0.10. warnings.warn( "CRS mismatch between CRS of the passed geometries " "and 'crs'. Use 'GeoDataFrame.set_crs(crs, " "allow_override=True)' to overwrite CRS or " "'GeoDataFrame.to_crs(crs)' to reproject geometries. " "CRS mismatch will raise an error in the future versions " "of GeoPandas.", FutureWarning, stacklevel=3, ) class GeoDataFrame(GeoPandasBase, DataFrame): """ A GeoDataFrame object is a pandas.DataFrame that has a column with geometry. In addition to the standard DataFrame constructor arguments, GeoDataFrame also accepts the following keyword arguments: Parameters ---------- crs : value (optional) Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. geometry : str or array (optional) If str, column to use as geometry. If array, will be set as 'geometry' column on GeoDataFrame. Examples -------- Constructing GeoDataFrame from a dictionary. >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) Notice that the inferred dtype of 'geometry' columns is geometry. >>> gdf.dtypes col1 object geometry geometry dtype: object Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries: >>> import pandas as pd >>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']} >>> df = pd.DataFrame(d) >>> gs = geopandas.GeoSeries.from_wkt(df['wkt']) >>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326") >>> gdf col1 wkt geometry 0 name1 POINT (1 2) POINT (1.00000 2.00000) 1 name2 POINT (2 1) POINT (2.00000 1.00000) See also -------- GeoSeries : Series object designed to store shapely geometry objects """ _metadata = ["_crs", "_geometry_column_name"] _geometry_column_name = DEFAULT_GEO_COLUMN_NAME def __init__(self, data=None, *args, geometry=None, crs=None, **kwargs): with compat.ignore_shapely2_warnings(): super().__init__(data, *args, **kwargs) # need to set this before calling self['geometry'], because # getitem accesses crs self._crs = CRS.from_user_input(crs) if crs else None # set_geometry ensures the geometry data have the proper dtype, # but is not called if `geometry=None` ('geometry' column present # in the data), so therefore need to ensure it here manually # but within a try/except because currently non-geometries are # allowed in that case # TODO do we want to raise / return normal DataFrame in this case? # if gdf passed in and geo_col is set, we use that for geometry if geometry is None and isinstance(data, GeoDataFrame): self._geometry_column_name = data._geometry_column_name if crs is not None and data.crs != crs: _crs_mismatch_warning() # TODO: raise error in 0.9 or 0.10. return if geometry is None and "geometry" in self.columns: # Check for multiple columns with name "geometry". If there are, # self["geometry"] is a gdf and constructor gets recursively recalled # by pandas internals trying to access this if (self.columns == "geometry").sum() > 1: raise ValueError( "GeoDataFrame does not support multiple columns " "using the geometry column name 'geometry'." ) # only if we have actual geometry values -> call set_geometry index = self.index try: if ( hasattr(self["geometry"].values, "crs") and self["geometry"].values.crs and crs and not self["geometry"].values.crs == crs ): _crs_mismatch_warning() # TODO: raise error in 0.9 or 0.10. self["geometry"] = _ensure_geometry(self["geometry"].values, crs) except TypeError: pass else: if self.index is not index: # With pandas < 1.0 and an empty frame (no rows), the index # gets reset to a default RangeIndex -> set back the original # index if needed self.index = index geometry = "geometry" if geometry is not None: if ( hasattr(geometry, "crs") and geometry.crs and crs and not geometry.crs == crs ): _crs_mismatch_warning() # TODO: raise error in 0.9 or 0.10. self.set_geometry(geometry, inplace=True) if geometry is None and crs: warnings.warn( "Assigning CRS to a GeoDataFrame without a geometry column is now " "deprecated and will not be supported in the future.", FutureWarning, stacklevel=2, ) def __setattr__(self, attr, val): # have to special case geometry b/c pandas tries to use as column... if attr == "geometry": object.__setattr__(self, attr, val) else: super().__setattr__(attr, val) def _get_geometry(self): if self._geometry_column_name not in self: raise AttributeError( "No geometry data set yet (expected in" " column '%s'.)" % self._geometry_column_name ) return self[self._geometry_column_name] def _set_geometry(self, col): if not pd.api.types.is_list_like(col): raise ValueError("Must use a list-like to set the geometry property") self.set_geometry(col, inplace=True) geometry = property( fget=_get_geometry, fset=_set_geometry, doc="Geometry data for GeoDataFrame" ) def set_geometry(self, col, drop=False, inplace=False, crs=None): """ Set the GeoDataFrame geometry using either an existing column or the specified input. By default yields a new object. The original geometry column is replaced with the input. Parameters ---------- col : column label or array drop : boolean, default False Delete column to be used as the new geometry inplace : boolean, default False Modify the GeoDataFrame in place (do not create a new object) crs : pyproj.CRS, optional Coordinate system to use. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. If passed, overrides both DataFrame and col's crs. Otherwise, tries to get crs from passed col values or DataFrame. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) Passing an array: >>> df1 = gdf.set_geometry([Point(0,0), Point(1,1)]) >>> df1 col1 geometry 0 name1 POINT (0.00000 0.00000) 1 name2 POINT (1.00000 1.00000) Using existing column: >>> gdf["buffered"] = gdf.buffer(2) >>> df2 = gdf.set_geometry("buffered") >>> df2.geometry 0 POLYGON ((3.00000 2.00000, 2.99037 1.80397, 2.... 1 POLYGON ((4.00000 1.00000, 3.99037 0.80397, 3.... Name: buffered, dtype: geometry Returns ------- GeoDataFrame See also -------- GeoDataFrame.rename_geometry : rename an active geometry column """ # Most of the code here is taken from DataFrame.set_index() if inplace: frame = self else: frame = self.copy() to_remove = None geo_column_name = self._geometry_column_name if isinstance(col, (Series, list, np.ndarray, GeometryArray)): level = col elif hasattr(col, "ndim") and col.ndim != 1: raise ValueError("Must pass array with one dimension only.") else: try: level = frame[col] except KeyError: raise ValueError("Unknown column %s" % col) except Exception: raise if isinstance(level, DataFrame): raise ValueError( "GeoDataFrame does not support setting the geometry column where " "the column name is shared by multiple columns." ) if drop: to_remove = col geo_column_name = self._geometry_column_name else: geo_column_name = col if to_remove: del frame[to_remove] if not crs: level_crs = getattr(level, "crs", None) crs = level_crs if level_crs is not None else self._crs if isinstance(level, (GeoSeries, GeometryArray)) and level.crs != crs: # Avoids caching issues/crs sharing issues level = level.copy() level.crs = crs # Check that we are using a listlike of geometries level = _ensure_geometry(level, crs=crs) index = frame.index frame[geo_column_name] = level if frame.index is not index and len(frame.index) == len(index): # With pandas < 1.0 and an empty frame (no rows), the index gets reset # to a default RangeIndex -> set back the original index if needed frame.index = index frame._geometry_column_name = geo_column_name frame.crs = crs if not inplace: return frame def rename_geometry(self, col, inplace=False): """ Renames the GeoDataFrame geometry column to the specified name. By default yields a new object. The original geometry column is replaced with the input. Parameters ---------- col : new geometry column label inplace : boolean, default False Modify the GeoDataFrame in place (do not create a new object) Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> df = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> df1 = df.rename_geometry('geom1') >>> df1.geometry.name 'geom1' >>> df.rename_geometry('geom1', inplace=True) >>> df.geometry.name 'geom1' Returns ------- geodataframe : GeoDataFrame See also -------- GeoDataFrame.set_geometry : set the active geometry """ geometry_col = self.geometry.name if col in self.columns: raise ValueError(f"Column named {col} already exists") else: if not inplace: return self.rename(columns={geometry_col: col}).set_geometry( col, inplace ) self.rename(columns={geometry_col: col}, inplace=inplace) self.set_geometry(col, inplace=inplace) @property def crs(self): """ The Coordinate Reference System (CRS) represented as a ``pyproj.CRS`` object. Returns None if the CRS is not set, and to set the value it :getter: Returns a ``pyproj.CRS`` or None. When setting, the value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. Examples -------- >>> gdf.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See also -------- GeoDataFrame.set_crs : assign CRS GeoDataFrame.to_crs : re-project to another CRS """ return self._crs @crs.setter def crs(self, value): """Sets the value of the crs""" if self._geometry_column_name not in self: warnings.warn( "Assigning CRS to a GeoDataFrame without a geometry column is now " "deprecated and will not be supported in the future.", FutureWarning, stacklevel=4, ) self._crs = None if not value else CRS.from_user_input(value) else: if hasattr(self.geometry.values, "crs"): self.geometry.values.crs = value self._crs = self.geometry.values.crs else: # column called 'geometry' without geometry self._crs = None if not value else CRS.from_user_input(value) def __setstate__(self, state): # overriding DataFrame method for compat with older pickles (CRS handling) if isinstance(state, dict): if "_metadata" in state and "crs" in state["_metadata"]: metadata = state["_metadata"] metadata[metadata.index("crs")] = "_crs" if "crs" in state and "_crs" not in state: crs = state.pop("crs") state["_crs"] = CRS.from_user_input(crs) if crs is not None else crs super().__setstate__(state) # for some versions that didn't yet have CRS at array level -> crs is set # at GeoDataFrame level with '_crs' (and not 'crs'), so without propagating # to the GeoSeries/GeometryArray try: if self.crs is not None: if self.geometry.values.crs is None: self.crs = self.crs except Exception: pass @classmethod def from_dict(cls, data, geometry=None, crs=None, **kwargs): """ Construct GeoDataFrame from dict of array-like or dicts by overriding DataFrame.from_dict method with geometry and crs Parameters ---------- data : dict Of the form {field : array-like} or {field : dict}. geometry : str or array (optional) If str, column to use as geometry. If array, will be set as 'geometry' column on GeoDataFrame. crs : str or dict (optional) Coordinate reference system to set on the resulting frame. kwargs : key-word arguments These arguments are passed to DataFrame.from_dict Returns ------- GeoDataFrame """ dataframe = super().from_dict(data, **kwargs) return GeoDataFrame(dataframe, geometry=geometry, crs=crs) @classmethod def from_file(cls, filename, **kwargs): """Alternate constructor to create a ``GeoDataFrame`` from a file. It is recommended to use :func:`geopandas.read_file` instead. Can load a ``GeoDataFrame`` from a file in any format recognized by `fiona`. See http://fiona.readthedocs.io/en/latest/manual.html for details. Parameters ---------- filename : str File path or file handle to read from. Depending on which kwargs are included, the content of filename may vary. See http://fiona.readthedocs.io/en/latest/README.html#usage for usage details. kwargs : key-word arguments These arguments are passed to fiona.open, and can be used to access multi-layer data, data stored within archives (zip files), etc. Examples -------- >>> path = geopandas.datasets.get_path('nybb') >>> gdf = geopandas.GeoDataFrame.from_file(path) >>> gdf # doctest: +SKIP BoroCode BoroName Shape_Leng Shape_Area \ geometry 0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON ((\ (970217.022 145643.332, 970227.... 1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON ((\ (1029606.077 156073.814, 102957... 2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON ((\ (1021176.479 151374.797, 102100... 3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON ((\ (981219.056 188655.316, 980940.... 4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON ((\ (1012821.806 229228.265, 101278... The recommended method of reading files is :func:`geopandas.read_file`: >>> gdf = geopandas.read_file(path) See also -------- read_file : read file to GeoDataFame GeoDataFrame.to_file : write GeoDataFrame to file """ return geopandas.io.file._read_file(filename, **kwargs) @classmethod def from_features(cls, features, crs=None, columns=None): """ Alternate constructor to create GeoDataFrame from an iterable of features or a feature collection. Parameters ---------- features - Iterable of features, where each element must be a feature dictionary or implement the __geo_interface__. - Feature collection, where the 'features' key contains an iterable of features. - Object holding a feature collection that implements the ``__geo_interface__``. crs : str or dict (optional) Coordinate reference system to set on the resulting frame. columns : list of column names, optional Optionally specify the column names to include in the output frame. This does not overwrite the property names of the input, but can ensure a consistent output format. Returns ------- GeoDataFrame Notes ----- For more information about the ``__geo_interface__``, see https://gist.github.com/sgillies/2217756 Examples -------- >>> feature_coll = { ... "type": "FeatureCollection", ... "features": [ ... { ... "id": "0", ... "type": "Feature", ... "properties": {"col1": "name1"}, ... "geometry": {"type": "Point", "coordinates": (1.0, 2.0)}, ... "bbox": (1.0, 2.0, 1.0, 2.0), ... }, ... { ... "id": "1", ... "type": "Feature", ... "properties": {"col1": "name2"}, ... "geometry": {"type": "Point", "coordinates": (2.0, 1.0)}, ... "bbox": (2.0, 1.0, 2.0, 1.0), ... }, ... ], ... "bbox": (1.0, 1.0, 2.0, 2.0), ... } >>> df = geopandas.GeoDataFrame.from_features(feature_coll) >>> df geometry col1 0 POINT (1.00000 2.00000) name1 1 POINT (2.00000 1.00000) name2 """ # Handle feature collections if hasattr(features, "__geo_interface__"): fs = features.__geo_interface__ else: fs = features if isinstance(fs, dict) and fs.get("type") == "FeatureCollection": features_lst = fs["features"] else: features_lst = features rows = [] for feature in features_lst: # load geometry if hasattr(feature, "__geo_interface__"): feature = feature.__geo_interface__ row = { "geometry": shape(feature["geometry"]) if feature["geometry"] else None } # load properties row.update(feature["properties"]) rows.append(row) return GeoDataFrame(rows, columns=columns, crs=crs) @classmethod def from_postgis( cls, sql, con, geom_col="geom", crs=None, index_col=None, coerce_float=True, parse_dates=None, params=None, chunksize=None, ): """ Alternate constructor to create a ``GeoDataFrame`` from a sql query containing a geometry column in WKB representation. Parameters ---------- sql : string con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine geom_col : string, default 'geom' column name to convert to shapely geometries crs : optional Coordinate reference system to use for the returned GeoDataFrame index_col : string or list of strings, optional, default: None Column(s) to set as index(MultiIndex) coerce_float : boolean, default True Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets parse_dates : list or dict, default None - List of column names to parse as dates. - Dict of ``{column_name: format string}`` where format string is strftime compatible in case of parsing string times, or is one of (D, s, ns, ms, us) in case of parsing integer timestamps. - Dict of ``{column_name: arg dict}``, where the arg dict corresponds to the keyword arguments of :func:`pandas.to_datetime`. Especially useful with databases without native Datetime support, such as SQLite. params : list, tuple or dict, optional, default None List of parameters to pass to execute method. chunksize : int, default None If specified, return an iterator where chunksize is the number of rows to include in each chunk. Examples -------- PostGIS >>> from sqlalchemy import create_engine # doctest: +SKIP >>> db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydb" >>> con = create_engine(db_connection_url) # doctest: +SKIP >>> sql = "SELECT geom, highway FROM roads" >>> df = geopandas.GeoDataFrame.from_postgis(sql, con) # doctest: +SKIP SpatiaLite >>> sql = "SELECT ST_Binary(geom) AS geom, highway FROM roads" >>> df = geopandas.GeoDataFrame.from_postgis(sql, con) # doctest: +SKIP The recommended method of reading from PostGIS is :func:`geopandas.read_postgis`: >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP See also -------- geopandas.read_postgis : read PostGIS database to GeoDataFrame """ df = geopandas.io.sql._read_postgis( sql, con, geom_col=geom_col, crs=crs, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return df def to_json(self, na="null", show_bbox=False, drop_id=False, **kwargs): """ Returns a GeoJSON representation of the ``GeoDataFrame`` as a string. Parameters ---------- na : {'null', 'drop', 'keep'}, default 'null' Indicates how to output missing (NaN) values in the GeoDataFrame. See below. show_bbox : bool, optional, default: False Include bbox (bounds) in the geojson drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. Notes ----- The remaining *kwargs* are passed to json.dumps(). Missing (NaN) values in the GeoDataFrame can be represented as follows: - ``null``: output the missing entries as JSON null. - ``drop``: remove the property from the feature. This applies to each feature individually so that features may have different properties. - ``keep``: output the missing entries as NaN. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> gdf.to_json() '{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", \ "properties": {"col1": "name1"}, "geometry": {"type": "Point", "coordinates": [1.0,\ 2.0]}}, {"id": "1", "type": "Feature", "properties": {"col1": "name2"}, "geometry"\ : {"type": "Point", "coordinates": [2.0, 1.0]}}]}' Alternatively, you can write GeoJSON to file: >>> gdf.to_file(path, driver="GeoJSON") # doctest: +SKIP See also -------- GeoDataFrame.to_file : write GeoDataFrame to file """ return json.dumps( self._to_geo(na=na, show_bbox=show_bbox, drop_id=drop_id), **kwargs ) @property def __geo_interface__(self): """Returns a ``GeoDataFrame`` as a python feature collection. Implements the `geo_interface`. The returned python data structure represents the ``GeoDataFrame`` as a GeoJSON-like ``FeatureCollection``. This differs from `_to_geo()` only in that it is a property with default args instead of a method Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> gdf.__geo_interface__ {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', \ 'properties': {'col1': 'name1'}, 'geometry': {'type': 'Point', 'coordinates': (1.0\ , 2.0)}, 'bbox': (1.0, 2.0, 1.0, 2.0)}, {'id': '1', 'type': 'Feature', 'properties\ ': {'col1': 'name2'}, 'geometry': {'type': 'Point', 'coordinates': (2.0, 1.0)}, 'b\ box': (2.0, 1.0, 2.0, 1.0)}], 'bbox': (1.0, 1.0, 2.0, 2.0)} """ return self._to_geo(na="null", show_bbox=True, drop_id=False) def iterfeatures(self, na="null", show_bbox=False, drop_id=False): """ Returns an iterator that yields feature dictionaries that comply with __geo_interface__ Parameters ---------- na : str, optional Options are {'null', 'drop', 'keep'}, default 'null'. Indicates how to output missing (NaN) values in the GeoDataFrame - null: output the missing entries as JSON null - drop: remove the property from the feature. This applies to each feature \ individually so that features may have different properties - keep: output the missing entries as NaN show_bbox : bool, optional Include bbox (bounds) in the geojson. Default False. drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> feature = next(gdf.iterfeatures()) >>> feature {'id': '0', 'type': 'Feature', 'properties': {'col1': 'name1'}, 'geometry': {\ 'type': 'Point', 'coordinates': (1.0, 2.0)}} """ if na not in ["null", "drop", "keep"]: raise ValueError("Unknown na method {0}".format(na)) if self._geometry_column_name not in self: raise AttributeError( "No geometry data set (expected in" " column '%s')." % self._geometry_column_name ) ids = np.array(self.index, copy=False) geometries = np.array(self[self._geometry_column_name], copy=False) if not self.columns.is_unique: raise ValueError("GeoDataFrame cannot contain duplicated column names.") properties_cols = self.columns.difference([self._geometry_column_name]) if len(properties_cols) > 0: # convert to object to get python scalars. properties = self[properties_cols].astype(object).values if na == "null": properties[pd.isnull(self[properties_cols]).values] = None for i, row in enumerate(properties): geom = geometries[i] if na == "drop": properties_items = { k: v for k, v in zip(properties_cols, row) if not pd.isnull(v) } else: properties_items = {k: v for k, v in zip(properties_cols, row)} if drop_id: feature = {} else: feature = {"id": str(ids[i])} feature["type"] = "Feature" feature["properties"] = properties_items feature["geometry"] = mapping(geom) if geom else None if show_bbox: feature["bbox"] = geom.bounds if geom else None yield feature else: for fid, geom in zip(ids, geometries): if drop_id: feature = {} else: feature = {"id": str(fid)} feature["type"] = "Feature" feature["properties"] = {} feature["geometry"] = mapping(geom) if geom else None if show_bbox: feature["bbox"] = geom.bounds if geom else None yield feature def _to_geo(self, **kwargs): """ Returns a python feature collection (i.e. the geointerface) representation of the GeoDataFrame. """ geo = { "type": "FeatureCollection", "features": list(self.iterfeatures(**kwargs)), } if kwargs.get("show_bbox", False): geo["bbox"] = tuple(self.total_bounds) return geo def to_wkb(self, hex=False, **kwargs): """ Encode all geometry columns in the GeoDataFrame to WKB. Parameters ---------- hex : bool If true, export the WKB as a hexadecimal string. The default is to return a binary bytes object. kwargs Additional keyword args will be passed to :func:`pygeos.to_wkb` if pygeos is installed. Returns ------- DataFrame geometry columns are encoded to WKB """ df = DataFrame(self.copy()) # Encode all geometry columns to WKB for col in df.columns[df.dtypes == "geometry"]: df[col] = to_wkb(df[col].values, hex=hex, **kwargs) return df def to_wkt(self, **kwargs): """ Encode all geometry columns in the GeoDataFrame to WKT. Parameters ---------- kwargs Keyword args will be passed to :func:`pygeos.to_wkt` if pygeos is installed. Returns ------- DataFrame geometry columns are encoded to WKT """ df = DataFrame(self.copy()) # Encode all geometry columns to WKT for col in df.columns[df.dtypes == "geometry"]: df[col] = to_wkt(df[col].values, **kwargs) return df def to_parquet(self, path, index=None, compression="snappy", **kwargs): """Write a GeoDataFrame to the Parquet format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow'. WARNING: this is an initial implementation of Parquet file support and associated metadata. This is tracking version 0.1.0 of the metadata specification at: https://github.com/geopandas/geo-arrow-spec This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Parquet files. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy' Name of the compression to use. Use ``None`` for no compression. kwargs Additional keyword arguments passed to :func:`pyarrow.parquet.write_table`. Examples -------- >>> gdf.to_parquet('data.parquet') # doctest: +SKIP See also -------- GeoDataFrame.to_feather : write GeoDataFrame to feather GeoDataFrame.to_file : write GeoDataFrame to file """ from geopandas.io.arrow import _to_parquet _to_parquet(self, path, compression=compression, index=index, **kwargs) def to_feather(self, path, index=None, compression=None, **kwargs): """Write a GeoDataFrame to the Feather format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow' >= 0.17. WARNING: this is an initial implementation of Feather file support and associated metadata. This is tracking version 0.1.0 of the metadata specification at: https://github.com/geopandas/geo-arrow-spec This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Feather files. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'zstd', 'lz4', 'uncompressed'}, optional Name of the compression to use. Use ``"uncompressed"`` for no compression. By default uses LZ4 if available, otherwise uncompressed. kwargs Additional keyword arguments passed to to :func:`pyarrow.feather.write_feather`. Examples -------- >>> gdf.to_feather('data.feather') # doctest: +SKIP See also -------- GeoDataFrame.to_parquet : write GeoDataFrame to parquet GeoDataFrame.to_file : write GeoDataFrame to file """ from geopandas.io.arrow import _to_feather _to_feather(self, path, index=index, compression=compression, **kwargs) def to_file(self, filename, driver=None, schema=None, index=None, **kwargs): """Write the ``GeoDataFrame`` to a file. By default, an ESRI shapefile is written, but any OGR data source supported by Fiona can be written. A dictionary of supported OGR providers is available via: >>> import fiona >>> fiona.supported_drivers # doctest: +SKIP Parameters ---------- filename : string File path or file handle to write to. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. schema : dict, default: None If specified, the schema dictionary is passed to Fiona to better control how the file is written. index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. Notes ----- The extra keyword arguments ``**kwargs`` are passed to fiona.open and can be used to write to multi-layer data, store data within archives (zip files), etc. The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. See Also -------- GeoSeries.to_file GeoDataFrame.to_postgis : write GeoDataFrame to PostGIS database GeoDataFrame.to_parquet : write GeoDataFrame to parquet GeoDataFrame.to_feather : write GeoDataFrame to feather Examples -------- >>> gdf.to_file('dataframe.shp') # doctest: +SKIP >>> gdf.to_file('dataframe.gpkg', driver='GPKG', layer='name') # doctest: +SKIP >>> gdf.to_file('dataframe.geojson', driver='GeoJSON') # doctest: +SKIP With selected drivers you can also append to a file with `mode="a"`: >>> gdf.to_file('dataframe.shp', mode="a") # doctest: +SKIP """ from geopandas.io.file import _to_file _to_file(self, filename, driver, schema, index, **kwargs) def set_crs(self, crs=None, epsg=None, inplace=False, allow_override=False): """ Set the Coordinate Reference System (CRS) of the ``GeoDataFrame``. If there are multiple geometry columns within the GeoDataFrame, only the CRS of the active geometry column is set. NOTE: The underlying geometries are not transformed to this CRS. To transform the geometries to a new CRS, use the ``to_crs`` method. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying the projection. inplace : bool, default False If True, the CRS of the GeoDataFrame will be changed in place (while still returning the result) instead of making a copy of the GeoDataFrame. allow_override : bool, default False If the the GeoDataFrame already has a CRS, allow to replace the existing CRS, even when both are not equal. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d) >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) Setting CRS to a GeoDataFrame without one: >>> gdf.crs is None True >>> gdf = gdf.set_crs('epsg:3857') >>> gdf.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Overriding existing CRS: >>> gdf = gdf.set_crs(4326, allow_override=True) Without ``allow_override=True``, ``set_crs`` returns an error if you try to override CRS. See also -------- GeoDataFrame.to_crs : re-project to another CRS """ if not inplace: df = self.copy() else: df = self df.geometry = df.geometry.set_crs( crs=crs, epsg=epsg, allow_override=allow_override, inplace=True ) return df def to_crs(self, crs=None, epsg=None, inplace=False): """Transform geometries to a new coordinate reference system. Transform all geometries in an active geometry column to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. inplace : bool, optional, default: False Whether to return a new GeoDataFrame or do the transformation in place. Returns ------- GeoDataFrame Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> gdf.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> gdf = gdf.to_crs(3857) >>> gdf col1 geometry 0 name1 POINT (111319.491 222684.209) 1 name2 POINT (222638.982 111325.143) >>> gdf.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See also -------- GeoDataFrame.set_crs : assign CRS without re-projection """ if inplace: df = self else: df = self.copy() geom = df.geometry.to_crs(crs=crs, epsg=epsg) df.geometry = geom df.crs = geom.crs if not inplace: return df def estimate_utm_crs(self, datum_name="WGS 84"): """Returns the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 .. note:: Requires pyproj 3+ Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> world = geopandas.read_file( ... geopandas.datasets.get_path("naturalearth_lowres") ... ) >>> germany = world.loc[world.name == "Germany"] >>> germany.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 32N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: World - N hemisphere - 6°E to 12°E - by country - bounds: (6.0, 0.0, 12.0, 84.0) Coordinate Operation: - name: UTM zone 32N - method: Transverse Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ return self.geometry.estimate_utm_crs(datum_name=datum_name) def __getitem__(self, key): """ If the result is a column containing only 'geometry', return a GeoSeries. If it's a DataFrame with a 'geometry' column, return a GeoDataFrame. """ result = super().__getitem__(key) geo_col = self._geometry_column_name if isinstance(result, Series) and isinstance(result.dtype, GeometryDtype): result.__class__ = GeoSeries elif isinstance(result, DataFrame) and geo_col in result: result.__class__ = GeoDataFrame result._geometry_column_name = geo_col elif isinstance(result, DataFrame) and geo_col not in result: result.__class__ = DataFrame return result def __setitem__(self, key, value): """ Overwritten to preserve CRS of GeometryArray in cases like df['geometry'] = [geom... for geom in df.geometry] """ if not pd.api.types.is_list_like(key) and key == self._geometry_column_name: if pd.api.types.is_scalar(value) or isinstance(value, BaseGeometry): value = [value] * self.shape[0] try: value = _ensure_geometry(value, crs=self.crs) self._crs = value.crs except TypeError: warnings.warn("Geometry column does not contain geometry.") super().__setitem__(key, value) # # Implement pandas methods # def merge(self, *args, **kwargs): r"""Merge two ``GeoDataFrame`` objects with a database-style join. Returns a ``GeoDataFrame`` if a geometry column is present; otherwise, returns a pandas ``DataFrame``. Returns ------- GeoDataFrame or DataFrame Notes ----- The extra arguments ``*args`` and keyword arguments ``**kwargs`` are passed to DataFrame.merge. Reference --------- https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas\ .DataFrame.merge.html """ result = DataFrame.merge(self, *args, **kwargs) geo_col = self._geometry_column_name if isinstance(result, DataFrame) and geo_col in result: result.__class__ = GeoDataFrame result.crs = self.crs result._geometry_column_name = geo_col elif isinstance(result, DataFrame) and geo_col not in result: result.__class__ = DataFrame return result @doc(pd.DataFrame) def apply(self, func, axis=0, raw=False, result_type=None, args=(), **kwargs): result = super().apply( func, axis=axis, raw=raw, result_type=result_type, args=args, **kwargs ) if ( isinstance(result, GeoDataFrame) and self._geometry_column_name in result.columns and isinstance(result[self._geometry_column_name].dtype, GeometryDtype) ): # apply calls _constructor which resets geom col name to geometry result._geometry_column_name = self._geometry_column_name if self.crs is not None and result.crs is None: result.set_crs(self.crs, inplace=True) return result @property def _constructor(self): return GeoDataFrame def __finalize__(self, other, method=None, **kwargs): """propagate metadata from other to self""" self = super().__finalize__(other, method=method, **kwargs) # merge operation: using metadata of the left object if method == "merge": for name in self._metadata: object.__setattr__(self, name, getattr(other.left, name, None)) # concat operation: using metadata of the first object elif method == "concat": for name in self._metadata: object.__setattr__(self, name, getattr(other.objs[0], name, None)) if (self.columns == self._geometry_column_name).sum() > 1: raise ValueError( "Concat operation has resulted in multiple columns using " f"the geometry column name '{self._geometry_column_name}'.\n" f"Please ensure this column from the first DataFrame is not " f"repeated." ) return self def dissolve( self, by=None, aggfunc="first", as_index=True, level=None, sort=True, observed=False, dropna=True, ): """ Dissolve geometries within `groupby` into single observation. This is accomplished by applying the `unary_union` method to all geometries within a groupself. Observations associated with each `groupby` group will be aggregated using the `aggfunc`. Parameters ---------- by : string, default None Column whose values define groups to be dissolved. If None, whole GeoDataFrame is considered a single group. aggfunc : function or string, default "first" Aggregation function for manipulation of data associated with each group. Passed to pandas `groupby.agg` method. as_index : boolean, default True If true, groupby columns become index of result. level : int or str or sequence of int or sequence of str, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. .. versionadded:: 0.9.0 sort : bool, default True Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. .. versionadded:: 0.9.0 observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. .. versionadded:: 0.9.0 dropna : bool, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups. This parameter is not supported for pandas < 1.1.0. A warning will be emitted for earlier pandas versions if a non-default value is given for this parameter. .. versionadded:: 0.9.0 Returns ------- GeoDataFrame Examples -------- >>> from shapely.geometry import Point >>> d = { ... "col1": ["name1", "name2", "name1"], ... "geometry": [Point(1, 2), Point(2, 1), Point(0, 1)], ... } >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) 2 name1 POINT (0.00000 1.00000) >>> dissolved = gdf.dissolve('col1') >>> dissolved # doctest: +SKIP geometry col1 name1 MULTIPOINT (0.00000 1.00000, 1.00000 2.00000) name2 POINT (2.00000 1.00000) See also -------- GeoDataFrame.explode : explode muti-part geometries into single geometries """ if by is None and level is None: by = np.zeros(len(self), dtype="int64") groupby_kwargs = dict( by=by, level=level, sort=sort, observed=observed, dropna=dropna ) if not compat.PANDAS_GE_11: groupby_kwargs.pop("dropna") if not dropna: # If they passed a non-default dropna value warnings.warn("dropna kwarg is not supported for pandas < 1.1.0") # Process non-spatial component data = self.drop(labels=self.geometry.name, axis=1) aggregated_data = data.groupby(**groupby_kwargs).agg(aggfunc) # Process spatial component def merge_geometries(block): merged_geom = block.unary_union return merged_geom g = self.groupby(group_keys=False, **groupby_kwargs)[self.geometry.name].agg( merge_geometries ) # Aggregate aggregated_geometry = GeoDataFrame(g, geometry=self.geometry.name, crs=self.crs) # Recombine aggregated = aggregated_geometry.join(aggregated_data) # Reset if requested if not as_index: aggregated = aggregated.reset_index() return aggregated # overrides the pandas native explode method to break up features geometrically def explode(self, column=None, ignore_index=False, index_parts=None, **kwargs): """ Explode muti-part geometries into multiple single geometries. Each row containing a multi-part geometry will be split into multiple rows with single geometries, thereby increasing the vertical size of the GeoDataFrame. .. note:: ignore_index requires pandas 1.1.0 or newer. Parameters ---------- column : string, default None Column to explode. In the case of a geometry column, multi-part geometries are converted to single-part. If None, the active geometry column is used. ignore_index : bool, default False If True, the resulting index will be labelled 0, 1, …, n - 1, ignoring `index_parts`. index_parts : boolean, default True If True, the resulting index will be a multi-index (original index with an additional level indicating the multiple geometries: a new zero-based index for each single part geometry per multi-part geometry). Returns ------- GeoDataFrame Exploded geodataframe with each single geometry as a separate entry in the geodataframe. Examples -------- >>> from shapely.geometry import MultiPoint >>> d = { ... "col1": ["name1", "name2"], ... "geometry": [ ... MultiPoint([(1, 2), (3, 4)]), ... MultiPoint([(2, 1), (0, 0)]), ... ], ... } >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 MULTIPOINT (1.00000 2.00000, 3.00000 4.00000) 1 name2 MULTIPOINT (2.00000 1.00000, 0.00000 0.00000) >>> exploded = gdf.explode(index_parts=True) >>> exploded col1 geometry 0 0 name1 POINT (1.00000 2.00000) 1 name1 POINT (3.00000 4.00000) 1 0 name2 POINT (2.00000 1.00000) 1 name2 POINT (0.00000 0.00000) >>> exploded = gdf.explode(index_parts=False) >>> exploded col1 geometry 0 name1 POINT (1.00000 2.00000) 0 name1 POINT (3.00000 4.00000) 1 name2 POINT (2.00000 1.00000) 1 name2 POINT (0.00000 0.00000) >>> exploded = gdf.explode(ignore_index=True) >>> exploded col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name1 POINT (3.00000 4.00000) 2 name2 POINT (2.00000 1.00000) 3 name2 POINT (0.00000 0.00000) See also -------- GeoDataFrame.dissolve : dissolve geometries into a single observation. """ # If no column is specified then default to the active geometry column if column is None: column = self.geometry.name # If the specified column is not a geometry dtype use pandas explode if not isinstance(self[column].dtype, GeometryDtype): if compat.PANDAS_GE_11: return super().explode(column, ignore_index=ignore_index, **kwargs) else: return super().explode(column, **kwargs) if index_parts is None: if not ignore_index: warnings.warn( "Currently, index_parts defaults to True, but in the future, " "it will default to False to be consistent with Pandas. " "Use `index_parts=True` to keep the current behavior and " "True/False to silence the warning.", FutureWarning, stacklevel=2, ) index_parts = True df_copy = self.copy() level_str = f"level_{df_copy.index.nlevels}" if level_str in df_copy.columns: # GH1393 df_copy = df_copy.rename(columns={level_str: f"__{level_str}"}) if index_parts: exploded_geom = df_copy.geometry.explode(index_parts=True) exploded_index = exploded_geom.index exploded_geom = exploded_geom.reset_index(level=-1, drop=True) else: exploded_geom = df_copy.geometry.explode(index_parts=True).reset_index( level=-1, drop=True ) exploded_index = exploded_geom.index df = ( df_copy.drop(df_copy._geometry_column_name, axis=1) .join(exploded_geom) .__finalize__(self) ) if ignore_index: df.reset_index(inplace=True, drop=True) elif index_parts: # reset to MultiIndex, otherwise df index is only first level of # exploded GeoSeries index. df.set_index(exploded_index, inplace=True) df.index.names = list(self.index.names) + [None] else: df.set_index(exploded_index, inplace=True) df.index.names = self.index.names if f"__{level_str}" in df.columns: df = df.rename(columns={f"__{level_str}": level_str}) geo_df = df.set_geometry(self._geometry_column_name) return geo_df # overrides the pandas astype method to ensure the correct return type def astype(self, dtype, copy=True, errors="raise", **kwargs): """ Cast a pandas object to a specified dtype ``dtype``. Returns a GeoDataFrame when the geometry column is kept as geometries, otherwise returns a pandas DataFrame. See the pandas.DataFrame.astype docstring for more details. Returns ------- GeoDataFrame or DataFrame """ df = super().astype(dtype, copy=copy, errors=errors, **kwargs) try: geoms = df[self._geometry_column_name] if is_geometry_type(geoms): return geopandas.GeoDataFrame(df, geometry=self._geometry_column_name) except KeyError: pass # if the geometry column is converted to non-geometries or did not exist # do not return a GeoDataFrame return pd.DataFrame(df) def convert_dtypes(self, *args, **kwargs): """ Convert columns to best possible dtypes using dtypes supporting ``pd.NA``. Always returns a GeoDataFrame as no conversions are applied to the geometry column. See the pandas.DataFrame.convert_dtypes docstring for more details. Returns ------- GeoDataFrame """ # Overridden to fix GH1870, that return type is not preserved always # (and where it was, geometry col was not) if not compat.PANDAS_GE_10: raise NotImplementedError( "GeoDataFrame.convert_dtypes requires pandas >= 1.0" ) return GeoDataFrame( super().convert_dtypes(*args, **kwargs), geometry=self.geometry.name, crs=self.crs, ) def to_postgis( self, name, con, schema=None, if_exists="fail", index=False, index_label=None, chunksize=None, dtype=None, ): """ Upload GeoDataFrame into PostGIS database. This method requires SQLAlchemy and GeoAlchemy2, and a PostgreSQL Python driver (e.g. psycopg2) to be installed. Parameters ---------- name : str Name of the target table. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the PostGIS database. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists: - fail: Raise a ValueError. - replace: Drop the table before inserting new values. - append: Insert new values to the existing table. schema : string, optional Specify the schema. If None, use default schema: 'public'. index : bool, default True Write DataFrame index as a column. Uses *index_label* as the column name in the table. index_label : string or sequence, default None Column label for index column(s). If None is given (default) and index is True, then the index names are used. chunksize : int, optional Rows will be written in batches of this size at a time. By default, all rows will be written at once. dtype : dict of column name to SQL type, default None Specifying the datatype for columns. The keys should be the column names and the values should be the SQLAlchemy types. Examples -------- >>> from sqlalchemy import create_engine >>> engine = create_engine("postgresql://myusername:mypassword@myhost:5432\ /mydatabase") # doctest: +SKIP >>> gdf.to_postgis("my_table", engine) # doctest: +SKIP See also -------- GeoDataFrame.to_file : write GeoDataFrame to file read_postgis : read PostGIS database to GeoDataFrame """ geopandas.io.sql._write_postgis( self, name, con, schema, if_exists, index, index_label, chunksize, dtype ) # # Implement standard operators for GeoSeries # def __xor__(self, other): """Implement ^ operator as for builtin set type""" warnings.warn( "'^' operator will be deprecated. Use the 'symmetric_difference' " "method instead.", DeprecationWarning, stacklevel=2, ) return self.geometry.symmetric_difference(other) def __or__(self, other): """Implement | operator as for builtin set type""" warnings.warn( "'|' operator will be deprecated. Use the 'union' method instead.", DeprecationWarning, stacklevel=2, ) return self.geometry.union(other) def __and__(self, other): """Implement & operator as for builtin set type""" warnings.warn( "'&' operator will be deprecated. Use the 'intersection' method instead.", DeprecationWarning, stacklevel=2, ) return self.geometry.intersection(other) def __sub__(self, other): """Implement - operator as for builtin set type""" warnings.warn( "'-' operator will be deprecated. Use the 'difference' method instead.", DeprecationWarning, stacklevel=2, ) return self.geometry.difference(other) plot = CachedAccessor("plot", geopandas.plotting.GeoplotAccessor) @doc(_explore) def explore(self, *args, **kwargs): """Interactive map based on folium/leaflet.js""" return _explore(self, *args, **kwargs) def sjoin(self, df, *args, **kwargs): """Spatial join of two GeoDataFrames. See the User Guide page :doc:`../../user_guide/mergingdata` for details. Parameters ---------- df : GeoDataFrame how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column predicate : string, default 'intersects' Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as ``left_df.sindex.valid_query_predicates`` or ``right_df.sindex.valid_query_predicates`` lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). Examples -------- >>> countries = geopandas.read_file( \ geopandas.datasets.get_path("naturalearth_lowres")) >>> cities = geopandas.read_file( \ geopandas.datasets.get_path("naturalearth_cities")) >>> countries.head() # doctest: +SKIP pop_est continent name \ iso_a3 gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 \ MULTIPOLYGON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 \ POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... 2 603253 Africa W. Sahara ESH 906.5 \ POLYGON ((-8.66559 27.65643, -8.66512 27.58948... 3 35623680 North America Canada CAN 1674000.0 \ MULTIPOLYGON (((-122.84000 49.00000, -122.9742... 4 326625791 North America United States of America USA 18560000.0 \ MULTIPOLYGON (((-122.84000 49.00000, -120.0000... >>> cities.head() name geometry 0 Vatican City POINT (12.45339 41.90328) 1 San Marino POINT (12.44177 43.93610) 2 Vaduz POINT (9.51667 47.13372) 3 Luxembourg POINT (6.13000 49.61166) 4 Palikir POINT (158.14997 6.91664) >>> cities_w_country_data = cities.sjoin(countries) >>> cities_w_country_data.head() # doctest: +SKIP name_left geometry index_right pop_est \ continent name_right iso_a3 gdp_md_est 0 Vatican City POINT (12.45339 41.90328) 141 62137802 \ Europe Italy ITA 2221000.0 1 San Marino POINT (12.44177 43.93610) 141 62137802 \ Europe Italy ITA 2221000.0 192 Rome POINT (12.48131 41.89790) 141 62137802 \ Europe Italy ITA 2221000.0 2 Vaduz POINT (9.51667 47.13372) 114 8754413 \ Europe Au stria AUT 416600.0 184 Vienna POINT (16.36469 48.20196) 114 8754413 \ Europe Austria AUT 416600.0 Notes ------ Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. See also -------- GeoDataFrame.sjoin_nearest : nearest neighbor join sjoin : equivalent top-level function """ return geopandas.sjoin(left_df=self, right_df=df, *args, **kwargs) def sjoin_nearest( self, right, how="inner", max_distance=None, lsuffix="left", rsuffix="right", distance_col=None, ): """ Spatial join of two GeoDataFrames based on the distance between their geometries. Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors. See the User Guide page https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html for more details. Parameters ---------- right : GeoDataFrame how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column max_distance : float, default None Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance_col : string, default None If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame. Examples -------- >>> countries = geopandas.read_file(geopandas.datasets.get_\ path("naturalearth_lowres")) >>> cities = geopandas.read_file(geopandas.datasets.get_path("naturalearth_citi\ es")) >>> countries.head(2).name # doctest: +SKIP pop_est continent name \ iso_a3 gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 MULTI\ POLYGON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 POLYG\ ON ((33.90371 -0.95000, 34.07262 -1.05982... >>> cities.head(2).name # doctest: +SKIP name geometry 0 Vatican City POINT (12.45339 41.90328) 1 San Marino POINT (12.44177 43.93610) >>> cities_w_country_data = cities.sjoin_nearest(countries) >>> cities_w_country_data[['name_left', 'name_right']].head(2) # doctest: +SKIP name_left geometry index_right pop_est continent n\ ame_right iso_a3 gdp_md_est 0 Vatican City POINT (12.45339 41.90328) 141 62137802 Europe \ Italy ITA 2221000.0 1 San Marino POINT (12.44177 43.93610) 141 62137802 Europe \ Italy ITA 2221000.0 To include the distances: >>> cities_w_country_data = cities.sjoin_nearest(countries, \ distance_col="distances") >>> cities_w_country_data[["name_left", "name_right", \ "distances"]].head(2) # doctest: +SKIP name_left name_right distances 0 Vatican City Italy 0.0 1 San Marino Italy 0.0 In the following example, we get multiple cities for Italy because all results are equidistant (in this case zero because they intersect). In fact, we get 3 results in total: >>> countries_w_city_data = cities.sjoin_nearest(countries, \ distance_col="distances", how="right") >>> italy_results = \ countries_w_city_data[countries_w_city_data["name_left"] == "Italy"] >>> italy_results # doctest: +SKIP name_x name_y 141 Vatican City Italy 141 San Marino Italy 141 Rome Italy See also -------- GeoDataFrame.sjoin : binary predicate joins sjoin_nearest : equivalent top-level function Notes ----- Since this join relies on distances, results will be innaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return geopandas.sjoin_nearest( self, right, how=how, max_distance=max_distance, lsuffix=lsuffix, rsuffix=rsuffix, distance_col=distance_col, ) def clip(self, mask, keep_geom_type=False): """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The GeoDataFrame will be clipped to the full extent of the `mask` object. If there are multiple polygons in mask, data from the GeoDataFrame will be clipped to the total boundary of all polygons in mask. Parameters ---------- mask : GeoDataFrame, GeoSeries, (Multi)Polygon Polygon vector layer used to clip `gdf`. The mask's geometry is dissolved into one geometric feature and intersected with `gdf`. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed types). Returns ------- GeoDataFrame Vector data (points, lines, polygons) from `gdf` clipped to polygon boundary from mask. See also -------- clip : equivalent top-level function Examples -------- Clip points (global cities) with a polygon (the South American continent): >>> world = geopandas.read_file( ... geopandas.datasets.get_path('naturalearth_lowres')) >>> south_america = world[world['continent'] == "South America"] >>> capitals = geopandas.read_file( ... geopandas.datasets.get_path('naturalearth_cities')) >>> capitals.shape (202, 2) >>> sa_capitals = capitals.clip(south_america) >>> sa_capitals.shape (12, 2) """ return geopandas.clip(self, mask=mask, keep_geom_type=keep_geom_type) def overlay(self, right, how="intersection", keep_geom_type=None, make_valid=True): """Perform spatial overlay between GeoDataFrames. Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union. See the User Guide page :doc:`../../user_guide/set_operations` for details. Parameters ---------- right : GeoDataFrame how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. keep_geom_type : bool If True, return only geometries of the same geometry type the GeoDataFrame has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries. make_valid : bool, default True If True, any invalid input geometries are corrected with a call to `buffer(0)`, if False, a `ValueError` is raised if any input geometries are invalid. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay Examples -------- >>> from shapely.geometry import Polygon >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), ... Polygon([(2,2), (4,2), (4,4), (2,4)])]) >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), ... Polygon([(3,3), (5,3), (5,5), (3,5)])]) >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]}) >>> df1.overlay(df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... 3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 4 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... 5 NaN 1.0 MULTIPOLYGON (((2.00000 2.00000, 3.00000 2.000... 6 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5.... >>> df1.overlay(df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2 1 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2 2 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... >>> df1.overlay(df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 1 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... 2 NaN 1.0 MULTIPOLYGON (((2.00000 2.00000, 3.00000 2.000... 3 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5.... >>> df1.overlay(df2, how='difference') geometry df1_data 0 POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 1 1 MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... 2 >>> df1.overlay(df2, how='identity') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... 3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 4 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... See also -------- GeoDataFrame.sjoin : spatial join overlay : equivalent top-level function Notes ------ Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return geopandas.overlay( self, right, how=how, keep_geom_type=keep_geom_type, make_valid=make_valid ) def _dataframe_set_geometry(self, col, drop=False, inplace=False, crs=None): if inplace: raise ValueError( "Can't do inplace setting when converting from DataFrame to GeoDataFrame" ) gf = GeoDataFrame(self) # this will copy so that BlockManager gets copied return gf.set_geometry(col, drop=drop, inplace=False, crs=crs) DataFrame.set_geometry = _dataframe_set_geometry geopandas-0.10.2/geopandas/geoseries.py000066400000000000000000001321401413247402600200370ustar00rootroot00000000000000import json import warnings import numpy as np import pandas as pd from pandas import Series, MultiIndex from pandas.core.internals import SingleBlockManager from pyproj import CRS from shapely.geometry.base import BaseGeometry from geopandas.base import GeoPandasBase, _delegate_property from geopandas.plotting import plot_series from geopandas.explore import _explore_geoseries import geopandas from . import _compat as compat from ._decorator import doc from .array import ( GeometryDtype, from_shapely, from_wkb, from_wkt, points_from_xy, to_wkb, to_wkt, ) from .base import is_geometry_type _SERIES_WARNING_MSG = """\ You are passing non-geometry data to the GeoSeries constructor. Currently, it falls back to returning a pandas Series. But in the future, we will start to raise a TypeError instead.""" def _geoseries_constructor_with_fallback(data=None, index=None, crs=None, **kwargs): """ A flexible constructor for GeoSeries._constructor, which needs to be able to fall back to a Series (if a certain operation does not produce geometries) """ try: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message=_SERIES_WARNING_MSG, category=FutureWarning, module="geopandas[.*]", ) return GeoSeries(data=data, index=index, crs=crs, **kwargs) except TypeError: return Series(data=data, index=index, **kwargs) class GeoSeries(GeoPandasBase, Series): """ A Series object designed to store shapely geometry objects. Parameters ---------- data : array-like, dict, scalar value The geometries to store in the GeoSeries. index : array-like or Index The index for the GeoSeries. crs : value (optional) Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1.00000 1.00000) 1 POINT (2.00000 2.00000) 2 POINT (3.00000 3.00000) dtype: geometry >>> s = geopandas.GeoSeries( ... [Point(1, 1), Point(2, 2), Point(3, 3)], crs="EPSG:3857" ... ) >>> s.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> s = geopandas.GeoSeries( ... [Point(1, 1), Point(2, 2), Point(3, 3)], index=["a", "b", "c"], crs=4326 ... ) >>> s a POINT (1.00000 1.00000) b POINT (2.00000 2.00000) c POINT (3.00000 3.00000) dtype: geometry >>> s.crs Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World. - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoDataFrame pandas.Series """ _metadata = ["name"] def __new__(cls, data=None, index=None, crs=None, **kwargs): # we need to use __new__ because we want to return Series instance # instead of GeoSeries instance in case of non-geometry data if hasattr(data, "crs") and crs: if not data.crs: # make a copy to avoid setting CRS to passed GeometryArray data = data.copy() else: if not data.crs == crs: warnings.warn( "CRS mismatch between CRS of the passed geometries " "and 'crs'. Use 'GeoDataFrame.set_crs(crs, " "allow_override=True)' to overwrite CRS or " "'GeoSeries.to_crs(crs)' to reproject geometries. " "CRS mismatch will raise an error in the future versions " "of GeoPandas.", FutureWarning, stacklevel=2, ) # TODO: raise error in 0.9 or 0.10. if isinstance(data, SingleBlockManager): if isinstance(data.blocks[0].dtype, GeometryDtype): if data.blocks[0].ndim == 2: # bug in pandas 0.23 where in certain indexing operations # (such as .loc) a 2D ExtensionBlock (still with 1D values # is created) which results in other failures # bug in pandas <= 0.25.0 when len(values) == 1 # (https://github.com/pandas-dev/pandas/issues/27785) from pandas.core.internals import ExtensionBlock values = data.blocks[0].values block = ExtensionBlock(values, slice(0, len(values), 1), ndim=1) data = SingleBlockManager([block], data.axes[0], fastpath=True) self = super(GeoSeries, cls).__new__(cls) super(GeoSeries, self).__init__(data, index=index, **kwargs) self.crs = getattr(self.values, "crs", crs) return self warnings.warn(_SERIES_WARNING_MSG, FutureWarning, stacklevel=2) return Series(data, index=index, **kwargs) if isinstance(data, BaseGeometry): # fix problem for scalar geometries passed, ensure the list of # scalars is of correct length if index is specified n = len(index) if index is not None else 1 data = [data] * n name = kwargs.pop("name", None) if not is_geometry_type(data): # if data is None and dtype is specified (eg from empty overlay # test), specifying dtype raises an error: # https://github.com/pandas-dev/pandas/issues/26469 kwargs.pop("dtype", None) # Use Series constructor to handle input data with compat.ignore_shapely2_warnings(): # suppress additional warning from pandas for empty data # (will always give object dtype instead of float dtype in the future, # making the `if s.empty: s = s.astype(object)` below unnecessary) empty_msg = "The default dtype for empty Series" warnings.filterwarnings("ignore", empty_msg, DeprecationWarning) warnings.filterwarnings("ignore", empty_msg, FutureWarning) s = pd.Series(data, index=index, name=name, **kwargs) # prevent trying to convert non-geometry objects if s.dtype != object: if (s.empty and s.dtype == "float64") or data is None: # pd.Series with empty data gives float64 for older pandas versions s = s.astype(object) else: warnings.warn(_SERIES_WARNING_MSG, FutureWarning, stacklevel=2) return s # try to convert to GeometryArray, if fails return plain Series try: data = from_shapely(s.values, crs) except TypeError: warnings.warn(_SERIES_WARNING_MSG, FutureWarning, stacklevel=2) return s index = s.index name = s.name self = super(GeoSeries, cls).__new__(cls) super(GeoSeries, self).__init__(data, index=index, name=name, **kwargs) if not self.crs: self.crs = crs return self def __init__(self, *args, **kwargs): # need to overwrite Series init to prevent calling it for GeoSeries # (doesn't know crs, all work is already done above) pass def append(self, *args, **kwargs): return self._wrapped_pandas_method("append", *args, **kwargs) @property def geometry(self): return self @property def x(self): """Return the x location of point geometries in a GeoSeries Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s.x 0 1.0 1 2.0 2 3.0 dtype: float64 See Also -------- GeoSeries.y GeoSeries.z """ return _delegate_property("x", self) @property def y(self): """Return the y location of point geometries in a GeoSeries Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s.y 0 1.0 1 2.0 2 3.0 dtype: float64 See Also -------- GeoSeries.x GeoSeries.z """ return _delegate_property("y", self) @property def z(self): """Return the z location of point geometries in a GeoSeries Returns ------- pandas.Series Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1, 1), Point(2, 2, 2), Point(3, 3, 3)]) >>> s.z 0 1.0 1 2.0 2 3.0 dtype: float64 See Also -------- GeoSeries.x GeoSeries.y """ return _delegate_property("z", self) @classmethod def from_file(cls, filename, **kwargs): """Alternate constructor to create a ``GeoSeries`` from a file. Can load a ``GeoSeries`` from a file from any format recognized by `fiona`. See http://fiona.readthedocs.io/en/latest/manual.html for details. From a file with attributes loads only geometry column. Note that to do that, GeoPandas first loads the whole GeoDataFrame. Parameters ---------- filename : str File path or file handle to read from. Depending on which kwargs are included, the content of filename may vary. See http://fiona.readthedocs.io/en/latest/README.html#usage for usage details. kwargs : key-word arguments These arguments are passed to fiona.open, and can be used to access multi-layer data, data stored within archives (zip files), etc. Examples -------- >>> path = geopandas.datasets.get_path('nybb') >>> s = geopandas.GeoSeries.from_file(path) >>> s 0 MULTIPOLYGON (((970217.022 145643.332, 970227.... 1 MULTIPOLYGON (((1029606.077 156073.814, 102957... 2 MULTIPOLYGON (((1021176.479 151374.797, 102100... 3 MULTIPOLYGON (((981219.056 188655.316, 980940.... 4 MULTIPOLYGON (((1012821.806 229228.265, 101278... Name: geometry, dtype: geometry See Also -------- read_file : read file to GeoDataFame """ from geopandas import GeoDataFrame df = GeoDataFrame.from_file(filename, **kwargs) return GeoSeries(df.geometry, crs=df.crs) @classmethod def from_wkb(cls, data, index=None, crs=None, **kwargs): """ Alternate constructor to create a ``GeoSeries`` from a list or array of WKB objects Parameters ---------- data : array-like or Series Series, list or array of WKB objects index : array-like or Index The index for the GeoSeries. crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Returns ------- GeoSeries See Also -------- GeoSeries.from_wkt """ return cls._from_wkb_or_wkb(from_wkb, data, index=index, crs=crs, **kwargs) @classmethod def from_wkt(cls, data, index=None, crs=None, **kwargs): """ Alternate constructor to create a ``GeoSeries`` from a list or array of WKT objects Parameters ---------- data : array-like, Series Series, list, or array of WKT objects index : array-like or Index The index for the GeoSeries. crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Returns ------- GeoSeries See Also -------- GeoSeries.from_wkb Examples -------- >>> wkts = [ ... 'POINT (1 1)', ... 'POINT (2 2)', ... 'POINT (3 3)', ... ] >>> s = geopandas.GeoSeries.from_wkt(wkts) >>> s 0 POINT (1.00000 1.00000) 1 POINT (2.00000 2.00000) 2 POINT (3.00000 3.00000) dtype: geometry """ return cls._from_wkb_or_wkb(from_wkt, data, index=index, crs=crs, **kwargs) @classmethod def from_xy(cls, x, y, z=None, index=None, crs=None, **kwargs): """ Alternate constructor to create a :class:`~geopandas.GeoSeries` of Point geometries from lists or arrays of x, y(, z) coordinates In case of geographic coordinates, it is assumed that longitude is captured by ``x`` coordinates and latitude by ``y``. Parameters ---------- x, y, z : iterable index : array-like or Index, optional The index for the GeoSeries. If not given and all coordinate inputs are Series with an equal index, that index is used. crs : value, optional Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. **kwargs Additional arguments passed to the Series constructor, e.g. ``name``. Returns ------- GeoSeries See Also -------- GeoSeries.from_wkt points_from_xy Examples -------- >>> x = [2.5, 5, -3.0] >>> y = [0.5, 1, 1.5] >>> s = geopandas.GeoSeries.from_xy(x, y, crs="EPSG:4326") >>> s 0 POINT (2.50000 0.50000) 1 POINT (5.00000 1.00000) 2 POINT (-3.00000 1.50000) dtype: geometry """ if index is None: if ( isinstance(x, Series) and isinstance(y, Series) and x.index.equals(y.index) and (z is None or (isinstance(z, Series) and x.index.equals(z.index))) ): # check if we can reuse index index = x.index return cls(points_from_xy(x, y, z, crs=crs), index=index, crs=crs, **kwargs) @classmethod def _from_wkb_or_wkb( cls, from_wkb_or_wkt_function, data, index=None, crs=None, **kwargs ): """Create a GeoSeries from either WKT or WKB values""" if isinstance(data, Series): if index is not None: data = data.reindex(index) else: index = data.index data = data.values return cls(from_wkb_or_wkt_function(data, crs=crs), index=index, **kwargs) @property def __geo_interface__(self): """Returns a ``GeoSeries`` as a python feature collection. Implements the `geo_interface`. The returned python data structure represents the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection``. Note that the features will have an empty ``properties`` dict as they don't have associated attributes (geometry only). Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s.__geo_interface__ {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', \ 'properties': {}, 'geometry': {'type': 'Point', 'coordinates': (1.0, 1.0)}, \ 'bbox': (1.0, 1.0, 1.0, 1.0)}, {'id': '1', 'type': 'Feature', \ 'properties': {}, 'geometry': {'type': 'Point', 'coordinates': (2.0, 2.0)}, \ 'bbox': (2.0, 2.0, 2.0, 2.0)}, {'id': '2', 'type': 'Feature', 'properties': \ {}, 'geometry': {'type': 'Point', 'coordinates': (3.0, 3.0)}, 'bbox': (3.0, \ 3.0, 3.0, 3.0)}], 'bbox': (1.0, 1.0, 3.0, 3.0)} """ from geopandas import GeoDataFrame return GeoDataFrame({"geometry": self}).__geo_interface__ def to_file(self, filename, driver=None, index=None, **kwargs): """Write the ``GeoSeries`` to a file. By default, an ESRI shapefile is written, but any OGR data source supported by Fiona can be written. Parameters ---------- filename : string File path or file handle to write to. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. Notes ----- The extra keyword arguments ``**kwargs`` are passed to fiona.open and can be used to write to multi-layer data, store data within archives (zip files), etc. See Also -------- GeoDataFrame.to_file : write GeoDataFrame to file read_file : read file to GeoDataFame Examples -------- >>> s.to_file('series.shp') # doctest: +SKIP >>> s.to_file('series.gpkg', driver='GPKG', layer='name1') # doctest: +SKIP >>> s.to_file('series.geojson', driver='GeoJSON') # doctest: +SKIP """ from geopandas import GeoDataFrame data = GeoDataFrame({"geometry": self}, index=self.index) data.crs = self.crs data.to_file(filename, driver, index=index, **kwargs) # # Implement pandas methods # @property def _constructor(self): return _geoseries_constructor_with_fallback @property def _constructor_expanddim(self): from geopandas import GeoDataFrame return GeoDataFrame def _wrapped_pandas_method(self, mtd, *args, **kwargs): """Wrap a generic pandas method to ensure it returns a GeoSeries""" val = getattr(super(), mtd)(*args, **kwargs) if type(val) == Series: val.__class__ = GeoSeries val.crs = self.crs return val def __getitem__(self, key): return self._wrapped_pandas_method("__getitem__", key) @doc(pd.Series) def sort_index(self, *args, **kwargs): return self._wrapped_pandas_method("sort_index", *args, **kwargs) @doc(pd.Series) def take(self, *args, **kwargs): return self._wrapped_pandas_method("take", *args, **kwargs) @doc(pd.Series) def select(self, *args, **kwargs): return self._wrapped_pandas_method("select", *args, **kwargs) @doc(pd.Series) def apply(self, func, convert_dtype=True, args=(), **kwargs): result = super().apply(func, convert_dtype=convert_dtype, args=args, **kwargs) if isinstance(result, GeoSeries): if self.crs is not None: result.set_crs(self.crs, inplace=True) return result def __finalize__(self, other, method=None, **kwargs): """propagate metadata from other to self""" # NOTE: backported from pandas master (upcoming v0.13) for name in self._metadata: object.__setattr__(self, name, getattr(other, name, None)) return self def isna(self): """ Detect missing values. Historically, NA values in a GeoSeries could be represented by empty geometric objects, in addition to standard representations such as None and np.nan. This behaviour is changed in version 0.6.0, and now only actual missing values return True. To detect empty geometries, use ``GeoSeries.is_empty`` instead. Returns ------- A boolean pandas Series of the same size as the GeoSeries, True where a value is NA. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 None 2 GEOMETRYCOLLECTION EMPTY dtype: geometry >>> s.isna() 0 False 1 True 2 False dtype: bool See Also -------- GeoSeries.notna : inverse of isna GeoSeries.is_empty : detect empty geometries """ if self.is_empty.any(): warnings.warn( "GeoSeries.isna() previously returned True for both missing (None) " "and empty geometries. Now, it only returns True for missing values. " "Since the calling GeoSeries contains empty geometries, the result " "has changed compared to previous versions of GeoPandas.\n" "Given a GeoSeries 's', you can use 's.is_empty | s.isna()' to get " "back the old behaviour.\n\n" "To further ignore this warning, you can do: \n" "import warnings; warnings.filterwarnings('ignore', 'GeoSeries.isna', " "UserWarning)", UserWarning, stacklevel=2, ) return super().isna() def isnull(self): """Alias for `isna` method. See `isna` for more detail.""" return self.isna() def notna(self): """ Detect non-missing values. Historically, NA values in a GeoSeries could be represented by empty geometric objects, in addition to standard representations such as None and np.nan. This behaviour is changed in version 0.6.0, and now only actual missing values return False. To detect empty geometries, use ``~GeoSeries.is_empty`` instead. Returns ------- A boolean pandas Series of the same size as the GeoSeries, False where a value is NA. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 None 2 GEOMETRYCOLLECTION EMPTY dtype: geometry >>> s.notna() 0 True 1 False 2 True dtype: bool See Also -------- GeoSeries.isna : inverse of notna GeoSeries.is_empty : detect empty geometries """ if self.is_empty.any(): warnings.warn( "GeoSeries.notna() previously returned False for both missing (None) " "and empty geometries. Now, it only returns False for missing values. " "Since the calling GeoSeries contains empty geometries, the result " "has changed compared to previous versions of GeoPandas.\n" "Given a GeoSeries 's', you can use '~s.is_empty & s.notna()' to get " "back the old behaviour.\n\n" "To further ignore this warning, you can do: \n" "import warnings; warnings.filterwarnings('ignore', " "'GeoSeries.notna', UserWarning)", UserWarning, stacklevel=2, ) return super().notna() def notnull(self): """Alias for `notna` method. See `notna` for more detail.""" return self.notna() def fillna(self, value=None, method=None, inplace=False, **kwargs): """Fill NA values with a geometry (empty polygon by default). "method" is currently not implemented for pandas <= 0.12. Examples -------- >>> from shapely.geometry import Polygon >>> s = geopandas.GeoSeries( ... [ ... Polygon([(0, 0), (1, 1), (0, 1)]), ... None, ... Polygon([(0, 0), (-1, 1), (0, -1)]), ... ] ... ) >>> s 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 None 2 POLYGON ((0.00000 0.00000, -1.00000 1.00000, 0... dtype: geometry >>> s.fillna() 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 GEOMETRYCOLLECTION EMPTY 2 POLYGON ((0.00000 0.00000, -1.00000 1.00000, 0... dtype: geometry >>> s.fillna(Polygon([(0, 1), (2, 1), (1, 2)])) 0 POLYGON ((0.00000 0.00000, 1.00000 1.00000, 0.... 1 POLYGON ((0.00000 1.00000, 2.00000 1.00000, 1.... 2 POLYGON ((0.00000 0.00000, -1.00000 1.00000, 0... dtype: geometry See Also -------- GeoSeries.isna : detect missing values """ if value is None: value = BaseGeometry() return super().fillna(value=value, method=method, inplace=inplace, **kwargs) def __contains__(self, other): """Allow tests of the form "geom in s" Tests whether a GeoSeries contains a geometry. Note: This is not the same as the geometric method "contains". """ if isinstance(other, BaseGeometry): return np.any(self.geom_equals(other)) else: return False @doc(plot_series) def plot(self, *args, **kwargs): return plot_series(self, *args, **kwargs) @doc(_explore_geoseries) def explore(self, *args, **kwargs): """Interactive map based on folium/leaflet.js""" return _explore_geoseries(self, *args, **kwargs) def explode(self, ignore_index=False, index_parts=None): """ Explode multi-part geometries into multiple single geometries. Single rows can become multiple rows. This is analogous to PostGIS's ST_Dump(). The 'path' index is the second level of the returned MultiIndex Parameters ---------- ignore_index : bool, default False If True, the resulting index will be labelled 0, 1, …, n - 1, ignoring `index_parts`. index_parts : boolean, default True If True, the resulting index will be a multi-index (original index with an additional level indicating the multiple geometries: a new zero-based index for each single part geometry per multi-part geometry). Returns ------- A GeoSeries with a MultiIndex. The levels of the MultiIndex are the original index and a zero-based integer index that counts the number of single geometries within a multi-part geometry. Examples -------- >>> from shapely.geometry import MultiPoint >>> s = geopandas.GeoSeries( ... [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ... ) >>> s 0 MULTIPOINT (0.00000 0.00000, 1.00000 1.00000) 1 MULTIPOINT (2.00000 2.00000, 3.00000 3.00000, ... dtype: geometry >>> s.explode(index_parts=True) 0 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 1 0 POINT (2.00000 2.00000) 1 POINT (3.00000 3.00000) 2 POINT (4.00000 4.00000) dtype: geometry See also -------- GeoDataFrame.explode """ if index_parts is None and not ignore_index: warnings.warn( "Currently, index_parts defaults to True, but in the future, " "it will default to False to be consistent with Pandas. " "Use `index_parts=True` to keep the current behavior and True/False " "to silence the warning.", FutureWarning, stacklevel=2, ) index_parts = True if compat.USE_PYGEOS and compat.PYGEOS_GE_09: import pygeos # noqa geometries, outer_idx = pygeos.get_parts( self.values.data, return_index=True ) if len(outer_idx): # Generate inner index as a range per value of outer_idx # 1. identify the start of each run of values in outer_idx # 2. count number of values per run # 3. use cumulative sums to create an incremental range # starting at 0 in each run run_start = np.r_[True, outer_idx[:-1] != outer_idx[1:]] counts = np.diff(np.r_[np.nonzero(run_start)[0], len(outer_idx)]) inner_index = (~run_start).cumsum() inner_index -= np.repeat(inner_index[run_start], counts) else: inner_index = [] # extract original index values based on integer index outer_index = self.index.take(outer_idx) if ignore_index: index = range(len(geometries)) elif index_parts: nlevels = outer_index.nlevels index_arrays = [ outer_index.get_level_values(lvl) for lvl in range(nlevels) ] index_arrays.append(inner_index) index = MultiIndex.from_arrays( index_arrays, names=self.index.names + [None] ) else: index = outer_index return GeoSeries(geometries, index=index, crs=self.crs).__finalize__(self) # else PyGEOS is not available or version <= 0.8 index = [] geometries = [] for idx, s in self.geometry.iteritems(): if s.type.startswith("Multi") or s.type == "GeometryCollection": geoms = s.geoms idxs = [(idx, i) for i in range(len(geoms))] else: geoms = [s] idxs = [(idx, 0)] index.extend(idxs) geometries.extend(geoms) if ignore_index: index = range(len(geometries)) elif index_parts: # if self.index is a MultiIndex then index is a list of nested tuples if isinstance(self.index, MultiIndex): index = [tuple(outer) + (inner,) for outer, inner in index] index = MultiIndex.from_tuples(index, names=self.index.names + [None]) else: index = [idx for idx, _ in index] return GeoSeries(geometries, index=index, crs=self.crs).__finalize__(self) # # Additional methods # def set_crs(self, crs=None, epsg=None, inplace=False, allow_override=False): """ Set the Coordinate Reference System (CRS) of a ``GeoSeries``. NOTE: The underlying geometries are not transformed to this CRS. To transform the geometries to a new CRS, use the ``to_crs`` method. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying the projection. inplace : bool, default False If True, the CRS of the GeoSeries will be changed in place (while still returning the result) instead of making a copy of the GeoSeries. allow_override : bool, default False If the the GeoSeries already has a CRS, allow to replace the existing CRS, even when both are not equal. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1.00000 1.00000) 1 POINT (2.00000 2.00000) 2 POINT (3.00000 3.00000) dtype: geometry Setting CRS to a GeoSeries without one: >>> s.crs is None True >>> s = s.set_crs('epsg:3857') >>> s.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Overriding existing CRS: >>> s = s.set_crs(4326, allow_override=True) Without ``allow_override=True``, ``set_crs`` returns an error if you try to override CRS. See Also -------- GeoSeries.to_crs : re-project to another CRS """ if crs is not None: crs = CRS.from_user_input(crs) elif epsg is not None: crs = CRS.from_epsg(epsg) else: raise ValueError("Must pass either crs or epsg.") if not allow_override and self.crs is not None and not self.crs == crs: raise ValueError( "The GeoSeries already has a CRS which is not equal to the passed " "CRS. Specify 'allow_override=True' to allow replacing the existing " "CRS without doing any transformation. If you actually want to " "transform the geometries, use 'GeoSeries.to_crs' instead." ) if not inplace: result = self.copy() else: result = self result.crs = crs return result def to_crs(self, crs=None, epsg=None): """Returns a ``GeoSeries`` with all geometries transformed to a new coordinate reference system. Transform all geometries in a GeoSeries to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion or projecting entire geometries. All segments joining points are assumed to be lines in the current projection, not geodesics. Objects crossing the dateline (or other projection boundary) will have undesirable behavior. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. Returns ------- GeoSeries Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)], crs=4326) >>> s 0 POINT (1.00000 1.00000) 1 POINT (2.00000 2.00000) 2 POINT (3.00000 3.00000) dtype: geometry >>> s.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> s = s.to_crs(3857) >>> s 0 POINT (111319.491 111325.143) 1 POINT (222638.982 222684.209) 2 POINT (333958.472 334111.171) dtype: geometry >>> s.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See Also -------- GeoSeries.set_crs : assign CRS """ return GeoSeries( self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name ) def estimate_utm_crs(self, datum_name="WGS 84"): """Returns the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 .. note:: Requires pyproj 3+ Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> world = geopandas.read_file( ... geopandas.datasets.get_path("naturalearth_lowres") ... ) >>> germany = world.loc[world.name == "Germany"] >>> germany.geometry.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 32N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: World - N hemisphere - 6°E to 12°E - by country - bounds: (6.0, 0.0, 12.0, 84.0) Coordinate Operation: - name: UTM zone 32N - method: Transverse Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ return self.values.estimate_utm_crs(datum_name) def to_json(self, **kwargs): """ Returns a GeoJSON string representation of the GeoSeries. Parameters ---------- *kwargs* that will be passed to json.dumps(). Returns ------- JSON string Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1.00000 1.00000) 1 POINT (2.00000 2.00000) 2 POINT (3.00000 3.00000) dtype: geometry >>> s.to_json() '{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", "pr\ operties": {}, "geometry": {"type": "Point", "coordinates": [1.0, 1.0]}, "bbox": [1.0,\ 1.0, 1.0, 1.0]}, {"id": "1", "type": "Feature", "properties": {}, "geometry": {"type"\ : "Point", "coordinates": [2.0, 2.0]}, "bbox": [2.0, 2.0, 2.0, 2.0]}, {"id": "2", "typ\ e": "Feature", "properties": {}, "geometry": {"type": "Point", "coordinates": [3.0, 3.\ 0]}, "bbox": [3.0, 3.0, 3.0, 3.0]}], "bbox": [1.0, 1.0, 3.0, 3.0]}' See Also -------- GeoSeries.to_file : write GeoSeries to file """ return json.dumps(self.__geo_interface__, **kwargs) def to_wkb(self, hex=False, **kwargs): """ Convert GeoSeries geometries to WKB Parameters ---------- hex : bool If true, export the WKB as a hexadecimal string. The default is to return a binary bytes object. kwargs Additional keyword args will be passed to :func:`pygeos.to_wkb` if pygeos is installed. Returns ------- Series WKB representations of the geometries See also -------- GeoSeries.to_wkt """ return Series(to_wkb(self.array, hex=hex, **kwargs), index=self.index) def to_wkt(self, **kwargs): """ Convert GeoSeries geometries to WKT Parameters ---------- kwargs Keyword args will be passed to :func:`pygeos.to_wkt` if pygeos is installed. Returns ------- Series WKT representations of the geometries Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)]) >>> s 0 POINT (1.00000 1.00000) 1 POINT (2.00000 2.00000) 2 POINT (3.00000 3.00000) dtype: geometry >>> s.to_wkt() 0 POINT (1 1) 1 POINT (2 2) 2 POINT (3 3) dtype: object See also -------- GeoSeries.to_wkb """ return Series(to_wkt(self.array, **kwargs), index=self.index) # # Implement standard operators for GeoSeries # def __xor__(self, other): """Implement ^ operator as for builtin set type""" warnings.warn( "'^' operator will be deprecated. Use the 'symmetric_difference' " "method instead.", DeprecationWarning, stacklevel=2, ) return self.symmetric_difference(other) def __or__(self, other): """Implement | operator as for builtin set type""" warnings.warn( "'|' operator will be deprecated. Use the 'union' method instead.", DeprecationWarning, stacklevel=2, ) return self.union(other) def __and__(self, other): """Implement & operator as for builtin set type""" warnings.warn( "'&' operator will be deprecated. Use the 'intersection' method instead.", DeprecationWarning, stacklevel=2, ) return self.intersection(other) def __sub__(self, other): """Implement - operator as for builtin set type""" warnings.warn( "'-' operator will be deprecated. Use the 'difference' method instead.", DeprecationWarning, stacklevel=2, ) return self.difference(other) def clip(self, mask, keep_geom_type=False): """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The GeoSeries will be clipped to the full extent of the `mask` object. If there are multiple polygons in mask, data from the GeoSeries will be clipped to the total boundary of all polygons in mask. Parameters ---------- mask : GeoDataFrame, GeoSeries, (Multi)Polygon Polygon vector layer used to clip `gdf`. The mask's geometry is dissolved into one geometric feature and intersected with `gdf`. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed-types). Returns ------- GeoSeries Vector data (points, lines, polygons) from `gdf` clipped to polygon boundary from mask. See also -------- clip : top-level function for clip Examples -------- Clip points (global cities) with a polygon (the South American continent): >>> world = geopandas.read_file( ... geopandas.datasets.get_path('naturalearth_lowres')) >>> south_america = world[world['continent'] == "South America"] >>> capitals = geopandas.read_file( ... geopandas.datasets.get_path('naturalearth_cities')) >>> capitals.shape (202, 2) >>> sa_capitals = capitals.geometry.clip(south_america) >>> sa_capitals.shape (12,) """ return geopandas.clip(self, mask=mask, keep_geom_type=keep_geom_type) geopandas-0.10.2/geopandas/io/000077500000000000000000000000001413247402600161065ustar00rootroot00000000000000geopandas-0.10.2/geopandas/io/__init__.py000066400000000000000000000000001413247402600202050ustar00rootroot00000000000000geopandas-0.10.2/geopandas/io/arrow.py000066400000000000000000000417311413247402600176200ustar00rootroot00000000000000from distutils.version import LooseVersion import json import warnings from pandas import DataFrame from geopandas._compat import import_optional_dependency from geopandas.array import from_wkb from geopandas import GeoDataFrame import geopandas from .file import _expand_user METADATA_VERSION = "0.1.0" # reference: https://github.com/geopandas/geo-arrow-spec # Metadata structure: # { # "geo": { # "columns": { # "": { # "crs": "", # "encoding": "WKB" # } # }, # "creator": { # "library": "geopandas", # "version": "" # } # "primary_column": "", # "schema_version": "" # } # } def _is_fsspec_url(url): return ( isinstance(url, str) and "://" in url and not url.startswith(("http://", "https://")) ) def _create_metadata(df): """Create and encode geo metadata dict. Parameters ---------- df : GeoDataFrame Returns ------- dict """ # Construct metadata for each geometry column_metadata = {} for col in df.columns[df.dtypes == "geometry"]: series = df[col] column_metadata[col] = { "crs": series.crs.to_wkt() if series.crs else None, "encoding": "WKB", "bbox": series.total_bounds.tolist(), } return { "primary_column": df._geometry_column_name, "columns": column_metadata, "schema_version": METADATA_VERSION, "creator": {"library": "geopandas", "version": geopandas.__version__}, } def _encode_metadata(metadata): """Encode metadata dict to UTF-8 JSON string Parameters ---------- metadata : dict Returns ------- UTF-8 encoded JSON string """ return json.dumps(metadata).encode("utf-8") def _decode_metadata(metadata_str): """Decode a UTF-8 encoded JSON string to dict Parameters ---------- metadata_str : string (UTF-8 encoded) Returns ------- dict """ if metadata_str is None: return None return json.loads(metadata_str.decode("utf-8")) def _validate_dataframe(df): """Validate that the GeoDataFrame conforms to requirements for writing to Parquet format. Raises `ValueError` if the GeoDataFrame is not valid. copied from `pandas.io.parquet` Parameters ---------- df : GeoDataFrame """ if not isinstance(df, DataFrame): raise ValueError("Writing to Parquet/Feather only supports IO with DataFrames") # must have value column names (strings only) if df.columns.inferred_type not in {"string", "unicode", "empty"}: raise ValueError("Writing to Parquet/Feather requires string column names") # index level names must be strings valid_names = all( isinstance(name, str) for name in df.index.names if name is not None ) if not valid_names: raise ValueError("Index level names must be strings") def _validate_metadata(metadata): """Validate geo metadata. Must not be empty, and must contain the structure specified above. Raises ValueError if metadata is not valid. Parameters ---------- metadata : dict """ if not metadata: raise ValueError("Missing or malformed geo metadata in Parquet/Feather file") required_keys = ("primary_column", "columns") for key in required_keys: if metadata.get(key, None) is None: raise ValueError( "'geo' metadata in Parquet/Feather file is missing required key: " "'{key}'".format(key=key) ) if not isinstance(metadata["columns"], dict): raise ValueError("'columns' in 'geo' metadata must be a dict") # Validate that geometry columns have required metadata and values required_col_keys = ("crs", "encoding") for col, column_metadata in metadata["columns"].items(): for key in required_col_keys: if key not in column_metadata: raise ValueError( "'geo' metadata in Parquet/Feather file is missing required key " "'{key}' for column '{col}'".format(key=key, col=col) ) if column_metadata["encoding"] != "WKB": raise ValueError("Only WKB geometry encoding is supported") def _geopandas_to_arrow(df, index=None): """ Helper function with main, shared logic for to_parquet/to_feather. """ from pyarrow import Table warnings.warn( "this is an initial implementation of Parquet/Feather file support and " "associated metadata. This is tracking version 0.1.0 of the metadata " "specification at " "https://github.com/geopandas/geo-arrow-spec\n\n" "This metadata specification does not yet make stability promises. " "We do not yet recommend using this in a production setting unless you " "are able to rewrite your Parquet/Feather files.\n\n" "To further ignore this warning, you can do: \n" "import warnings; warnings.filterwarnings('ignore', " "message='.*initial implementation of Parquet.*')", UserWarning, stacklevel=4, ) _validate_dataframe(df) # create geo metadata before altering incoming data frame geo_metadata = _create_metadata(df) df = df.to_wkb() table = Table.from_pandas(df, preserve_index=index) # Store geopandas specific file-level metadata # This must be done AFTER creating the table or it is not persisted metadata = table.schema.metadata metadata.update({b"geo": _encode_metadata(geo_metadata)}) return table.replace_schema_metadata(metadata) def _to_parquet(df, path, index=None, compression="snappy", **kwargs): """ Write a GeoDataFrame to the Parquet format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow'. WARNING: this is an initial implementation of Parquet file support and associated metadata. This is tracking version 0.1.0 of the metadata specification at: https://github.com/geopandas/geo-arrow-spec This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Parquet files. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy' Name of the compression to use. Use ``None`` for no compression. kwargs Additional keyword arguments passed to pyarrow.parquet.write_table(). """ parquet = import_optional_dependency( "pyarrow.parquet", extra="pyarrow is required for Parquet support." ) path = _expand_user(path) table = _geopandas_to_arrow(df, index=index) parquet.write_table(table, path, compression=compression, **kwargs) def _to_feather(df, path, index=None, compression=None, **kwargs): """ Write a GeoDataFrame to the Feather format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow' >= 0.17. WARNING: this is an initial implementation of Feather file support and associated metadata. This is tracking version 0.1.0 of the metadata specification at: https://github.com/geopandas/geo-arrow-spec This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Feather files. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'zstd', 'lz4', 'uncompressed'}, optional Name of the compression to use. Use ``"uncompressed"`` for no compression. By default uses LZ4 if available, otherwise uncompressed. kwargs Additional keyword arguments passed to pyarrow.feather.write_feather(). """ feather = import_optional_dependency( "pyarrow.feather", extra="pyarrow is required for Feather support." ) # TODO move this into `import_optional_dependency` import pyarrow if pyarrow.__version__ < LooseVersion("0.17.0"): raise ImportError("pyarrow >= 0.17 required for Feather support") path = _expand_user(path) table = _geopandas_to_arrow(df, index=index) feather.write_feather(table, path, compression=compression, **kwargs) def _arrow_to_geopandas(table): """ Helper function with main, shared logic for read_parquet/read_feather. """ df = table.to_pandas() metadata = table.schema.metadata if metadata is None or b"geo" not in metadata: raise ValueError( """Missing geo metadata in Parquet/Feather file. Use pandas.read_parquet/read_feather() instead.""" ) try: metadata = _decode_metadata(metadata.get(b"geo", b"")) except (TypeError, json.decoder.JSONDecodeError): raise ValueError("Missing or malformed geo metadata in Parquet/Feather file") _validate_metadata(metadata) # Find all geometry columns that were read from the file. May # be a subset if 'columns' parameter is used. geometry_columns = df.columns.intersection(metadata["columns"]) if not len(geometry_columns): raise ValueError( """No geometry columns are included in the columns read from the Parquet/Feather file. To read this file without geometry columns, use pandas.read_parquet/read_feather() instead.""" ) geometry = metadata["primary_column"] # Missing geometry likely indicates a subset of columns was read; # promote the first available geometry to the primary geometry. if len(geometry_columns) and geometry not in geometry_columns: geometry = geometry_columns[0] # if there are multiple non-primary geometry columns, raise a warning if len(geometry_columns) > 1: warnings.warn( "Multiple non-primary geometry columns read from Parquet/Feather " "file. The first column read was promoted to the primary geometry." ) # Convert the WKB columns that are present back to geometry. for col in geometry_columns: df[col] = from_wkb(df[col].values, crs=metadata["columns"][col]["crs"]) return GeoDataFrame(df, geometry=geometry) def _get_filesystem_path(path, filesystem=None, storage_options=None): """ Get the filesystem and path for a given filesystem and path. If the filesystem is not None then it's just returned as is. """ import pyarrow if ( isinstance(path, str) and storage_options is None and filesystem is None and LooseVersion(pyarrow.__version__) >= "5.0.0" ): # Use the native pyarrow filesystem if possible. try: from pyarrow.fs import FileSystem filesystem, path = FileSystem.from_uri(path) except Exception: # fallback to use get_handle / fsspec for filesystems # that pyarrow doesn't support pass if _is_fsspec_url(path) and filesystem is None: fsspec = import_optional_dependency( "fsspec", extra="fsspec is requred for 'storage_options'." ) filesystem, path = fsspec.core.url_to_fs(path, **(storage_options or {})) if filesystem is None and storage_options: raise ValueError( "Cannot provide 'storage_options' with non-fsspec path '{}'".format(path) ) return filesystem, path def _read_parquet(path, columns=None, storage_options=None, **kwargs): """ Load a Parquet object from the file path, returning a GeoDataFrame. You can read a subset of columns in the file using the ``columns`` parameter. However, the structure of the returned GeoDataFrame will depend on which columns you read: * if no geometry columns are read, this will raise a ``ValueError`` - you should use the pandas `read_parquet` method instead. * if the primary geometry column saved to this file is not included in columns, the first available geometry column will be set as the geometry column of the returned GeoDataFrame. Requires 'pyarrow'. .. versionadded:: 0.8 Parameters ---------- path : str, path object columns : list-like of strings, default=None If not None, only these columns will be read from the file. If the primary geometry column is not included, the first secondary geometry read from the file will be set as the geometry column of the returned GeoDataFrame. If no geometry columns are present, a ``ValueError`` will be raised. storage_options : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details. When no storage options are provided and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec`` (e.g. "s3://") then the ``pyarrow.fs`` filesystem is preferred. Provide the instantiated fsspec filesystem using the ``filesystem`` keyword if you wish to use its implementation. **kwargs Any additional kwargs passed to pyarrow.parquet.read_table(). Returns ------- GeoDataFrame Examples -------- >>> df = geopandas.read_parquet("data.parquet") # doctest: +SKIP Specifying columns to read: >>> df = geopandas.read_parquet( ... "data.parquet", ... columns=["geometry", "pop_est"] ... ) # doctest: +SKIP """ parquet = import_optional_dependency( "pyarrow.parquet", extra="pyarrow is required for Parquet support." ) # TODO(https://github.com/pandas-dev/pandas/pull/41194): see if pandas # adds filesystem as a keyword and match that. filesystem = kwargs.pop("filesystem", None) filesystem, path = _get_filesystem_path( path, filesystem=filesystem, storage_options=storage_options ) path = _expand_user(path) kwargs["use_pandas_metadata"] = True table = parquet.read_table(path, columns=columns, filesystem=filesystem, **kwargs) return _arrow_to_geopandas(table) def _read_feather(path, columns=None, **kwargs): """ Load a Feather object from the file path, returning a GeoDataFrame. You can read a subset of columns in the file using the ``columns`` parameter. However, the structure of the returned GeoDataFrame will depend on which columns you read: * if no geometry columns are read, this will raise a ``ValueError`` - you should use the pandas `read_feather` method instead. * if the primary geometry column saved to this file is not included in columns, the first available geometry column will be set as the geometry column of the returned GeoDataFrame. Requires 'pyarrow' >= 0.17. .. versionadded:: 0.8 Parameters ---------- path : str, path object columns : list-like of strings, default=None If not None, only these columns will be read from the file. If the primary geometry column is not included, the first secondary geometry read from the file will be set as the geometry column of the returned GeoDataFrame. If no geometry columns are present, a ``ValueError`` will be raised. **kwargs Any additional kwargs passed to pyarrow.feather.read_table(). Returns ------- GeoDataFrame Examples -------- >>> df = geopandas.read_feather("data.feather") # doctest: +SKIP Specifying columns to read: >>> df = geopandas.read_feather( ... "data.feather", ... columns=["geometry", "pop_est"] ... ) # doctest: +SKIP """ feather = import_optional_dependency( "pyarrow.feather", extra="pyarrow is required for Feather support." ) # TODO move this into `import_optional_dependency` import pyarrow if pyarrow.__version__ < LooseVersion("0.17.0"): raise ImportError("pyarrow >= 0.17 required for Feather support") path = _expand_user(path) table = feather.read_table(path, columns=columns, **kwargs) return _arrow_to_geopandas(table) geopandas-0.10.2/geopandas/io/file.py000066400000000000000000000363411413247402600174060ustar00rootroot00000000000000import os from distutils.version import LooseVersion from pathlib import Path import warnings import numpy as np import pandas as pd import pyproj from shapely.geometry import mapping from shapely.geometry.base import BaseGeometry try: import fiona fiona_import_error = None # only try to import fiona.Env if the main fiona import succeeded (otherwise you # can get confusing "AttributeError: module 'fiona' has no attribute '_loading'" # / partially initialized module errors) try: from fiona import Env as fiona_env except ImportError: try: from fiona import drivers as fiona_env except ImportError: fiona_env = None except ImportError as err: fiona = None fiona_import_error = str(err) from geopandas import GeoDataFrame, GeoSeries # Adapted from pandas.io.common from urllib.request import urlopen as _urlopen from urllib.parse import urlparse as parse_url from urllib.parse import uses_netloc, uses_params, uses_relative _VALID_URLS = set(uses_relative + uses_netloc + uses_params) _VALID_URLS.discard("") _EXTENSION_TO_DRIVER = { ".bna": "BNA", ".dxf": "DXF", ".csv": "CSV", ".shp": "ESRI Shapefile", ".dbf": "ESRI Shapefile", ".json": "GeoJSON", ".geojson": "GeoJSON", ".geojsonl": "GeoJSONSeq", ".geojsons": "GeoJSONSeq", ".gpkg": "GPKG", ".gml": "GML", ".xml": "GML", ".gpx": "GPX", ".gtm": "GPSTrackMaker", ".gtz": "GPSTrackMaker", ".tab": "MapInfo File", ".mif": "MapInfo File", ".mid": "MapInfo File", ".dgn": "DGN", } def _expand_user(path): """Expand paths that use ~.""" if isinstance(path, str): path = os.path.expanduser(path) elif isinstance(path, Path): path = path.expanduser() return path def _check_fiona(func): if fiona is None: raise ImportError( f"the {func} requires the 'fiona' package, but it is not installed or does " f"not import correctly.\nImporting fiona resulted in: {fiona_import_error}" ) def _is_url(url): """Check to see if *url* has a valid protocol.""" try: return parse_url(url).scheme in _VALID_URLS except Exception: return False def _is_zip(path): """Check if a given path is a zipfile""" parsed = fiona.path.ParsedPath.from_uri(path) return ( parsed.archive.endswith(".zip") if parsed.archive else parsed.path.endswith(".zip") ) def _read_file(filename, bbox=None, mask=None, rows=None, **kwargs): """ Returns a GeoDataFrame from a file or URL. .. versionadded:: 0.7.0 mask, rows Parameters ---------- filename : str, path object or file-like object Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO) bbox : tuple | GeoDataFrame or GeoSeries | shapely Geometry, default None Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Tuple is (minx, miny, maxx, maxy) to match the bounds property of shapely geometry objects. Cannot be used with mask. mask : dict | GeoDataFrame or GeoSeries | shapely Geometry, default None Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. rows : int or slice, default None Load in specific rows by passing an integer (first `n` rows) or a slice() object. **kwargs : Keyword args to be passed to the `open` or `BytesCollection` method in the fiona library when opening the file. For more information on possible keywords, type: ``import fiona; help(fiona.open)`` Examples -------- >>> df = geopandas.read_file("nybb.shp") # doctest: +SKIP Specifying layer of GPKG: >>> df = geopandas.read_file("file.gpkg", layer='cities') # doctest: +SKIP Reading only first 10 rows: >>> df = geopandas.read_file("nybb.shp", rows=10) # doctest: +SKIP Reading only geometries intersecting ``mask``: >>> df = geopandas.read_file("nybb.shp", mask=polygon) # doctest: +SKIP Reading only geometries intersecting ``bbox``: >>> df = geopandas.read_file("nybb.shp", bbox=(0, 0, 10, 20)) # doctest: +SKIP Returns ------- :obj:`geopandas.GeoDataFrame` or :obj:`pandas.DataFrame` : If `ignore_geometry=True` a :obj:`pandas.DataFrame` will be returned. Notes ----- The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. """ _check_fiona("'read_file' function") filename = _expand_user(filename) if _is_url(filename): req = _urlopen(filename) path_or_bytes = req.read() reader = fiona.BytesCollection elif pd.api.types.is_file_like(filename): data = filename.read() path_or_bytes = data.encode("utf-8") if isinstance(data, str) else data reader = fiona.BytesCollection else: # Opening a file via URL or file-like-object above automatically detects a # zipped file. In order to match that behavior, attempt to add a zip scheme # if missing. if _is_zip(str(filename)): parsed = fiona.parse_path(str(filename)) if isinstance(parsed, fiona.path.ParsedPath): # If fiona is able to parse the path, we can safely look at the scheme # and update it to have a zip scheme if necessary. schemes = (parsed.scheme or "").split("+") if "zip" not in schemes: parsed.scheme = "+".join(["zip"] + schemes) filename = parsed.name elif isinstance(parsed, fiona.path.UnparsedPath) and not str( filename ).startswith("/vsi"): # If fiona is unable to parse the path, it might have a Windows drive # scheme. Try adding zip:// to the front. If the path starts with "/vsi" # it is a legacy GDAL path type, so let it pass unmodified. filename = "zip://" + parsed.name path_or_bytes = filename reader = fiona.open with fiona_env(): with reader(path_or_bytes, **kwargs) as features: # In a future Fiona release the crs attribute of features will # no longer be a dict, but will behave like a dict. So this should # be forwards compatible crs = ( features.crs["init"] if features.crs and "init" in features.crs else features.crs_wkt ) # handle loading the bounding box if bbox is not None: if isinstance(bbox, (GeoDataFrame, GeoSeries)): bbox = tuple(bbox.to_crs(crs).total_bounds) elif isinstance(bbox, BaseGeometry): bbox = bbox.bounds assert len(bbox) == 4 # handle loading the mask elif isinstance(mask, (GeoDataFrame, GeoSeries)): mask = mapping(mask.to_crs(crs).unary_union) elif isinstance(mask, BaseGeometry): mask = mapping(mask) # setup the data loading filter if rows is not None: if isinstance(rows, int): rows = slice(rows) elif not isinstance(rows, slice): raise TypeError("'rows' must be an integer or a slice.") f_filt = features.filter( rows.start, rows.stop, rows.step, bbox=bbox, mask=mask ) elif any((bbox, mask)): f_filt = features.filter(bbox=bbox, mask=mask) else: f_filt = features # get list of columns columns = list(features.schema["properties"]) if kwargs.get("ignore_geometry", False): return pd.DataFrame( [record["properties"] for record in f_filt], columns=columns ) return GeoDataFrame.from_features( f_filt, crs=crs, columns=columns + ["geometry"] ) def read_file(*args, **kwargs): import warnings warnings.warn( "geopandas.io.file.read_file() is intended for internal " "use only, and will be deprecated. Use geopandas.read_file() instead.", DeprecationWarning, stacklevel=2, ) return _read_file(*args, **kwargs) def to_file(*args, **kwargs): import warnings warnings.warn( "geopandas.io.file.to_file() is intended for internal " "use only, and will be deprecated. Use GeoDataFrame.to_file() " "or GeoSeries.to_file() instead.", DeprecationWarning, stacklevel=2, ) return _to_file(*args, **kwargs) def _detect_driver(path): """ Attempt to auto-detect driver based on the extension """ try: # in case the path is a file handle path = path.name except AttributeError: pass try: return _EXTENSION_TO_DRIVER[Path(path).suffix.lower()] except KeyError: # Assume it is a shapefile folder for now. In the future, # will likely raise an exception when the expected # folder writing behavior is more clearly defined. return "ESRI Shapefile" def _to_file( df, filename, driver=None, schema=None, index=None, mode="w", crs=None, **kwargs, ): """ Write this GeoDataFrame to an OGR data source A dictionary of supported OGR providers is available via: >>> import fiona >>> fiona.supported_drivers # doctest: +SKIP Parameters ---------- df : GeoDataFrame to be written filename : string File path or file handle to write to. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. schema : dict, default None If specified, the schema dictionary is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the schema based on each column's dtype index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. mode : string, default 'w' The write mode, 'w' to overwrite the existing file and 'a' to append. Not all drivers support appending. The drivers that support appending are listed in fiona.supported_drivers or https://github.com/Toblerity/Fiona/blob/master/fiona/drvsupport.py crs : pyproj.CRS, default None If specified, the CRS is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the crs based on crs df attribute. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. The *kwargs* are passed to fiona.open and can be used to write to multi-layer data, store data within archives (zip files), etc. The path may specify a fiona VSI scheme. Notes ----- The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. """ _check_fiona("'to_file' method") filename = _expand_user(filename) if index is None: # Determine if index attribute(s) should be saved to file index = list(df.index.names) != [None] or type(df.index) not in ( pd.RangeIndex, pd.Int64Index, ) if index: df = df.reset_index(drop=False) if schema is None: schema = infer_schema(df) if crs: crs = pyproj.CRS.from_user_input(crs) else: crs = df.crs if driver is None: driver = _detect_driver(filename) if driver == "ESRI Shapefile" and any([len(c) > 10 for c in df.columns.tolist()]): warnings.warn( "Column names longer than 10 characters will be truncated when saved to " "ESRI Shapefile.", stacklevel=3, ) with fiona_env(): crs_wkt = None try: gdal_version = fiona.env.get_gdal_release_name() except AttributeError: gdal_version = "2.0.0" # just assume it is not the latest if LooseVersion(gdal_version) >= LooseVersion("3.0.0") and crs: crs_wkt = crs.to_wkt() elif crs: crs_wkt = crs.to_wkt("WKT1_GDAL") with fiona.open( filename, mode=mode, driver=driver, crs_wkt=crs_wkt, schema=schema, **kwargs ) as colxn: colxn.writerecords(df.iterfeatures()) def infer_schema(df): from collections import OrderedDict # TODO: test pandas string type and boolean type once released types = {"Int64": "int", "string": "str", "boolean": "bool"} def convert_type(column, in_type): if in_type == object: return "str" if in_type.name.startswith("datetime64"): # numpy datetime type regardless of frequency return "datetime" if str(in_type) in types: out_type = types[str(in_type)] else: out_type = type(np.zeros(1, in_type).item()).__name__ if out_type == "long": out_type = "int" return out_type properties = OrderedDict( [ (col, convert_type(col, _type)) for col, _type in zip(df.columns, df.dtypes) if col != df._geometry_column_name ] ) if df.empty: raise ValueError("Cannot write empty DataFrame to file.") # Since https://github.com/Toblerity/Fiona/issues/446 resolution, # Fiona allows a list of geometry types geom_types = _geometry_types(df) schema = {"geometry": geom_types, "properties": properties} return schema def _geometry_types(df): """ Determine the geometry types in the GeoDataFrame for the schema. """ geom_types_2D = df[~df.geometry.has_z].geometry.geom_type.unique() geom_types_2D = [gtype for gtype in geom_types_2D if gtype is not None] geom_types_3D = df[df.geometry.has_z].geometry.geom_type.unique() geom_types_3D = ["3D " + gtype for gtype in geom_types_3D if gtype is not None] geom_types = geom_types_3D + geom_types_2D if len(geom_types) == 0: # Default geometry type supported by Fiona # (Since https://github.com/Toblerity/Fiona/issues/446 resolution) return "Unknown" if len(geom_types) == 1: geom_types = geom_types[0] return geom_types geopandas-0.10.2/geopandas/io/sql.py000066400000000000000000000334601413247402600172650ustar00rootroot00000000000000import warnings from contextlib import contextmanager import pandas as pd import shapely.wkb from geopandas import GeoDataFrame from .. import _compat as compat @contextmanager def _get_conn(conn_or_engine): """ Yield a connection within a transaction context. Engine.begin() returns a Connection with an implicit Transaction while Connection.begin() returns the Transaction. This helper will always return a Connection with an implicit (possibly nested) Transaction. Parameters ---------- conn_or_engine : Connection or Engine A sqlalchemy Connection or Engine instance Returns ------- Connection """ from sqlalchemy.engine.base import Engine, Connection if isinstance(conn_or_engine, Connection): with conn_or_engine.begin(): yield conn_or_engine elif isinstance(conn_or_engine, Engine): with conn_or_engine.begin() as conn: yield conn else: raise ValueError(f"Unknown Connectable: {conn_or_engine}") def _df_to_geodf(df, geom_col="geom", crs=None): """ Transforms a pandas DataFrame into a GeoDataFrame. The column 'geom_col' must be a geometry column in WKB representation. To be used to convert df based on pd.read_sql to gdf. Parameters ---------- df : DataFrame pandas DataFrame with geometry column in WKB representation. geom_col : string, default 'geom' column name to convert to shapely geometries crs : pyproj.CRS, optional CRS to use for the returned GeoDataFrame. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. If not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Returns ------- GeoDataFrame """ if geom_col not in df: raise ValueError("Query missing geometry column '{}'".format(geom_col)) geoms = df[geom_col].dropna() if not geoms.empty: load_geom_bytes = shapely.wkb.loads """Load from Python 3 binary.""" def load_geom_buffer(x): """Load from Python 2 binary.""" return shapely.wkb.loads(str(x)) def load_geom_text(x): """Load from binary encoded as text.""" return shapely.wkb.loads(str(x), hex=True) if isinstance(geoms.iat[0], bytes): load_geom = load_geom_bytes else: load_geom = load_geom_text df[geom_col] = geoms = geoms.apply(load_geom) if crs is None: srid = shapely.geos.lgeos.GEOSGetSRID(geoms.iat[0]._geom) # if no defined SRID in geodatabase, returns SRID of 0 if srid != 0: crs = "epsg:{}".format(srid) return GeoDataFrame(df, crs=crs, geometry=geom_col) def _read_postgis( sql, con, geom_col="geom", crs=None, index_col=None, coerce_float=True, parse_dates=None, params=None, chunksize=None, ): """ Returns a GeoDataFrame corresponding to the result of the query string, which must contain a geometry column in WKB representation. Parameters ---------- sql : string SQL query to execute in selecting entries from database, or name of the table to read from the database. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the database to query. geom_col : string, default 'geom' column name to convert to shapely geometries crs : dict or str, optional CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. chunksize : int, default None If specified, return an iterator where chunksize is the number of rows to include in each chunk. See the documentation for pandas.read_sql for further explanation of the following parameters: index_col, coerce_float, parse_dates, params, chunksize Returns ------- GeoDataFrame Examples -------- PostGIS >>> from sqlalchemy import create_engine # doctest: +SKIP >>> db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydatabase" >>> con = create_engine(db_connection_url) # doctest: +SKIP >>> sql = "SELECT geom, highway FROM roads" >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP SpatiaLite >>> sql = "SELECT ST_Binary(geom) AS geom, highway FROM roads" >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP """ if chunksize is None: # read all in one chunk and return a single GeoDataFrame df = pd.read_sql( sql, con, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return _df_to_geodf(df, geom_col=geom_col, crs=crs) else: # read data in chunks and return a generator df_generator = pd.read_sql( sql, con, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return (_df_to_geodf(df, geom_col=geom_col, crs=crs) for df in df_generator) def read_postgis(*args, **kwargs): import warnings warnings.warn( "geopandas.io.sql.read_postgis() is intended for internal " "use only, and will be deprecated. Use geopandas.read_postgis() instead.", DeprecationWarning, stacklevel=2, ) return _read_postgis(*args, **kwargs) def _get_geometry_type(gdf): """ Get basic geometry type of a GeoDataFrame. See more info from: https://geoalchemy-2.readthedocs.io/en/latest/types.html#geoalchemy2.types._GISType Following rules apply: - if geometries all share the same geometry-type, geometries are inserted with the given GeometryType with following types: - Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, GeometryCollection. - LinearRing geometries will be converted into LineString -objects. - in all other cases, geometries will be inserted with type GEOMETRY: - a mix of Polygons and MultiPolygons in GeoSeries - a mix of Points and LineStrings in GeoSeries - geometry is of type GeometryCollection, such as GeometryCollection([Point, LineStrings]) - if any of the geometries has Z-coordinate, all records will be written with 3D. """ geom_types = list(gdf.geometry.geom_type.unique()) has_curve = False for gt in geom_types: if gt is None: continue elif "LinearRing" in gt: has_curve = True if len(geom_types) == 1: if has_curve: target_geom_type = "LINESTRING" else: if geom_types[0] is None: raise ValueError("No valid geometries in the data.") else: target_geom_type = geom_types[0].upper() else: target_geom_type = "GEOMETRY" # Check for 3D-coordinates if any(gdf.geometry.has_z): target_geom_type = target_geom_type + "Z" return target_geom_type, has_curve def _get_srid_from_crs(gdf): """ Get EPSG code from CRS if available. If not, return -1. """ # Use geoalchemy2 default for srid # Note: undefined srid in PostGIS is 0 srid = -1 warning_msg = ( "Could not parse CRS from the GeoDataFrame. " + "Inserting data without defined CRS.", ) if gdf.crs is not None: try: srid = gdf.crs.to_epsg(min_confidence=25) if srid is None: srid = -1 warnings.warn(warning_msg, UserWarning, stacklevel=2) except Exception: warnings.warn(warning_msg, UserWarning, stacklevel=2) return srid def _convert_linearring_to_linestring(gdf, geom_name): from shapely.geometry import LineString # Todo: Use Pygeos function once it's implemented: # https://github.com/pygeos/pygeos/issues/76 mask = gdf.geom_type == "LinearRing" gdf.loc[mask, geom_name] = gdf.loc[mask, geom_name].apply( lambda geom: LineString(geom) ) return gdf def _convert_to_ewkb(gdf, geom_name, srid): """Convert geometries to ewkb. """ if compat.USE_PYGEOS: from pygeos import set_srid, to_wkb geoms = to_wkb( set_srid(gdf[geom_name].values.data, srid=srid), hex=True, include_srid=True ) else: from shapely.wkb import dumps geoms = [dumps(geom, srid=srid, hex=True) for geom in gdf[geom_name]] # The gdf will warn that the geometry column doesn't hold in-memory geometries # now that they are EWKB, so convert back to a regular dataframe to avoid warning # the user that the dtypes are unexpected. df = pd.DataFrame(gdf, copy=False) df[geom_name] = geoms return df def _psql_insert_copy(tbl, conn, keys, data_iter): import io import csv s_buf = io.StringIO() writer = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ", ".join('"{}"'.format(k) for k in keys) dbapi_conn = conn.connection with dbapi_conn.cursor() as cur: sql = 'COPY "{}"."{}" ({}) FROM STDIN WITH CSV'.format( tbl.table.schema, tbl.table.name, columns ) cur.copy_expert(sql=sql, file=s_buf) def _write_postgis( gdf, name, con, schema=None, if_exists="fail", index=False, index_label=None, chunksize=None, dtype=None, ): """ Upload GeoDataFrame into PostGIS database. This method requires SQLAlchemy and GeoAlchemy2, and a PostgreSQL Python driver (e.g. psycopg2) to be installed. Parameters ---------- name : str Name of the target table. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the PostGIS database. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists: - fail: Raise a ValueError. - replace: Drop the table before inserting new values. - append: Insert new values to the existing table. schema : string, optional Specify the schema. If None, use default schema: 'public'. index : bool, default True Write DataFrame index as a column. Uses *index_label* as the column name in the table. index_label : string or sequence, default None Column label for index column(s). If None is given (default) and index is True, then the index names are used. chunksize : int, optional Rows will be written in batches of this size at a time. By default, all rows will be written at once. dtype : dict of column name to SQL type, default None Specifying the datatype for columns. The keys should be the column names and the values should be the SQLAlchemy types. Examples -------- >>> from sqlalchemy import create_engine # doctest: +SKIP >>> engine = create_engine("postgresql://myusername:mypassword@myhost:5432\ /mydatabase";) # doctest: +SKIP >>> gdf.to_postgis("my_table", engine) # doctest: +SKIP """ try: from geoalchemy2 import Geometry except ImportError: raise ImportError("'to_postgis()' requires geoalchemy2 package. ") if not compat.SHAPELY_GE_17: raise ImportError( "'to_postgis()' requires newer version of Shapely " "(>= '1.7.0').\nYou can update the library using " "'pip install shapely --upgrade' or using " "'conda update shapely' if using conda package manager." ) gdf = gdf.copy() geom_name = gdf.geometry.name # Get srid srid = _get_srid_from_crs(gdf) # Get geometry type and info whether data contains LinearRing. geometry_type, has_curve = _get_geometry_type(gdf) # Build dtype with Geometry if dtype is not None: dtype[geom_name] = Geometry(geometry_type=geometry_type, srid=srid) else: dtype = {geom_name: Geometry(geometry_type=geometry_type, srid=srid)} # Convert LinearRing geometries to LineString if has_curve: gdf = _convert_linearring_to_linestring(gdf, geom_name) # Convert geometries to EWKB gdf = _convert_to_ewkb(gdf, geom_name, srid) if schema is not None: schema_name = schema else: schema_name = "public" if if_exists == "append": # Check that the geometry srid matches with the current GeoDataFrame with _get_conn(con) as connection: # Only check SRID if table exists if connection.dialect.has_table(connection, name, schema): target_srid = connection.execute( "SELECT Find_SRID('{schema}', '{table}', '{geom_col}');".format( schema=schema_name, table=name, geom_col=geom_name ) ).fetchone()[0] if target_srid != srid: msg = ( "The CRS of the target table (EPSG:{epsg_t}) differs from the " "CRS of current GeoDataFrame (EPSG:{epsg_src}).".format( epsg_t=target_srid, epsg_src=srid ) ) raise ValueError(msg) with _get_conn(con) as connection: gdf.to_sql( name, connection, schema=schema_name, if_exists=if_exists, index=index, index_label=index_label, chunksize=chunksize, dtype=dtype, method=_psql_insert_copy, ) return geopandas-0.10.2/geopandas/io/tests/000077500000000000000000000000001413247402600172505ustar00rootroot00000000000000geopandas-0.10.2/geopandas/io/tests/__init__.py000066400000000000000000000000001413247402600213470ustar00rootroot00000000000000geopandas-0.10.2/geopandas/io/tests/data/000077500000000000000000000000001413247402600201615ustar00rootroot00000000000000geopandas-0.10.2/geopandas/io/tests/data/pickle/000077500000000000000000000000001413247402600214305ustar00rootroot00000000000000geopandas-0.10.2/geopandas/io/tests/data/pickle/0.5.1_pd-0.25.3_py-3.7.3_x86_64_linux.pickle000066400000000000000000000030061413247402600301020ustar00rootroot00000000000000}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhhKhR(KKKh#](hI)RC??bhI)RC@@bhI)RC@@betbe](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}he}(hghhh](}(hkhhlhoKKKRu}(hkhhlhoKKKRueustbhvhwhxhyhz EPSG:4326h{hubu.geopandas-0.10.2/geopandas/io/tests/data/pickle/0.6.3_pd-0.25.3_py-3.8.0_x86_64_linux.pickle000066400000000000000000000030721413247402600301060ustar00rootroot00000000000000/}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhD)}hhhKhR(KKh#](hN)RC??bhN)RC@@bhN)RC@@betbsbe](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}hj}(hlhhm](}(hphhqhtKKKRu}(hphhqhtKKKRueustbh{h|h}h~h EPSG:4326hhubu.geopandas-0.10.2/geopandas/io/tests/data/pickle/0.7.0_pd-1.0.4_py-3.7.6_x86_64_linux.pickle000066400000000000000000000031461413247402600300260ustar00rootroot00000000000000[}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhD)}hhhKhR(KKh#](hN)RC??bhN)RC@@bhN)RC@@betbsbe](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}hj}(hlhhm](}(hphhqhtKKKRu}(hphhqhtKKKRueustbh{h|h}h~h}hpyproj.crs.crsCRS EPSG:4326Rhhubu.geopandas-0.10.2/geopandas/io/tests/data/pickle/0.8.0_pd-1.0.5_py-3.8.3_x86_64_linux.pickle000066400000000000000000000031571413247402600300300ustar00rootroot00000000000000d}( gdf_the_geomgeopandas.geodataframe GeoDataFrame)}(_datapandas.core.internals.managers BlockManager)(](pandas.core.indexes.base _new_Indexh Index}(datanumpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypeO8KKR(K|NNNJJK?tb](athe_geometbnameNuRhpandas.core.indexes.range RangeIndex}(h*NstartKstopKstepKuRe](hhKhR(KKKh i8KKR(KNNNJJKtbC??333333?tbhD)}(hhhKhR(KKh#](hN)RC??bhN)RC@@bhN)RC@@betbhVpyproj.crs.crsCRS EPSG:4326Rube](hh}(hhhKhR(KKh#]h'atbh*NuRhh}(hhhKhR(KKh#]hatbh*NuRe}hk}(hmhhn](}(hqhhrhuKKKRu}(hqhhrhuKKKRueustbh|h}h~hh}hVhhhubu.geopandas-0.10.2/geopandas/io/tests/generate_legacy_storage_files.py000066400000000000000000000052601413247402600256510ustar00rootroot00000000000000""" Script to create the data and write legacy storage (pickle) files. Based on pandas' generate_legacy_storage_files.py script. To use this script, create an environment for which you want to generate pickles, activate the environment, and run this script as: $ python geopandas/geopandas/io/tests/generate_legacy_storage_files.py \ geopandas/geopandas/io/tests/data/pickle/ pickle This script generates a storage file for the current arch, system, The idea here is you are using the *current* version of the generate_legacy_storage_files with an *older* version of geopandas to generate a pickle file. We will then check this file into a current branch, and test using test_pickle.py. This will load the *older* pickles and test versus the current data that is generated (with master). These are then compared. """ import os import pickle import platform import sys import pandas as pd import geopandas from shapely.geometry import Point def create_pickle_data(): """ create the pickle data """ # custom geometry column name gdf_the_geom = geopandas.GeoDataFrame( {"a": [1, 2, 3], "the_geom": [Point(1, 1), Point(2, 2), Point(3, 3)]}, geometry="the_geom", ) # with crs gdf_crs = geopandas.GeoDataFrame( {"a": [0.1, 0.2, 0.3], "geometry": [Point(1, 1), Point(2, 2), Point(3, 3)]}, crs="EPSG:4326", ) return dict(gdf_the_geom=gdf_the_geom, gdf_crs=gdf_crs) def platform_name(): return "_".join( [ str(geopandas.__version__), "pd-" + str(pd.__version__), "py-" + str(platform.python_version()), str(platform.machine()), str(platform.system().lower()), ] ) def write_legacy_pickles(output_dir): print( "This script generates a storage file for the current arch, system, " "and python version" ) print("geopandas version: {}").format(geopandas.__version__) print(" output dir : {}".format(output_dir)) print(" storage format: pickle") pth = "{}.pickle".format(platform_name()) fh = open(os.path.join(output_dir, pth), "wb") pickle.dump(create_pickle_data(), fh, pickle.DEFAULT_PROTOCOL) fh.close() print("created pickle file: {}".format(pth)) def main(): if len(sys.argv) != 3: exit( "Specify output directory and storage type: generate_legacy_" "storage_files.py " ) output_dir = str(sys.argv[1]) storage_type = str(sys.argv[2]) if storage_type == "pickle": write_legacy_pickles(output_dir=output_dir) else: exit("storage_type must be one of {'pickle'}") if __name__ == "__main__": main() geopandas-0.10.2/geopandas/io/tests/test_arrow.py000066400000000000000000000414431413247402600220210ustar00rootroot00000000000000from __future__ import absolute_import from distutils.version import LooseVersion import os import pytest from pandas import DataFrame, read_parquet as pd_read_parquet from pandas.testing import assert_frame_equal import numpy as np from shapely.geometry import box import geopandas from geopandas import GeoDataFrame, read_file, read_parquet, read_feather from geopandas.array import to_wkb from geopandas.datasets import get_path from geopandas.io.arrow import ( _create_metadata, _decode_metadata, _encode_metadata, _get_filesystem_path, _validate_dataframe, _validate_metadata, METADATA_VERSION, ) from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal # Skip all tests in this module if pyarrow is not available pyarrow = pytest.importorskip("pyarrow") # TEMPORARY: hide warning from to_parquet pytestmark = pytest.mark.filterwarnings("ignore:.*initial implementation of Parquet.*") @pytest.fixture( params=[ "parquet", pytest.param( "feather", marks=pytest.mark.skipif( pyarrow.__version__ < LooseVersion("0.17.0"), reason="needs pyarrow >= 0.17", ), ), ] ) def file_format(request): if request.param == "parquet": return read_parquet, GeoDataFrame.to_parquet elif request.param == "feather": return read_feather, GeoDataFrame.to_feather def test_create_metadata(): test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) metadata = _create_metadata(df) assert isinstance(metadata, dict) assert metadata["schema_version"] == METADATA_VERSION assert metadata["creator"]["library"] == "geopandas" assert metadata["creator"]["version"] == geopandas.__version__ assert metadata["primary_column"] == "geometry" assert "geometry" in metadata["columns"] assert metadata["columns"]["geometry"]["crs"] == df.geometry.crs.to_wkt() assert metadata["columns"]["geometry"]["encoding"] == "WKB" assert np.array_equal( metadata["columns"]["geometry"]["bbox"], df.geometry.total_bounds ) def test_encode_metadata(): metadata = {"a": "b"} expected = b'{"a": "b"}' assert _encode_metadata(metadata) == expected def test_decode_metadata(): metadata_str = b'{"a": "b"}' expected = {"a": "b"} assert _decode_metadata(metadata_str) == expected def test_validate_dataframe(): test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) # valid: should not raise ValueError _validate_dataframe(df) _validate_dataframe(df.set_index("iso_a3")) # add column with non-string type df[0] = 1 # invalid: should raise ValueError with pytest.raises(ValueError): _validate_dataframe(df) with pytest.raises(ValueError): _validate_dataframe(df.set_index(0)) # not a DataFrame: should raise ValueError with pytest.raises(ValueError): _validate_dataframe("not a dataframe") def test_validate_metadata_valid(): _validate_metadata( { "primary_column": "geometry", "columns": {"geometry": {"crs": None, "encoding": "WKB"}}, } ) _validate_metadata( { "primary_column": "geometry", "columns": {"geometry": {"crs": "WKT goes here", "encoding": "WKB"}}, } ) @pytest.mark.parametrize( "metadata,error", [ ({}, "Missing or malformed geo metadata in Parquet/Feather file"), ( {"primary_column": "foo"}, "'geo' metadata in Parquet/Feather file is missing required key:", ), ( {"primary_column": "foo", "columns": None}, "'geo' metadata in Parquet/Feather file is missing required key", ), ( {"primary_column": "foo", "columns": []}, "'columns' in 'geo' metadata must be a dict", ), ( {"primary_column": "foo", "columns": {"foo": {}}}, ( "'geo' metadata in Parquet/Feather file is missing required key 'crs' " "for column 'foo'" ), ), ( {"primary_column": "foo", "columns": {"foo": {"crs": None}}}, "'geo' metadata in Parquet/Feather file is missing required key", ), ( {"primary_column": "foo", "columns": {"foo": {"encoding": None}}}, "'geo' metadata in Parquet/Feather file is missing required key", ), ( { "primary_column": "foo", "columns": {"foo": {"crs": None, "encoding": None}}, }, "Only WKB geometry encoding is supported", ), ( { "primary_column": "foo", "columns": {"foo": {"crs": None, "encoding": "BKW"}}, }, "Only WKB geometry encoding is supported", ), ], ) def test_validate_metadata_invalid(metadata, error): with pytest.raises(ValueError, match=error): _validate_metadata(metadata) # TEMPORARY: used to determine if pyarrow fails for roundtripping pandas data # without geometries def test_pandas_parquet_roundtrip1(tmpdir): df = DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename) pq_df = pd_read_parquet(filename) assert_frame_equal(df, pq_df) @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb"] ) def test_pandas_parquet_roundtrip2(test_dataset, tmpdir): test_dataset = "naturalearth_lowres" df = DataFrame(read_file(get_path(test_dataset)).drop(columns=["geometry"])) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename) pq_df = pd_read_parquet(filename) assert_frame_equal(df, pq_df) @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb"] ) def test_roundtrip(tmpdir, file_format, test_dataset): """Writing to parquet should not raise errors, and should not alter original GeoDataFrame """ reader, writer = file_format df = read_file(get_path(test_dataset)) orig = df.copy() filename = os.path.join(str(tmpdir), "test.pq") # TEMP: Initial implementation should raise a UserWarning with pytest.warns(UserWarning, match="initial implementation"): writer(df, filename) assert os.path.exists(filename) # make sure that the original data frame is unaltered assert_geodataframe_equal(df, orig) # make sure that we can roundtrip the data frame pq_df = reader(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) def test_index(tmpdir, file_format): """Setting index=`True` should preserve index in output, and setting index=`False` should drop index from output. """ reader, writer = file_format test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)).set_index("iso_a3") filename = os.path.join(str(tmpdir), "test_with_index.pq") writer(df, filename, index=True) pq_df = reader(filename) assert_geodataframe_equal(df, pq_df) filename = os.path.join(str(tmpdir), "drop_index.pq") writer(df, filename, index=False) pq_df = reader(filename) assert_geodataframe_equal(df.reset_index(drop=True), pq_df) @pytest.mark.parametrize("compression", ["snappy", "gzip", "brotli", None]) def test_parquet_compression(compression, tmpdir): """Using compression options should not raise errors, and should return identical GeoDataFrame. """ test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) filename = os.path.join(str(tmpdir), "test.pq") df.to_parquet(filename, compression=compression) pq_df = read_parquet(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) @pytest.mark.skipif( pyarrow.__version__ < LooseVersion("0.17.0"), reason="Feather only supported for pyarrow >= 0.17", ) @pytest.mark.parametrize("compression", ["uncompressed", "lz4", "zstd"]) def test_feather_compression(compression, tmpdir): """Using compression options should not raise errors, and should return identical GeoDataFrame. """ test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) filename = os.path.join(str(tmpdir), "test.feather") df.to_feather(filename, compression=compression) pq_df = read_feather(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) def test_parquet_multiple_geom_cols(tmpdir, file_format): """If multiple geometry columns are present when written to parquet, they should all be returned as such when read from parquet. """ reader, writer = file_format test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) df["geom2"] = df.geometry.copy() filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) assert os.path.exists(filename) pq_df = reader(filename) assert isinstance(pq_df, GeoDataFrame) assert_geodataframe_equal(df, pq_df) assert_geoseries_equal(df.geom2, pq_df.geom2, check_geom_type=True) def test_parquet_missing_metadata(tmpdir): """Missing geo metadata, such as from a parquet file created from a pandas DataFrame, will raise a ValueError. """ test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) # convert to DataFrame df = DataFrame(df) # convert the geometry column so we can extract later df["geometry"] = to_wkb(df["geometry"].values) filename = os.path.join(str(tmpdir), "test.pq") # use pandas to_parquet (no geo metadata) df.to_parquet(filename) # missing metadata will raise ValueError with pytest.raises( ValueError, match="Missing geo metadata in Parquet/Feather file." ): read_parquet(filename) def test_parquet_missing_metadata2(tmpdir): """Missing geo metadata, such as from a parquet file created from a pyarrow Table (which will also not contain pandas metadata), will raise a ValueError. """ import pyarrow.parquet as pq table = pyarrow.table({"a": [1, 2, 3]}) filename = os.path.join(str(tmpdir), "test.pq") # use pyarrow.parquet write_table (no geo metadata, but also no pandas metadata) pq.write_table(table, filename) # missing metadata will raise ValueError with pytest.raises( ValueError, match="Missing geo metadata in Parquet/Feather file." ): read_parquet(filename) @pytest.mark.parametrize( "geo_meta,error", [ ({"geo": b""}, "Missing or malformed geo metadata in Parquet/Feather file"), ( {"geo": _encode_metadata({})}, "Missing or malformed geo metadata in Parquet/Feather file", ), ( {"geo": _encode_metadata({"foo": "bar"})}, "'geo' metadata in Parquet/Feather file is missing required key", ), ], ) def test_parquet_invalid_metadata(tmpdir, geo_meta, error): """Has geo metadata with missing required fields will raise a ValueError. This requires writing the parquet file directly below, so that we can control the metadata that is written for this test. """ from pyarrow import parquet, Table test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) # convert to DataFrame and encode geometry to WKB df = DataFrame(df) df["geometry"] = to_wkb(df["geometry"].values) table = Table.from_pandas(df) metadata = table.schema.metadata metadata.update(geo_meta) table = table.replace_schema_metadata(metadata) filename = os.path.join(str(tmpdir), "test.pq") parquet.write_table(table, filename) with pytest.raises(ValueError, match=error): read_parquet(filename) def test_subset_columns(tmpdir, file_format): """Reading a subset of columns should correctly decode selected geometry columns. """ reader, writer = file_format test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) pq_df = reader(filename, columns=["name", "geometry"]) assert_geodataframe_equal(df[["name", "geometry"]], pq_df) with pytest.raises( ValueError, match="No geometry columns are included in the columns read" ): reader(filename, columns=["name"]) def test_promote_secondary_geometry(tmpdir, file_format): """Reading a subset of columns that does not include the primary geometry column should promote the first geometry column present. """ reader, writer = file_format test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) df["geom2"] = df.geometry.copy() filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) pq_df = reader(filename, columns=["name", "geom2"]) assert_geodataframe_equal(df.set_geometry("geom2")[["name", "geom2"]], pq_df) df["geom3"] = df.geometry.copy() writer(df, filename) with pytest.warns( UserWarning, match="Multiple non-primary geometry columns read from Parquet/Feather file.", ): pq_df = reader(filename, columns=["name", "geom2", "geom3"]) assert_geodataframe_equal( df.set_geometry("geom2")[["name", "geom2", "geom3"]], pq_df ) def test_columns_no_geometry(tmpdir, file_format): """Reading a parquet file that is missing all of the geometry columns should raise a ValueError""" reader, writer = file_format test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) with pytest.raises(ValueError): reader(filename, columns=["name"]) def test_missing_crs(tmpdir, file_format): """If CRS is `None`, it should be properly handled and remain `None` when read from parquet`. """ reader, writer = file_format test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) df.crs = None filename = os.path.join(str(tmpdir), "test.pq") writer(df, filename) pq_df = reader(filename) assert pq_df.crs is None assert_geodataframe_equal(df, pq_df, check_crs=True) @pytest.mark.skipif( pyarrow.__version__ >= LooseVersion("0.17.0"), reason="Feather only supported for pyarrow >= 0.17", ) def test_feather_arrow_version(tmpdir): df = read_file(get_path("naturalearth_lowres")) filename = os.path.join(str(tmpdir), "test.feather") with pytest.raises( ImportError, match="pyarrow >= 0.17 required for Feather support" ): df.to_feather(filename) def test_fsspec_url(): fsspec = pytest.importorskip("fsspec") import fsspec.implementations.memory class MyMemoryFileSystem(fsspec.implementations.memory.MemoryFileSystem): # Simple fsspec filesystem that adds a required keyword. # Attempting to use this filesystem without the keyword will raise an exception. def __init__(self, is_set, *args, **kwargs): self.is_set = is_set super().__init__(*args, **kwargs) fsspec.register_implementation("memory", MyMemoryFileSystem, clobber=True) memfs = MyMemoryFileSystem(is_set=True) test_dataset = "naturalearth_lowres" df = read_file(get_path(test_dataset)) with memfs.open("data.parquet", "wb") as f: df.to_parquet(f) result = read_parquet("memory://data.parquet", storage_options=dict(is_set=True)) assert_geodataframe_equal(result, df) result = read_parquet("memory://data.parquet", filesystem=memfs) assert_geodataframe_equal(result, df) def test_non_fsspec_url_with_storage_options_raises(): with pytest.raises(ValueError, match="storage_options"): test_dataset = "naturalearth_lowres" read_parquet(get_path(test_dataset), storage_options={"foo": "bar"}) @pytest.mark.skipif( pyarrow.__version__ < LooseVersion("5.0.0"), reason="pyarrow.fs requires pyarrow>=5.0.0", ) def test_prefers_pyarrow_fs(): filesystem, _ = _get_filesystem_path("file:///data.parquet") assert isinstance(filesystem, pyarrow.fs.LocalFileSystem) def test_write_read_parquet_expand_user(): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="epsg:4326") test_file = "~/test_file.parquet" gdf.to_parquet(test_file) pq_df = geopandas.read_parquet(test_file) assert_geodataframe_equal(gdf, pq_df, check_crs=True) os.remove(os.path.expanduser(test_file)) def test_write_read_feather_expand_user(): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs="epsg:4326") test_file = "~/test_file.feather" gdf.to_feather(test_file) f_df = geopandas.read_feather(test_file) assert_geodataframe_equal(gdf, f_df, check_crs=True) os.remove(os.path.expanduser(test_file)) geopandas-0.10.2/geopandas/io/tests/test_file.py000066400000000000000000000663231413247402600216120ustar00rootroot00000000000000from collections import OrderedDict import datetime import io import os import pathlib import tempfile import numpy as np import pandas as pd import fiona from shapely.geometry import Point, Polygon, box import geopandas from geopandas import GeoDataFrame, read_file from geopandas.io.file import fiona_env, _detect_driver, _EXTENSION_TO_DRIVER from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from geopandas.tests.util import PACKAGE_DIR, validate_boro_df import pytest _CRS = "epsg:4326" @pytest.fixture def df_nybb(): nybb_path = geopandas.datasets.get_path("nybb") df = read_file(nybb_path) return df @pytest.fixture def df_null(): return read_file( os.path.join(PACKAGE_DIR, "geopandas", "tests", "data", "null_geom.geojson") ) @pytest.fixture def file_path(): return os.path.join(PACKAGE_DIR, "geopandas", "tests", "data", "null_geom.geojson") @pytest.fixture def df_points(): N = 10 crs = _CRS df = GeoDataFrame( [ {"geometry": Point(x, y), "value1": x + y, "value2": x * y} for x, y in zip(range(N), range(N)) ], crs=crs, ) return df # ----------------------------------------------------------------------------- # to_file tests # ----------------------------------------------------------------------------- driver_ext_pairs = [ ("ESRI Shapefile", ".shp"), ("GeoJSON", ".geojson"), ("GPKG", ".gpkg"), (None, ".shp"), (None, ""), (None, ".geojson"), (None, ".gpkg"), ] def assert_correct_driver(file_path, ext): # check the expected driver expected_driver = "ESRI Shapefile" if ext == "" else _EXTENSION_TO_DRIVER[ext] with fiona.open(str(file_path)) as fds: assert fds.driver == expected_driver @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file(tmpdir, df_nybb, df_null, driver, ext): """Test to_file and from_file""" tempfilename = os.path.join(str(tmpdir), "boros." + ext) df_nybb.to_file(tempfilename, driver=driver) # Read layer back in df = GeoDataFrame.from_file(tempfilename) assert "geometry" in df assert len(df) == 5 assert np.alltrue(df["BoroName"].values == df_nybb["BoroName"]) # Write layer with null geometry out to file tempfilename = os.path.join(str(tmpdir), "null_geom" + ext) df_null.to_file(tempfilename, driver=driver) # Read layer back in df = GeoDataFrame.from_file(tempfilename) assert "geometry" in df assert len(df) == 2 assert np.alltrue(df["Name"].values == df_null["Name"]) # check the expected driver assert_correct_driver(tempfilename, ext) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_pathlib(tmpdir, df_nybb, df_null, driver, ext): """Test to_file and from_file""" temppath = pathlib.Path(os.path.join(str(tmpdir), "boros." + ext)) df_nybb.to_file(temppath, driver=driver) # Read layer back in df = GeoDataFrame.from_file(temppath) assert "geometry" in df assert len(df) == 5 assert np.alltrue(df["BoroName"].values == df_nybb["BoroName"]) # check the expected driver assert_correct_driver(temppath, ext) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_bool(tmpdir, driver, ext): """Test error raise when writing with a boolean column (GH #437).""" tempfilename = os.path.join(str(tmpdir), "temp.{0}".format(ext)) df = GeoDataFrame( { "a": [1, 2, 3], "b": [True, False, True], "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], }, crs=4326, ) df.to_file(tempfilename, driver=driver) result = read_file(tempfilename) if ext in (".shp", ""): # Shapefile does not support boolean, so is read back as int df["b"] = df["b"].astype("int64") assert_geodataframe_equal(result, df) # check the expected driver assert_correct_driver(tempfilename, ext) def test_to_file_datetime(tmpdir): """Test writing a data file with the datetime column type""" tempfilename = os.path.join(str(tmpdir), "test_datetime.gpkg") point = Point(0, 0) now = datetime.datetime.now() df = GeoDataFrame({"a": [1, 2], "b": [now, now]}, geometry=[point, point], crs=4326) df.to_file(tempfilename, driver="GPKG") df_read = read_file(tempfilename) assert_geoseries_equal(df.geometry, df_read.geometry) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_with_point_z(tmpdir, ext, driver): """Test that 3D geometries are retained in writes (GH #612).""" tempfilename = os.path.join(str(tmpdir), "test_3Dpoint" + ext) point3d = Point(0, 0, 500) point2d = Point(1, 1) df = GeoDataFrame({"a": [1, 2]}, geometry=[point3d, point2d], crs=_CRS) df.to_file(tempfilename, driver=driver) df_read = GeoDataFrame.from_file(tempfilename) assert_geoseries_equal(df.geometry, df_read.geometry) # check the expected driver assert_correct_driver(tempfilename, ext) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_to_file_with_poly_z(tmpdir, ext, driver): """Test that 3D geometries are retained in writes (GH #612).""" tempfilename = os.path.join(str(tmpdir), "test_3Dpoly" + ext) poly3d = Polygon([[0, 0, 5], [0, 1, 5], [1, 1, 5], [1, 0, 5]]) poly2d = Polygon([[0, 0], [0, 1], [1, 1], [1, 0]]) df = GeoDataFrame({"a": [1, 2]}, geometry=[poly3d, poly2d], crs=_CRS) df.to_file(tempfilename, driver=driver) df_read = GeoDataFrame.from_file(tempfilename) assert_geoseries_equal(df.geometry, df_read.geometry) # check the expected driver assert_correct_driver(tempfilename, ext) def test_to_file_types(tmpdir, df_points): """Test various integer type columns (GH#93)""" tempfilename = os.path.join(str(tmpdir), "int.shp") int_types = [ np.int8, np.int16, np.int32, np.int64, np.intp, np.uint8, np.uint16, np.uint32, np.uint64, ] geometry = df_points.geometry data = dict( (str(i), np.arange(len(geometry), dtype=dtype)) for i, dtype in enumerate(int_types) ) df = GeoDataFrame(data, geometry=geometry) df.to_file(tempfilename) def test_to_file_int64(tmpdir, df_points): tempfilename = os.path.join(str(tmpdir), "int64.shp") geometry = df_points.geometry df = GeoDataFrame(geometry=geometry) df["data"] = pd.array([1, np.nan] * 5, dtype=pd.Int64Dtype()) df.to_file(tempfilename) df_read = GeoDataFrame.from_file(tempfilename) assert_geodataframe_equal(df_read, df, check_dtype=False, check_like=True) def test_to_file_empty(tmpdir): input_empty_df = GeoDataFrame() tempfilename = os.path.join(str(tmpdir), "test.shp") with pytest.raises(ValueError, match="Cannot write empty DataFrame to file."): input_empty_df.to_file(tempfilename) def test_to_file_privacy(tmpdir, df_nybb): tempfilename = os.path.join(str(tmpdir), "test.shp") with pytest.warns(DeprecationWarning): geopandas.io.file.to_file(df_nybb, tempfilename) def test_to_file_schema(tmpdir, df_nybb): """ Ensure that the file is written according to the schema if it is specified """ tempfilename = os.path.join(str(tmpdir), "test.shp") properties = OrderedDict( [ ("Shape_Leng", "float:19.11"), ("BoroName", "str:40"), ("BoroCode", "int:10"), ("Shape_Area", "float:19.11"), ] ) schema = {"geometry": "Polygon", "properties": properties} # Take the first 2 features to speed things up a bit df_nybb.iloc[:2].to_file(tempfilename, schema=schema) with fiona.open(tempfilename) as f: result_schema = f.schema assert result_schema == schema def test_to_file_column_len(tmpdir, df_points): """ Ensure that a warning about truncation is given when a geodataframe with column names longer than 10 characters is saved to shapefile """ tempfilename = os.path.join(str(tmpdir), "test.shp") df = df_points.iloc[:1].copy() df["0123456789A"] = ["the column name is 11 characters"] with pytest.warns( UserWarning, match="Column names longer than 10 characters will be truncated" ): df.to_file(tempfilename, driver="ESRI Shapefile") @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_append_file(tmpdir, df_nybb, df_null, driver, ext): """Test to_file with append mode and from_file""" from fiona import supported_drivers tempfilename = os.path.join(str(tmpdir), "boros" + ext) driver = driver if driver else _detect_driver(tempfilename) if "a" not in supported_drivers[driver]: return None df_nybb.to_file(tempfilename, driver=driver) df_nybb.to_file(tempfilename, mode="a", driver=driver) # Read layer back in df = GeoDataFrame.from_file(tempfilename) assert "geometry" in df assert len(df) == (5 * 2) expected = pd.concat([df_nybb] * 2, ignore_index=True) assert_geodataframe_equal(df, expected, check_less_precise=True) # Write layer with null geometry out to file tempfilename = os.path.join(str(tmpdir), "null_geom" + ext) df_null.to_file(tempfilename, driver=driver) df_null.to_file(tempfilename, mode="a", driver=driver) # Read layer back in df = GeoDataFrame.from_file(tempfilename) assert "geometry" in df assert len(df) == (2 * 2) expected = pd.concat([df_null] * 2, ignore_index=True) assert_geodataframe_equal(df, expected, check_less_precise=True) @pytest.mark.parametrize("driver,ext", driver_ext_pairs) def test_empty_crs(tmpdir, driver, ext): """Test handling of undefined CRS with GPKG driver (GH #1975).""" if ext == ".gpkg": pytest.xfail("GPKG is read with Undefined geographic SRS.") tempfilename = os.path.join(str(tmpdir), "boros" + ext) df = GeoDataFrame( { "a": [1, 2, 3], "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], }, ) df.to_file(tempfilename, driver=driver) result = read_file(tempfilename) if ext == ".geojson": # geojson by default assumes epsg:4326 df.crs = "EPSG:4326" assert_geodataframe_equal(result, df) # ----------------------------------------------------------------------------- # read_file tests # ----------------------------------------------------------------------------- with fiona.open(geopandas.datasets.get_path("nybb")) as f: CRS = f.crs["init"] if "init" in f.crs else f.crs_wkt NYBB_COLUMNS = list(f.meta["schema"]["properties"].keys()) def test_read_file(df_nybb): df = df_nybb.rename(columns=lambda x: x.lower()) validate_boro_df(df) assert df.crs == CRS # get lower case columns, and exclude geometry column from comparison lower_columns = [c.lower() for c in NYBB_COLUMNS] assert (df.columns[:-1] == lower_columns).all() @pytest.mark.web def test_read_file_remote_geojson_url(): url = ( "https://raw.githubusercontent.com/geopandas/geopandas/" "master/geopandas/tests/data/null_geom.geojson" ) gdf = read_file(url) assert isinstance(gdf, geopandas.GeoDataFrame) @pytest.mark.web def test_read_file_remote_zipfile_url(): url = ( "https://raw.githubusercontent.com/geopandas/geopandas/" "master/geopandas/datasets/nybb_16a.zip" ) gdf = read_file(url) assert isinstance(gdf, geopandas.GeoDataFrame) def test_read_file_textio(file_path): file_text_stream = open(file_path) file_stringio = io.StringIO(open(file_path).read()) gdf_text_stream = read_file(file_text_stream) gdf_stringio = read_file(file_stringio) assert isinstance(gdf_text_stream, geopandas.GeoDataFrame) assert isinstance(gdf_stringio, geopandas.GeoDataFrame) def test_read_file_bytesio(file_path): file_binary_stream = open(file_path, "rb") file_bytesio = io.BytesIO(open(file_path, "rb").read()) gdf_binary_stream = read_file(file_binary_stream) gdf_bytesio = read_file(file_bytesio) assert isinstance(gdf_binary_stream, geopandas.GeoDataFrame) assert isinstance(gdf_bytesio, geopandas.GeoDataFrame) def test_read_file_raw_stream(file_path): file_raw_stream = open(file_path, "rb", buffering=0) gdf_raw_stream = read_file(file_raw_stream) assert isinstance(gdf_raw_stream, geopandas.GeoDataFrame) def test_read_file_pathlib(file_path): path_object = pathlib.Path(file_path) gdf_path_object = read_file(path_object) assert isinstance(gdf_path_object, geopandas.GeoDataFrame) def test_read_file_tempfile(): temp = tempfile.TemporaryFile() temp.write( b""" { "type": "Feature", "geometry": { "type": "Point", "coordinates": [0, 0] }, "properties": { "name": "Null Island" } } """ ) temp.seek(0) gdf_tempfile = geopandas.read_file(temp) assert isinstance(gdf_tempfile, geopandas.GeoDataFrame) temp.close() def test_read_binary_file_fsspec(): fsspec = pytest.importorskip("fsspec") # Remove the zip scheme so fsspec doesn't open as a zipped file, # instead we want to read as bytes and let fiona decode it. path = geopandas.datasets.get_path("nybb")[6:] with fsspec.open(path, "rb") as f: gdf = read_file(f) assert isinstance(gdf, geopandas.GeoDataFrame) def test_read_text_file_fsspec(file_path): fsspec = pytest.importorskip("fsspec") with fsspec.open(file_path, "r") as f: gdf = read_file(f) assert isinstance(gdf, geopandas.GeoDataFrame) def test_infer_zipped_file(): # Remove the zip scheme so that the test for a zipped file can # check it and add it back. path = geopandas.datasets.get_path("nybb")[6:] gdf = read_file(path) assert isinstance(gdf, geopandas.GeoDataFrame) # Check that it can successfully add a zip scheme to a path that already has a # scheme gdf = read_file("file+file://" + path) assert isinstance(gdf, geopandas.GeoDataFrame) # Check that it can add a zip scheme for a path that includes a subpath # within the archive. gdf = read_file(path + "!nybb.shp") assert isinstance(gdf, geopandas.GeoDataFrame) def test_allow_legacy_gdal_path(): # Construct a GDAL-style zip path. path = "/vsizip/" + geopandas.datasets.get_path("nybb")[6:] gdf = read_file(path) assert isinstance(gdf, geopandas.GeoDataFrame) def test_read_file_filtered__bbox(df_nybb): nybb_filename = geopandas.datasets.get_path("nybb") bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) filtered_df = read_file(nybb_filename, bbox=bbox) expected = df_nybb[df_nybb["BoroName"].isin(["Bronx", "Queens"])] assert_geodataframe_equal(filtered_df, expected.reset_index(drop=True)) def test_read_file_filtered__bbox__polygon(df_nybb): nybb_filename = geopandas.datasets.get_path("nybb") bbox = box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244 ) filtered_df = read_file(nybb_filename, bbox=bbox) expected = df_nybb[df_nybb["BoroName"].isin(["Bronx", "Queens"])] assert_geodataframe_equal(filtered_df, expected.reset_index(drop=True)) def test_read_file_filtered__rows(df_nybb): nybb_filename = geopandas.datasets.get_path("nybb") filtered_df = read_file(nybb_filename, rows=1) assert_geodataframe_equal(filtered_df, df_nybb.iloc[[0], :]) def test_read_file_filtered__rows_slice(df_nybb): nybb_filename = geopandas.datasets.get_path("nybb") filtered_df = read_file(nybb_filename, rows=slice(1, 3)) assert_geodataframe_equal(filtered_df, df_nybb.iloc[1:3, :].reset_index(drop=True)) @pytest.mark.filterwarnings( "ignore:Layer does not support OLC_FASTFEATURECOUNT:RuntimeWarning" ) # for the slice with -1 def test_read_file_filtered__rows_bbox(df_nybb): nybb_filename = geopandas.datasets.get_path("nybb") bbox = ( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) # combination bbox and rows (rows slice applied after bbox filtering!) filtered_df = read_file(nybb_filename, bbox=bbox, rows=slice(4, None)) assert filtered_df.empty filtered_df = read_file(nybb_filename, bbox=bbox, rows=slice(-1, None)) assert_geodataframe_equal(filtered_df, df_nybb.iloc[4:, :].reset_index(drop=True)) def test_read_file_filtered_rows_invalid(): with pytest.raises(TypeError): read_file(geopandas.datasets.get_path("nybb"), rows="not_a_slice") def test_read_file__ignore_geometry(): pdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres"), ignore_geometry=True ) assert "geometry" not in pdf.columns assert isinstance(pdf, pd.DataFrame) and not isinstance(pdf, geopandas.GeoDataFrame) def test_read_file__ignore_all_fields(): gdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_lowres"), ignore_fields=["pop_est", "continent", "name", "iso_a3", "gdp_md_est"], ) assert gdf.columns.tolist() == ["geometry"] def test_read_file_filtered_with_gdf_boundary(df_nybb): full_df_shape = df_nybb.shape nybb_filename = geopandas.datasets.get_path("nybb") bbox = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=CRS, ) filtered_df = read_file(nybb_filename, bbox=bbox) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) def test_read_file_filtered_with_gdf_boundary__mask(df_nybb): gdf_mask = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres")) gdf = geopandas.read_file( geopandas.datasets.get_path("naturalearth_cities"), mask=gdf_mask[gdf_mask.continent == "Africa"], ) filtered_df_shape = gdf.shape assert filtered_df_shape == (50, 2) def test_read_file_filtered_with_gdf_boundary__mask__polygon(df_nybb): full_df_shape = df_nybb.shape nybb_filename = geopandas.datasets.get_path("nybb") mask = box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244 ) filtered_df = read_file(nybb_filename, mask=mask) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) def test_read_file_filtered_with_gdf_boundary_mismatched_crs(df_nybb): full_df_shape = df_nybb.shape nybb_filename = geopandas.datasets.get_path("nybb") bbox = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=CRS, ) bbox.to_crs(epsg=4326, inplace=True) filtered_df = read_file(nybb_filename, bbox=bbox) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) def test_read_file_filtered_with_gdf_boundary_mismatched_crs__mask(df_nybb): full_df_shape = df_nybb.shape nybb_filename = geopandas.datasets.get_path("nybb") mask = geopandas.GeoDataFrame( geometry=[ box( 1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244, ) ], crs=CRS, ) mask.to_crs(epsg=4326, inplace=True) filtered_df = read_file(nybb_filename, mask=mask.geometry) filtered_df_shape = filtered_df.shape assert full_df_shape != filtered_df_shape assert filtered_df_shape == (2, 5) def test_read_file_empty_shapefile(tmpdir): # create empty shapefile meta = { "crs": {}, "crs_wkt": "", "driver": "ESRI Shapefile", "schema": { "geometry": "Point", "properties": OrderedDict([("A", "int:9"), ("Z", "float:24.15")]), }, } fname = str(tmpdir.join("test_empty.shp")) with fiona_env(): with fiona.open(fname, "w", **meta) as _: # noqa pass empty = read_file(fname) assert isinstance(empty, geopandas.GeoDataFrame) assert all(empty.columns == ["A", "Z", "geometry"]) def test_read_file_privacy(tmpdir, df_nybb): with pytest.warns(DeprecationWarning): geopandas.io.file.read_file(geopandas.datasets.get_path("nybb")) class FileNumber(object): def __init__(self, tmpdir, base, ext): self.tmpdir = str(tmpdir) self.base = base self.ext = ext self.fileno = 0 def __repr__(self): filename = "{0}{1:02d}.{2}".format(self.base, self.fileno, self.ext) return os.path.join(self.tmpdir, filename) def __next__(self): self.fileno += 1 return repr(self) @pytest.mark.parametrize( "driver,ext", [("ESRI Shapefile", "shp"), ("GeoJSON", "geojson")] ) def test_write_index_to_file(tmpdir, df_points, driver, ext): fngen = FileNumber(tmpdir, "check", ext) def do_checks(df, index_is_used): # check combinations of index=None|True|False on GeoDataFrame/GeoSeries other_cols = list(df.columns) other_cols.remove("geometry") if driver == "ESRI Shapefile": # ESRI Shapefile will add FID if no other columns exist driver_col = ["FID"] else: driver_col = [] if index_is_used: index_cols = list(df.index.names) else: index_cols = [None] * len(df.index.names) # replicate pandas' default index names for regular and MultiIndex if index_cols == [None]: index_cols = ["index"] elif len(index_cols) > 1 and not all(index_cols): for level, index_col in enumerate(index_cols): if index_col is None: index_cols[level] = "level_" + str(level) # check GeoDataFrame with default index=None to autodetect tempfilename = next(fngen) df.to_file(tempfilename, driver=driver, index=None) df_check = read_file(tempfilename) if len(other_cols) == 0: expected_cols = driver_col[:] else: expected_cols = [] if index_is_used: expected_cols += index_cols expected_cols += other_cols + ["geometry"] assert list(df_check.columns) == expected_cols # similar check on GeoSeries with index=None tempfilename = next(fngen) df.geometry.to_file(tempfilename, driver=driver, index=None) df_check = read_file(tempfilename) if index_is_used: expected_cols = index_cols + ["geometry"] else: expected_cols = driver_col + ["geometry"] assert list(df_check.columns) == expected_cols # check GeoDataFrame with index=True tempfilename = next(fngen) df.to_file(tempfilename, driver=driver, index=True) df_check = read_file(tempfilename) assert list(df_check.columns) == index_cols + other_cols + ["geometry"] # similar check on GeoSeries with index=True tempfilename = next(fngen) df.geometry.to_file(tempfilename, driver=driver, index=True) df_check = read_file(tempfilename) assert list(df_check.columns) == index_cols + ["geometry"] # check GeoDataFrame with index=False tempfilename = next(fngen) df.to_file(tempfilename, driver=driver, index=False) df_check = read_file(tempfilename) if len(other_cols) == 0: expected_cols = driver_col + ["geometry"] else: expected_cols = other_cols + ["geometry"] assert list(df_check.columns) == expected_cols # similar check on GeoSeries with index=False tempfilename = next(fngen) df.geometry.to_file(tempfilename, driver=driver, index=False) df_check = read_file(tempfilename) assert list(df_check.columns) == driver_col + ["geometry"] return # # Checks where index is not used/saved # # index is a default RangeIndex df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) do_checks(df, index_is_used=False) # index is a RangeIndex, starting from 1 df.index += 1 do_checks(df, index_is_used=False) # index is a Int64Index regular sequence from 1 df_p.index = list(range(1, len(df) + 1)) df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) do_checks(df, index_is_used=False) # index was a default RangeIndex, but delete one row to make an Int64Index df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry).drop(5, axis=0) do_checks(df, index_is_used=False) # no other columns (except geometry) df = GeoDataFrame(geometry=df_p.geometry) do_checks(df, index_is_used=False) # # Checks where index is used/saved # # named index df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index.name = "foo_index" do_checks(df, index_is_used=True) # named index, same as pandas' default name after .reset_index(drop=False) df.index.name = "index" do_checks(df, index_is_used=True) # named MultiIndex df_p = df_points.copy() df_p["value3"] = df_p["value2"] - df_p["value1"] df_p.set_index(["value1", "value2"], inplace=True) df = GeoDataFrame(df_p, geometry=df_p.geometry) do_checks(df, index_is_used=True) # partially unnamed MultiIndex df.index.names = ["first", None] do_checks(df, index_is_used=True) # unnamed MultiIndex df.index.names = [None, None] do_checks(df, index_is_used=True) # unnamed Float64Index df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index = df_p.index.astype(float) / 10 do_checks(df, index_is_used=True) # named Float64Index df.index.name = "centile" do_checks(df, index_is_used=True) # index as string df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index = pd.TimedeltaIndex(range(len(df)), "days") # TODO: TimedeltaIndex is an invalid field type df.index = df.index.astype(str) do_checks(df, index_is_used=True) # unnamed DatetimeIndex df_p = df_points.copy() df = GeoDataFrame(df_p["value1"], geometry=df_p.geometry) df.index = pd.TimedeltaIndex(range(len(df)), "days") + pd.DatetimeIndex( ["1999-12-27"] * len(df) ) if driver == "ESRI Shapefile": # Shapefile driver does not support datetime fields df.index = df.index.astype(str) do_checks(df, index_is_used=True) # named DatetimeIndex df.index.name = "datetime" do_checks(df, index_is_used=True) def test_to_file__undetermined_driver(tmp_path, df_nybb): shpdir = tmp_path / "boros.invalid" df_nybb.to_file(shpdir) assert shpdir.is_dir() assert list(shpdir.glob("*.shp")) @pytest.mark.parametrize( "test_file", [(pathlib.Path("~/test_file.geojson")), "~/test_file.geojson"] ) def test_write_read_file(test_file): gdf = geopandas.GeoDataFrame(geometry=[box(0, 0, 10, 10)], crs=_CRS) gdf.to_file(test_file, driver="GeoJSON") df_json = geopandas.read_file(test_file) assert_geodataframe_equal(gdf, df_json, check_crs=True) os.remove(os.path.expanduser(test_file)) geopandas-0.10.2/geopandas/io/tests/test_file_geom_types_drivers.py000066400000000000000000000161601413247402600255750ustar00rootroot00000000000000import os from shapely.geometry import ( LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, ) import geopandas from geopandas import GeoDataFrame from geopandas.testing import assert_geodataframe_equal import pytest # Credit: Polygons below come from Montreal city Open Data portal # http://donnees.ville.montreal.qc.ca/dataset/unites-evaluation-fonciere city_hall_boundaries = Polygon( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ) vauquelin_place = Polygon( ( (-73.5542465586147, 45.5081555487952), (-73.5540185061397, 45.5084409343852), (-73.5546126200639, 45.5086813829106), (-73.5548825850032, 45.5084033554357), (-73.5542465586147, 45.5081555487952), ) ) city_hall_walls = [ LineString( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), ) ), LineString( ( (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ), ] city_hall_entrance = Point(-73.553785, 45.508722) city_hall_balcony = Point(-73.554138, 45.509080) city_hall_council_chamber = Point(-73.554246, 45.508931) point_3D = Point(-73.553785, 45.508722, 300) # ***************************************** # TEST TOOLING class _ExpectedError: def __init__(self, error_type, error_message_match): self.type = error_type self.match = error_message_match class _ExpectedErrorBuilder: def __init__(self, composite_key): self.composite_key = composite_key def to_raise(self, error_type, error_match): _expected_exceptions[self.composite_key] = _ExpectedError( error_type, error_match ) def _expect_writing(gdf, ogr_driver): return _ExpectedErrorBuilder(_composite_key(gdf, ogr_driver)) def _composite_key(gdf, ogr_driver): return frozenset([id(gdf), ogr_driver]) def _expected_error_on(gdf, ogr_driver): composite_key = _composite_key(gdf, ogr_driver) return _expected_exceptions.get(composite_key, None) # ***************************************** # TEST CASES _geodataframes_to_write = [] _expected_exceptions = {} _CRS = "epsg:4326" # ------------------ # gdf with Points gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[city_hall_entrance, city_hall_balcony] ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with MultiPoints gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[ MultiPoint([city_hall_balcony, city_hall_council_chamber]), MultiPoint([city_hall_entrance, city_hall_balcony, city_hall_council_chamber]), ], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with Points and MultiPoints gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony], ) _geodataframes_to_write.append(gdf) # 'ESRI Shapefile' driver supports writing LineString/MultiLinestring and # Polygon/MultiPolygon but does not mention Point/MultiPoint # see https://www.gdal.org/drv_shapefile.html _expect_writing(gdf, "ESRI Shapefile").to_raise(RuntimeError, "Failed to write record") # ------------------ # gdf with LineStrings gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=city_hall_walls) _geodataframes_to_write.append(gdf) # ------------------ # gdf with MultiLineStrings gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[MultiLineString(city_hall_walls), MultiLineString(city_hall_walls)], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with LineStrings and MultiLineStrings gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[MultiLineString(city_hall_walls), city_hall_walls[0]], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with Polygons gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[city_hall_boundaries, vauquelin_place] ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with MultiPolygon gdf = GeoDataFrame( {"a": [1]}, crs=_CRS, geometry=[MultiPolygon((city_hall_boundaries, vauquelin_place))], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with Polygon and MultiPolygon gdf = GeoDataFrame( {"a": [1, 2]}, crs=_CRS, geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, ], ) _geodataframes_to_write.append(gdf) # ------------------ # gdf with null geometry and Point gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=[None, city_hall_entrance]) _geodataframes_to_write.append(gdf) # ------------------ # gdf with null geometry and 3D Point gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=[None, point_3D]) _geodataframes_to_write.append(gdf) # ------------------ # gdf with null geometries only gdf = GeoDataFrame({"a": [1, 2]}, crs=_CRS, geometry=[None, None]) _geodataframes_to_write.append(gdf) # ------------------ # gdf with all shape types mixed together gdf = GeoDataFrame( {"a": [1, 2, 3, 4, 5, 6]}, crs=_CRS, geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_entrance, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, ], ) _geodataframes_to_write.append(gdf) # Not supported by 'ESRI Shapefile' driver _expect_writing(gdf, "ESRI Shapefile").to_raise(RuntimeError, "Failed to write record") # ------------------ # gdf with all 2D shape types and 3D Point mixed together gdf = GeoDataFrame( {"a": [1, 2, 3, 4, 5, 6, 7]}, crs=_CRS, geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_entrance, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, point_3D, ], ) _geodataframes_to_write.append(gdf) # Not supported by 'ESRI Shapefile' driver _expect_writing(gdf, "ESRI Shapefile").to_raise(RuntimeError, "Failed to write record") @pytest.fixture(params=_geodataframes_to_write) def geodataframe(request): return request.param @pytest.fixture(params=["GeoJSON", "ESRI Shapefile", "GPKG"]) def ogr_driver(request): return request.param def test_to_file_roundtrip(tmpdir, geodataframe, ogr_driver): output_file = os.path.join(str(tmpdir), "output_file") expected_error = _expected_error_on(geodataframe, ogr_driver) if expected_error: with pytest.raises(RuntimeError, match="Failed to write record"): geodataframe.to_file(output_file, driver=ogr_driver) else: geodataframe.to_file(output_file, driver=ogr_driver) reloaded = geopandas.read_file(output_file) assert_geodataframe_equal(geodataframe, reloaded, check_column_type="equiv") geopandas-0.10.2/geopandas/io/tests/test_infer_schema.py000066400000000000000000000175171413247402600233170ustar00rootroot00000000000000from collections import OrderedDict from shapely.geometry import ( LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, ) import pandas as pd import numpy as np from geopandas import GeoDataFrame from geopandas.io.file import infer_schema # Credit: Polygons below come from Montreal city Open Data portal # http://donnees.ville.montreal.qc.ca/dataset/unites-evaluation-fonciere city_hall_boundaries = Polygon( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ) vauquelin_place = Polygon( ( (-73.5542465586147, 45.5081555487952), (-73.5540185061397, 45.5084409343852), (-73.5546126200639, 45.5086813829106), (-73.5548825850032, 45.5084033554357), (-73.5542465586147, 45.5081555487952), ) ) city_hall_walls = [ LineString( ( (-73.5541107525234, 45.5091983609661), (-73.5546126200639, 45.5086813829106), (-73.5540185061397, 45.5084409343852), ) ), LineString( ( (-73.5539986525799, 45.5084323044531), (-73.5535801792994, 45.5089539203786), (-73.5541107525234, 45.5091983609661), ) ), ] city_hall_entrance = Point(-73.553785, 45.508722) city_hall_balcony = Point(-73.554138, 45.509080) city_hall_council_chamber = Point(-73.554246, 45.508931) point_3D = Point(-73.553785, 45.508722, 300) linestring_3D = LineString( ( (-73.5541107525234, 45.5091983609661, 300), (-73.5546126200639, 45.5086813829106, 300), (-73.5540185061397, 45.5084409343852, 300), ) ) polygon_3D = Polygon( ( (-73.5541107525234, 45.5091983609661, 300), (-73.5535801792994, 45.5089539203786, 300), (-73.5541107525234, 45.5091983609661, 300), ) ) def test_infer_schema_only_points(): df = GeoDataFrame(geometry=[city_hall_entrance, city_hall_balcony]) assert infer_schema(df) == {"geometry": "Point", "properties": OrderedDict()} def test_infer_schema_points_and_multipoints(): df = GeoDataFrame( geometry=[ MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, ] ) assert infer_schema(df) == { "geometry": ["MultiPoint", "Point"], "properties": OrderedDict(), } def test_infer_schema_only_multipoints(): df = GeoDataFrame( geometry=[ MultiPoint( [city_hall_entrance, city_hall_balcony, city_hall_council_chamber] ) ] ) assert infer_schema(df) == {"geometry": "MultiPoint", "properties": OrderedDict()} def test_infer_schema_only_linestrings(): df = GeoDataFrame(geometry=city_hall_walls) assert infer_schema(df) == {"geometry": "LineString", "properties": OrderedDict()} def test_infer_schema_linestrings_and_multilinestrings(): df = GeoDataFrame(geometry=[MultiLineString(city_hall_walls), city_hall_walls[0]]) assert infer_schema(df) == { "geometry": ["MultiLineString", "LineString"], "properties": OrderedDict(), } def test_infer_schema_only_multilinestrings(): df = GeoDataFrame(geometry=[MultiLineString(city_hall_walls)]) assert infer_schema(df) == { "geometry": "MultiLineString", "properties": OrderedDict(), } def test_infer_schema_only_polygons(): df = GeoDataFrame(geometry=[city_hall_boundaries, vauquelin_place]) assert infer_schema(df) == {"geometry": "Polygon", "properties": OrderedDict()} def test_infer_schema_polygons_and_multipolygons(): df = GeoDataFrame( geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, ] ) assert infer_schema(df) == { "geometry": ["MultiPolygon", "Polygon"], "properties": OrderedDict(), } def test_infer_schema_only_multipolygons(): df = GeoDataFrame(geometry=[MultiPolygon((city_hall_boundaries, vauquelin_place))]) assert infer_schema(df) == {"geometry": "MultiPolygon", "properties": OrderedDict()} def test_infer_schema_multiple_shape_types(): df = GeoDataFrame( geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, ] ) assert infer_schema(df) == { "geometry": [ "MultiPolygon", "Polygon", "MultiLineString", "LineString", "MultiPoint", "Point", ], "properties": OrderedDict(), } def test_infer_schema_mixed_3D_shape_type(): df = GeoDataFrame( geometry=[ MultiPolygon((city_hall_boundaries, vauquelin_place)), city_hall_boundaries, MultiLineString(city_hall_walls), city_hall_walls[0], MultiPoint([city_hall_entrance, city_hall_balcony]), city_hall_balcony, point_3D, ] ) assert infer_schema(df) == { "geometry": [ "3D Point", "MultiPolygon", "Polygon", "MultiLineString", "LineString", "MultiPoint", "Point", ], "properties": OrderedDict(), } def test_infer_schema_mixed_3D_Point(): df = GeoDataFrame(geometry=[city_hall_balcony, point_3D]) assert infer_schema(df) == { "geometry": ["3D Point", "Point"], "properties": OrderedDict(), } def test_infer_schema_only_3D_Points(): df = GeoDataFrame(geometry=[point_3D, point_3D]) assert infer_schema(df) == {"geometry": "3D Point", "properties": OrderedDict()} def test_infer_schema_mixed_3D_linestring(): df = GeoDataFrame(geometry=[city_hall_walls[0], linestring_3D]) assert infer_schema(df) == { "geometry": ["3D LineString", "LineString"], "properties": OrderedDict(), } def test_infer_schema_only_3D_linestrings(): df = GeoDataFrame(geometry=[linestring_3D, linestring_3D]) assert infer_schema(df) == { "geometry": "3D LineString", "properties": OrderedDict(), } def test_infer_schema_mixed_3D_Polygon(): df = GeoDataFrame(geometry=[city_hall_boundaries, polygon_3D]) assert infer_schema(df) == { "geometry": ["3D Polygon", "Polygon"], "properties": OrderedDict(), } def test_infer_schema_only_3D_Polygons(): df = GeoDataFrame(geometry=[polygon_3D, polygon_3D]) assert infer_schema(df) == {"geometry": "3D Polygon", "properties": OrderedDict()} def test_infer_schema_null_geometry_and_2D_point(): df = GeoDataFrame(geometry=[None, city_hall_entrance]) # None geometry type is then omitted assert infer_schema(df) == {"geometry": "Point", "properties": OrderedDict()} def test_infer_schema_null_geometry_and_3D_point(): df = GeoDataFrame(geometry=[None, point_3D]) # None geometry type is then omitted assert infer_schema(df) == {"geometry": "3D Point", "properties": OrderedDict()} def test_infer_schema_null_geometry_all(): df = GeoDataFrame(geometry=[None, None]) # None geometry type in then replaced by 'Unknown' # (default geometry type supported by Fiona) assert infer_schema(df) == {"geometry": "Unknown", "properties": OrderedDict()} def test_infer_schema_int64(): int64col = pd.array([1, np.nan], dtype=pd.Int64Dtype()) df = GeoDataFrame(geometry=[city_hall_entrance, city_hall_balcony]) df["int64"] = int64col assert infer_schema(df) == { "geometry": "Point", "properties": OrderedDict([("int64", "int")]), } geopandas-0.10.2/geopandas/io/tests/test_pickle.py000066400000000000000000000043751413247402600221410ustar00rootroot00000000000000""" See generate_legacy_storage_files.py for the creation of the legacy files. """ from distutils.version import LooseVersion import glob import os import pathlib import pandas as pd import pyproj import pytest from geopandas.testing import assert_geodataframe_equal from geopandas import _compat as compat import geopandas from shapely.geometry import Point DATA_PATH = pathlib.Path(os.path.dirname(__file__)) / "data" @pytest.fixture(scope="module") def current_pickle_data(): # our current version pickle data from .generate_legacy_storage_files import create_pickle_data return create_pickle_data() files = glob.glob(str(DATA_PATH / "pickle" / "*.pickle")) @pytest.fixture(params=files, ids=[p.split("/")[-1] for p in files]) def legacy_pickle(request): return request.param @pytest.fixture def with_use_pygeos_false(): orig = geopandas.options.use_pygeos geopandas.options.use_pygeos = not orig yield geopandas.options.use_pygeos = orig @pytest.mark.skipif( compat.USE_PYGEOS or (str(pyproj.__version__) < LooseVersion("2.4")), reason=( "pygeos-based unpickling currently only works for pygeos-written files; " "old pyproj versions can't read pickles from newer pyproj versions" ), ) def test_legacy_pickles(current_pickle_data, legacy_pickle): result = pd.read_pickle(legacy_pickle) for name, value in result.items(): expected = current_pickle_data[name] assert_geodataframe_equal(value, expected) def test_round_trip_current(tmpdir, current_pickle_data): data = current_pickle_data for name, value in data.items(): path = str(tmpdir / "{}.pickle".format(name)) value.to_pickle(path) result = pd.read_pickle(path) assert_geodataframe_equal(result, value) assert isinstance(result.has_sindex, bool) @pytest.mark.skipif(not compat.HAS_PYGEOS, reason="requires pygeos to test #1745") def test_pygeos_switch(tmpdir, with_use_pygeos_false): gdf_crs = geopandas.GeoDataFrame( {"a": [0.1, 0.2, 0.3], "geometry": [Point(1, 1), Point(2, 2), Point(3, 3)]}, crs="EPSG:4326", ) path = str(tmpdir / "gdf_crs.pickle") gdf_crs.to_pickle(path) result = pd.read_pickle(path) assert_geodataframe_equal(result, gdf_crs) geopandas-0.10.2/geopandas/io/tests/test_sql.py000066400000000000000000000535341413247402600214720ustar00rootroot00000000000000""" Tests here include reading/writing to different types of spatial databases. The spatial database tests may not work without additional system configuration. postGIS tests require a test database to have been setup; see geopandas.tests.util for more information. """ import os import pandas as pd import geopandas from geopandas import GeoDataFrame, read_file, read_postgis from geopandas.io.sql import _get_conn as get_conn, _write_postgis as write_postgis from geopandas.tests.util import create_postgis, create_spatialite, validate_boro_df import pytest @pytest.fixture def df_nybb(): nybb_path = geopandas.datasets.get_path("nybb") df = read_file(nybb_path) return df @pytest.fixture() def connection_postgis(): """ Initiaties a connection to a postGIS database that must already exist. See create_postgis for more information. """ psycopg2 = pytest.importorskip("psycopg2") from psycopg2 import OperationalError dbname = "test_geopandas" user = os.environ.get("PGUSER") password = os.environ.get("PGPASSWORD") host = os.environ.get("PGHOST") port = os.environ.get("PGPORT") try: con = psycopg2.connect( dbname=dbname, user=user, password=password, host=host, port=port ) except OperationalError: pytest.skip("Cannot connect with postgresql database") yield con con.close() @pytest.fixture() def engine_postgis(): """ Initiaties a connection engine to a postGIS database that must already exist. """ sqlalchemy = pytest.importorskip("sqlalchemy") from sqlalchemy.engine.url import URL user = os.environ.get("PGUSER") password = os.environ.get("PGPASSWORD") host = os.environ.get("PGHOST") port = os.environ.get("PGPORT") dbname = "test_geopandas" try: con = sqlalchemy.create_engine( URL.create( drivername="postgresql+psycopg2", username=user, database=dbname, password=password, host=host, port=port, ) ) con.begin() except Exception: pytest.skip("Cannot connect with postgresql database") yield con con.dispose() @pytest.fixture() def connection_spatialite(): """ Return a memory-based SQLite3 connection with SpatiaLite enabled & initialized. `The sqlite3 module must be built with loadable extension support `_ and `SpatiaLite `_ must be available on the system as a SQLite module. Packages available on Anaconda meet requirements. Exceptions ---------- ``AttributeError`` on missing support for loadable SQLite extensions ``sqlite3.OperationalError`` on missing SpatiaLite """ sqlite3 = pytest.importorskip("sqlite3") try: with sqlite3.connect(":memory:") as con: con.enable_load_extension(True) con.load_extension("mod_spatialite") con.execute("SELECT InitSpatialMetaData(TRUE)") except Exception: con.close() pytest.skip("Cannot setup spatialite database") yield con con.close() def drop_table_if_exists(conn_or_engine, table): sqlalchemy = pytest.importorskip("sqlalchemy") if sqlalchemy.inspect(conn_or_engine).has_table(table): metadata = sqlalchemy.MetaData(conn_or_engine) metadata.reflect() table = metadata.tables.get(table) if table is not None: table.drop(checkfirst=True) @pytest.fixture def df_mixed_single_and_multi(): from shapely.geometry import Point, LineString, MultiLineString df = geopandas.GeoDataFrame( { "geometry": [ LineString([(0, 0), (1, 1)]), MultiLineString([[(0, 0), (1, 1)], [(2, 2), (3, 3)]]), Point(0, 1), ] }, crs="epsg:4326", ) return df @pytest.fixture def df_geom_collection(): from shapely.geometry import Point, LineString, Polygon, GeometryCollection df = geopandas.GeoDataFrame( { "geometry": [ GeometryCollection( [ Polygon([(0, 0), (1, 1), (0, 1)]), LineString([(0, 0), (1, 1)]), Point(0, 0), ] ) ] }, crs="epsg:4326", ) return df @pytest.fixture def df_linear_ring(): from shapely.geometry import LinearRing df = geopandas.GeoDataFrame( {"geometry": [LinearRing(((0, 0), (0, 1), (1, 1), (1, 0)))]}, crs="epsg:4326" ) return df @pytest.fixture def df_3D_geoms(): from shapely.geometry import Point, LineString, Polygon df = geopandas.GeoDataFrame( { "geometry": [ LineString([(0, 0, 0), (1, 1, 1)]), Polygon([(0, 0, 0), (1, 1, 1), (0, 1, 1)]), Point(0, 1, 2), ] }, crs="epsg:4326", ) return df class TestIO: def test_get_conn(self, engine_postgis): Connection = pytest.importorskip("sqlalchemy.engine.base").Connection engine = engine_postgis with get_conn(engine) as output: assert isinstance(output, Connection) with engine.connect() as conn: with get_conn(conn) as output: assert isinstance(output, Connection) with pytest.raises(ValueError): with get_conn(object()): pass def test_read_postgis_default(self, connection_postgis, df_nybb): con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con) validate_boro_df(df) # no crs defined on the created geodatabase, and none specified # by user; should not be set to 0, as from get_srid failure assert df.crs is None def test_read_postgis_custom_geom_col(self, connection_postgis, df_nybb): con = connection_postgis geom_col = "the_geom" create_postgis(con, df_nybb, geom_col=geom_col) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con, geom_col=geom_col) validate_boro_df(df) def test_read_postgis_select_geom_as(self, connection_postgis, df_nybb): """Tests that a SELECT {geom} AS {some_other_geom} works.""" con = connection_postgis orig_geom = "geom" out_geom = "the_geom" create_postgis(con, df_nybb, geom_col=orig_geom) sql = """SELECT borocode, boroname, shape_leng, shape_area, {} as {} FROM nybb;""".format( orig_geom, out_geom ) df = read_postgis(sql, con, geom_col=out_geom) validate_boro_df(df) def test_read_postgis_get_srid(self, connection_postgis, df_nybb): """Tests that an SRID can be read from a geodatabase (GH #451).""" con = connection_postgis crs = "epsg:4269" df_reproj = df_nybb.to_crs(crs) create_postgis(con, df_reproj, srid=4269) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con) validate_boro_df(df) assert df.crs == crs def test_read_postgis_override_srid(self, connection_postgis, df_nybb): """Tests that a user specified CRS overrides the geodatabase SRID.""" con = connection_postgis orig_crs = df_nybb.crs create_postgis(con, df_nybb, srid=4269) sql = "SELECT * FROM nybb;" df = read_postgis(sql, con, crs=orig_crs) validate_boro_df(df) assert df.crs == orig_crs def test_from_postgis_default(self, connection_postgis, df_nybb): con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" df = GeoDataFrame.from_postgis(sql, con) validate_boro_df(df, case_sensitive=False) def test_from_postgis_custom_geom_col(self, connection_postgis, df_nybb): con = connection_postgis geom_col = "the_geom" create_postgis(con, df_nybb, geom_col=geom_col) sql = "SELECT * FROM nybb;" df = GeoDataFrame.from_postgis(sql, con, geom_col=geom_col) validate_boro_df(df, case_sensitive=False) def test_read_postgis_null_geom(self, connection_spatialite, df_nybb): """Tests that geometry with NULL is accepted.""" con = connection_spatialite geom_col = df_nybb.geometry.name df_nybb.geometry.iat[0] = None create_spatialite(con, df_nybb) sql = ( "SELECT ogc_fid, borocode, boroname, shape_leng, shape_area, " 'AsEWKB("{0}") AS "{0}" FROM nybb'.format(geom_col) ) df = read_postgis(sql, con, geom_col=geom_col) validate_boro_df(df) def test_read_postgis_binary(self, connection_spatialite, df_nybb): """Tests that geometry read as binary is accepted.""" con = connection_spatialite geom_col = df_nybb.geometry.name create_spatialite(con, df_nybb) sql = ( "SELECT ogc_fid, borocode, boroname, shape_leng, shape_area, " 'ST_AsBinary("{0}") AS "{0}" FROM nybb'.format(geom_col) ) df = read_postgis(sql, con, geom_col=geom_col) validate_boro_df(df) def test_read_postgis_chunksize(self, connection_postgis, df_nybb): """Test chunksize argument""" chunksize = 2 con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" df = pd.concat(read_postgis(sql, con, chunksize=chunksize)) validate_boro_df(df) # no crs defined on the created geodatabase, and none specified # by user; should not be set to 0, as from get_srid failure assert df.crs is None def test_read_postgis_privacy(self, connection_postgis, df_nybb): con = connection_postgis create_postgis(con, df_nybb) sql = "SELECT * FROM nybb;" with pytest.warns(DeprecationWarning): geopandas.io.sql.read_postgis(sql, con) def test_write_postgis_default(self, engine_postgis, df_nybb): """Tests that GeoDataFrame can be written to PostGIS with defaults.""" engine = engine_postgis table = "nybb" # If table exists, delete it before trying to write with defaults drop_table_if_exists(engine, table) # Write to db write_postgis(df_nybb, con=engine, name=table, if_exists="fail") # Validate sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) def test_write_postgis_uppercase_tablename(self, engine_postgis, df_nybb): """Tests writing GeoDataFrame to PostGIS with uppercase tablename.""" engine = engine_postgis table = "aTestTable" # If table exists, delete it before trying to write with defaults drop_table_if_exists(engine, table) # Write to db write_postgis(df_nybb, con=engine, name=table, if_exists="fail") # Validate sql = 'SELECT * FROM "{table}";'.format(table=table) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) def test_write_postgis_sqlalchemy_connection(self, engine_postgis, df_nybb): """Tests that GeoDataFrame can be written to PostGIS with defaults.""" with engine_postgis.begin() as con: table = "nybb_con" # If table exists, delete it before trying to write with defaults drop_table_if_exists(con, table) # Write to db write_postgis(df_nybb, con=con, name=table, if_exists="fail") # Validate sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, con, geom_col="geometry") validate_boro_df(df) def test_write_postgis_fail_when_table_exists(self, engine_postgis, df_nybb): """ Tests that uploading the same table raises error when: if_replace='fail'. """ engine = engine_postgis table = "nybb" # Ensure table exists write_postgis(df_nybb, con=engine, name=table, if_exists="replace") try: write_postgis(df_nybb, con=engine, name=table, if_exists="fail") except ValueError as e: if "already exists" in str(e): pass else: raise e def test_write_postgis_replace_when_table_exists(self, engine_postgis, df_nybb): """ Tests that replacing a table is possible when: if_replace='replace'. """ engine = engine_postgis table = "nybb" # Ensure table exists write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Overwrite write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Validate sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) def test_write_postgis_append_when_table_exists(self, engine_postgis, df_nybb): """ Tests that appending to existing table produces correct results when: if_replace='append'. """ engine = engine_postgis table = "nybb" orig_rows, orig_cols = df_nybb.shape write_postgis(df_nybb, con=engine, name=table, if_exists="replace") write_postgis(df_nybb, con=engine, name=table, if_exists="append") # Validate sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") new_rows, new_cols = df.shape # There should be twice as many rows in the new table assert new_rows == orig_rows * 2, ( "There should be {target} rows," "found: {current}".format(target=orig_rows * 2, current=new_rows), ) # Number of columns should stay the same assert new_cols == orig_cols, ( "There should be {target} columns," "found: {current}".format(target=orig_cols, current=new_cols), ) def test_write_postgis_without_crs(self, engine_postgis, df_nybb): """ Tests that GeoDataFrame can be written to PostGIS without CRS information. """ engine = engine_postgis table = "nybb" # Write to db df_nybb = df_nybb df_nybb.crs = None write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Validate that srid is -1 target_srid = engine.execute( "SELECT Find_SRID('{schema}', '{table}', '{geom_col}');".format( schema="public", table=table, geom_col="geometry" ) ).fetchone()[0] assert target_srid == 0, "SRID should be 0, found %s" % target_srid def test_write_postgis_geometry_collection( self, engine_postgis, df_geom_collection ): """ Tests that writing a mix of different geometry types is possible. """ engine = engine_postgis table = "geomtype_tests" write_postgis(df_geom_collection, con=engine, name=table, if_exists="replace") # Validate geometry type sql = "SELECT DISTINCT(GeometryType(geometry)) FROM {table} ORDER BY 1;".format( table=table ) geom_type = engine.execute(sql).fetchone()[0] sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") assert geom_type.upper() == "GEOMETRYCOLLECTION" assert df.geom_type.unique()[0] == "GeometryCollection" def test_write_postgis_mixed_geometry_types( self, engine_postgis, df_mixed_single_and_multi ): """ Tests that writing a mix of single and MultiGeometries is possible. """ engine = engine_postgis table = "geomtype_tests" write_postgis( df_mixed_single_and_multi, con=engine, name=table, if_exists="replace" ) # Validate geometry type sql = "SELECT DISTINCT GeometryType(geometry) FROM {table} ORDER BY 1;".format( table=table ) res = engine.execute(sql).fetchall() assert res[0][0].upper() == "LINESTRING" assert res[1][0].upper() == "MULTILINESTRING" assert res[2][0].upper() == "POINT" def test_write_postgis_linear_ring(self, engine_postgis, df_linear_ring): """ Tests that writing a LinearRing. """ engine = engine_postgis table = "geomtype_tests" write_postgis(df_linear_ring, con=engine, name=table, if_exists="replace") # Validate geometry type sql = "SELECT DISTINCT(GeometryType(geometry)) FROM {table} ORDER BY 1;".format( table=table ) geom_type = engine.execute(sql).fetchone()[0] assert geom_type.upper() == "LINESTRING" def test_write_postgis_in_chunks(self, engine_postgis, df_mixed_single_and_multi): """ Tests writing a LinearRing works. """ engine = engine_postgis table = "geomtype_tests" write_postgis( df_mixed_single_and_multi, con=engine, name=table, if_exists="replace", chunksize=1, ) # Validate row count sql = "SELECT COUNT(geometry) FROM {table};".format(table=table) row_cnt = engine.execute(sql).fetchone()[0] assert row_cnt == 3 # Validate geometry type sql = "SELECT DISTINCT GeometryType(geometry) FROM {table} ORDER BY 1;".format( table=table ) res = engine.execute(sql).fetchall() assert res[0][0].upper() == "LINESTRING" assert res[1][0].upper() == "MULTILINESTRING" assert res[2][0].upper() == "POINT" def test_write_postgis_to_different_schema(self, engine_postgis, df_nybb): """ Tests writing data to alternative schema. """ engine = engine_postgis table = "nybb" schema_to_use = "test" sql = "CREATE SCHEMA IF NOT EXISTS {schema};".format(schema=schema_to_use) engine.execute(sql) write_postgis( df_nybb, con=engine, name=table, if_exists="replace", schema=schema_to_use ) # Validate sql = "SELECT * FROM {schema}.{table};".format( schema=schema_to_use, table=table ) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) def test_write_postgis_to_different_schema_when_table_exists( self, engine_postgis, df_nybb ): """ Tests writing data to alternative schema. """ engine = engine_postgis table = "nybb" schema_to_use = "test" sql = "CREATE SCHEMA IF NOT EXISTS {schema};".format(schema=schema_to_use) engine.execute(sql) try: write_postgis( df_nybb, con=engine, name=table, if_exists="fail", schema=schema_to_use ) # Validate sql = "SELECT * FROM {schema}.{table};".format( schema=schema_to_use, table=table ) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) # Should raise a ValueError when table exists except ValueError: pass # Try with replace flag on write_postgis( df_nybb, con=engine, name=table, if_exists="replace", schema=schema_to_use ) # Validate sql = "SELECT * FROM {schema}.{table};".format( schema=schema_to_use, table=table ) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) def test_write_postgis_3D_geometries(self, engine_postgis, df_3D_geoms): """ Tests writing a geometries with 3 dimensions works. """ engine = engine_postgis table = "geomtype_tests" write_postgis(df_3D_geoms, con=engine, name=table, if_exists="replace") # Check that all geometries have 3 dimensions sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") assert list(df.geometry.has_z) == [True, True, True] def test_row_order(self, engine_postgis, df_nybb): """ Tests that the row order in db table follows the order of the original frame. """ engine = engine_postgis table = "row_order_test" correct_order = df_nybb["BoroCode"].tolist() write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Check that the row order matches sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") assert df["BoroCode"].tolist() == correct_order def test_append_before_table_exists(self, engine_postgis, df_nybb): """ Tests that insert works with if_exists='append' when table does not exist yet. """ engine = engine_postgis table = "nybb" # If table exists, delete it before trying to write with defaults drop_table_if_exists(engine, table) write_postgis(df_nybb, con=engine, name=table, if_exists="append") # Check that the row order matches sql = "SELECT * FROM {table};".format(table=table) df = read_postgis(sql, engine, geom_col="geometry") validate_boro_df(df) def test_append_with_different_crs(self, engine_postgis, df_nybb): """ Tests that the warning is raised if table CRS differs from frame. """ engine = engine_postgis table = "nybb" write_postgis(df_nybb, con=engine, name=table, if_exists="replace") # Reproject df_nybb2 = df_nybb.to_crs(epsg=4326) # Should raise error when appending with pytest.raises(ValueError, match="CRS of the target table"): write_postgis(df_nybb2, con=engine, name=table, if_exists="append") geopandas-0.10.2/geopandas/plotting.py000066400000000000000000001021521413247402600177120ustar00rootroot00000000000000import warnings import numpy as np import pandas as pd from pandas.plotting import PlotAccessor import geopandas from distutils.version import LooseVersion from ._decorator import doc def deprecated(new): """Helper to provide deprecation warning.""" def old(*args, **kwargs): warnings.warn( "{} is intended for internal ".format(new.__name__[1:]) + "use only, and will be deprecated.", DeprecationWarning, stacklevel=2, ) new(*args, **kwargs) return old def _flatten_multi_geoms(geoms, prefix="Multi"): """ Returns Series like geoms and index, except that any Multi geometries are split into their components and indices are repeated for all component in the same Multi geometry. Maintains 1:1 matching of geometry to value. Prefix specifies type of geometry to be flatten. 'Multi' for MultiPoint and similar, "Geom" for GeometryCollection. Returns ------- components : list of geometry component_index : index array indices are repeated for all components in the same Multi geometry """ components, component_index = [], [] if not geoms.geom_type.str.startswith(prefix).any(): return geoms, np.arange(len(geoms)) for ix, geom in enumerate(geoms): if geom is not None and geom.type.startswith(prefix) and not geom.is_empty: for poly in geom.geoms: components.append(poly) component_index.append(ix) else: components.append(geom) component_index.append(ix) return components, np.array(component_index) def _expand_kwargs(kwargs, multiindex): """ Most arguments to the plot functions must be a (single) value, or a sequence of values. This function checks each key-value pair in 'kwargs' and expands it (in place) to the correct length/formats with help of 'multiindex', unless the value appears to already be a valid (single) value for the key. """ import matplotlib from matplotlib.colors import is_color_like from typing import Iterable mpl = matplotlib.__version__ if mpl >= LooseVersion("3.4") or (mpl > LooseVersion("3.3.2") and "+" in mpl): # alpha is supported as array argument with matplotlib 3.4+ scalar_kwargs = ["marker", "path_effects"] else: scalar_kwargs = ["marker", "alpha", "path_effects"] for att, value in kwargs.items(): if "color" in att: # color(s), edgecolor(s), facecolor(s) if is_color_like(value): continue elif "linestyle" in att: # linestyle(s) # A single linestyle can be 2-tuple of a number and an iterable. if ( isinstance(value, tuple) and len(value) == 2 and isinstance(value[1], Iterable) ): continue elif att in scalar_kwargs: # For these attributes, only a single value is allowed, so never expand. continue if pd.api.types.is_list_like(value): kwargs[att] = np.take(value, multiindex, axis=0) def _PolygonPatch(polygon, **kwargs): """Constructs a matplotlib patch from a Polygon geometry The `kwargs` are those supported by the matplotlib.patches.PathPatch class constructor. Returns an instance of matplotlib.patches.PathPatch. Example (using Shapely Point and a matplotlib axes):: b = shapely.geometry.Point(0, 0).buffer(1.0) patch = _PolygonPatch(b, fc='blue', ec='blue', alpha=0.5) ax.add_patch(patch) GeoPandas originally relied on the descartes package by Sean Gillies (BSD license, https://pypi.org/project/descartes) for PolygonPatch, but this dependency was removed in favor of the below matplotlib code. """ from matplotlib.patches import PathPatch from matplotlib.path import Path path = Path.make_compound_path( Path(np.asarray(polygon.exterior.coords)[:, :2]), *[Path(np.asarray(ring.coords)[:, :2]) for ring in polygon.interiors], ) return PathPatch(path, **kwargs) def _plot_polygon_collection( ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, **kwargs ): """ Plots a collection of Polygon and MultiPolygon geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : a sequence of `N` Polygons and/or MultiPolygons (can be mixed) values : a sequence of `N` values, optional Values will be mapped to colors using vmin/vmax/cmap. They should have 1:1 correspondence with the geometries (not their components). Otherwise follows `color` / `facecolor` kwargs. edgecolor : single color or sequence of `N` colors Color for the edge of the polygons facecolor : single color or sequence of `N` colors Color to fill the polygons. Cannot be used together with `values`. color : single color or sequence of `N` colors Sets both `edgecolor` and `facecolor` **kwargs Additional keyword arguments passed to the collection Returns ------- collection : matplotlib.collections.Collection that was plotted """ from matplotlib.collections import PatchCollection geoms, multiindex = _flatten_multi_geoms(geoms) if values is not None: values = np.take(values, multiindex, axis=0) # PatchCollection does not accept some kwargs. kwargs = { att: value for att, value in kwargs.items() if att not in ["markersize", "marker"] } # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color _expand_kwargs(kwargs, multiindex) collection = PatchCollection( [_PolygonPatch(poly) for poly in geoms if not poly.is_empty], **kwargs ) if values is not None: collection.set_array(np.asarray(values)) collection.set_cmap(cmap) if "norm" not in kwargs: collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=True) ax.autoscale_view() return collection plot_polygon_collection = deprecated(_plot_polygon_collection) def _plot_linestring_collection( ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, **kwargs ): """ Plots a collection of LineString and MultiLineString geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed) values : a sequence of `N` values, optional Values will be mapped to colors using vmin/vmax/cmap. They should have 1:1 correspondence with the geometries (not their components). color : single color or sequence of `N` colors Cannot be used together with `values`. Returns ------- collection : matplotlib.collections.Collection that was plotted """ from matplotlib.collections import LineCollection geoms, multiindex = _flatten_multi_geoms(geoms) if values is not None: values = np.take(values, multiindex, axis=0) # LineCollection does not accept some kwargs. kwargs = { att: value for att, value in kwargs.items() if att not in ["markersize", "marker"] } # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color _expand_kwargs(kwargs, multiindex) segments = [np.array(linestring.coords)[:, :2] for linestring in geoms] collection = LineCollection(segments, **kwargs) if values is not None: collection.set_array(np.asarray(values)) collection.set_cmap(cmap) if "norm" not in kwargs: collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=True) ax.autoscale_view() return collection plot_linestring_collection = deprecated(_plot_linestring_collection) def _plot_point_collection( ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, marker="o", markersize=None, **kwargs, ): """ Plots a collection of Point and MultiPoint geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : sequence of `N` Points or MultiPoints values : a sequence of `N` values, optional Values mapped to colors using vmin, vmax, and cmap. Cannot be specified together with `color`. markersize : scalar or array-like, optional Size of the markers. Note that under the hood ``scatter`` is used, so the specified value will be proportional to the area of the marker (size in points^2). Returns ------- collection : matplotlib.collections.Collection that was plotted """ if values is not None and color is not None: raise ValueError("Can only specify one of 'values' and 'color' kwargs") geoms, multiindex = _flatten_multi_geoms(geoms) # values are expanded below as kwargs["c"] x = [p.x if not p.is_empty else None for p in geoms] y = [p.y if not p.is_empty else None for p in geoms] # matplotlib 1.4 does not support c=None, and < 2.0 does not support s=None if values is not None: kwargs["c"] = values if markersize is not None: kwargs["s"] = markersize # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color if marker is not None: kwargs["marker"] = marker _expand_kwargs(kwargs, multiindex) if "norm" not in kwargs: collection = ax.scatter(x, y, vmin=vmin, vmax=vmax, cmap=cmap, **kwargs) else: collection = ax.scatter(x, y, cmap=cmap, **kwargs) return collection plot_point_collection = deprecated(_plot_point_collection) def plot_series( s, cmap=None, color=None, ax=None, figsize=None, aspect="auto", **style_kwds ): """ Plot a GeoSeries. Generate a plot of a GeoSeries geometry with matplotlib. Parameters ---------- s : Series The GeoSeries to be plotted. Currently Polygon, MultiPolygon, LineString, MultiLineString and Point geometries can be plotted. cmap : str (default None) The name of a colormap recognized by matplotlib. Any colormap will work, but categorical colormaps are generally recommended. Examples of useful discrete colormaps include: tab10, tab20, Accent, Dark2, Paired, Pastel1, Set1, Set2 color : str (default None) If specified, all objects will be colored uniformly. ax : matplotlib.pyplot.Artist (default None) axes on which to draw the plot figsize : pair of floats (default None) Size of the resulting matplotlib.figure.Figure. If the argument ax is given explicitly, figsize is ignored. aspect : 'auto', 'equal', None or float (default 'auto') Set aspect of axis. If 'auto', the default aspect for map plots is 'equal'; if however data are not projected (coordinates are long/lat), the aspect is by default set to 1/cos(s_y * pi/180) with s_y the y coordinate of the middle of the GeoSeries (the mean of the y range of bounding box) so that a long/lat square appears square in the middle of the plot. This implies an Equirectangular projection. If None, the aspect of `ax` won't be changed. It can also be set manually (float) as the ratio of y-unit to x-unit. **style_kwds : dict Color options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. Returns ------- ax : matplotlib axes instance """ if "colormap" in style_kwds: warnings.warn( "'colormap' is deprecated, please use 'cmap' instead " "(for consistency with matplotlib)", FutureWarning, ) cmap = style_kwds.pop("colormap") if "axes" in style_kwds: warnings.warn( "'axes' is deprecated, please use 'ax' instead " "(for consistency with pandas)", FutureWarning, ) ax = style_kwds.pop("axes") try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "The matplotlib package is required for plotting in geopandas. " "You can install it using 'conda install -c conda-forge matplotlib' or " "'pip install matplotlib'." ) if ax is None: fig, ax = plt.subplots(figsize=figsize) if aspect == "auto": if s.crs and s.crs.is_geographic: bounds = s.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) # formula ported from R package sp # https://github.com/edzer/sp/blob/master/R/mapasp.R else: ax.set_aspect("equal") elif aspect is not None: ax.set_aspect(aspect) if s.empty: warnings.warn( "The GeoSeries you are attempting to plot is " "empty. Nothing has been displayed.", UserWarning, ) return ax if s.is_empty.all(): warnings.warn( "The GeoSeries you are attempting to plot is " "composed of empty geometries. Nothing has been displayed.", UserWarning, ) return ax # if cmap is specified, create range of colors based on cmap values = None if cmap is not None: values = np.arange(len(s)) if hasattr(cmap, "N"): values = values % cmap.N style_kwds["vmin"] = style_kwds.get("vmin", values.min()) style_kwds["vmax"] = style_kwds.get("vmax", values.max()) # decompose GeometryCollections geoms, multiindex = _flatten_multi_geoms(s.geometry, prefix="Geom") values = np.take(values, multiindex, axis=0) if cmap else None expl_series = geopandas.GeoSeries(geoms) geom_types = expl_series.type poly_idx = np.asarray((geom_types == "Polygon") | (geom_types == "MultiPolygon")) line_idx = np.asarray( (geom_types == "LineString") | (geom_types == "MultiLineString") | (geom_types == "LinearRing") ) point_idx = np.asarray((geom_types == "Point") | (geom_types == "MultiPoint")) # plot all Polygons and all MultiPolygon components in the same collection polys = expl_series[poly_idx] if not polys.empty: # color overrides both face and edgecolor. As we want people to be # able to use edgecolor as well, pass color to facecolor facecolor = style_kwds.pop("facecolor", None) if color is not None: facecolor = color values_ = values[poly_idx] if cmap else None _plot_polygon_collection( ax, polys, values_, facecolor=facecolor, cmap=cmap, **style_kwds ) # plot all LineStrings and MultiLineString components in same collection lines = expl_series[line_idx] if not lines.empty: values_ = values[line_idx] if cmap else None _plot_linestring_collection( ax, lines, values_, color=color, cmap=cmap, **style_kwds ) # plot all Points in the same collection points = expl_series[point_idx] if not points.empty: values_ = values[point_idx] if cmap else None _plot_point_collection( ax, points, values_, color=color, cmap=cmap, **style_kwds ) plt.draw() return ax def plot_dataframe( df, column=None, cmap=None, color=None, ax=None, cax=None, categorical=False, legend=False, scheme=None, k=5, vmin=None, vmax=None, markersize=None, figsize=None, legend_kwds=None, categories=None, classification_kwds=None, missing_kwds=None, aspect="auto", **style_kwds, ): """ Plot a GeoDataFrame. Generate a plot of a GeoDataFrame with matplotlib. If a column is specified, the plot coloring will be based on values in that column. Parameters ---------- column : str, np.array, pd.Series (default None) The name of the dataframe column, np.array, or pd.Series to be plotted. If np.array or pd.Series are used then it must have same length as dataframe. Values are used to color the plot. Ignored if `color` is also set. kind: str The kind of plots to produce: - 'geo': Map (default) Pandas Kinds - 'line' : line plot - 'bar' : vertical bar plot - 'barh' : horizontal bar plot - 'hist' : histogram - 'box' : BoxPlot - 'kde' : Kernel Density Estimation plot - 'density' : same as 'kde' - 'area' : area plot - 'pie' : pie plot - 'scatter' : scatter plot - 'hexbin' : hexbin plot. cmap : str (default None) The name of a colormap recognized by matplotlib. color : str (default None) If specified, all objects will be colored uniformly. ax : matplotlib.pyplot.Artist (default None) axes on which to draw the plot cax : matplotlib.pyplot Artist (default None) axes on which to draw the legend in case of color map. categorical : bool (default False) If False, cmap will reflect numerical values of the column being plotted. For non-numerical columns, this will be set to True. legend : bool (default False) Plot a legend. Ignored if no `column` is given, or if `color` is given. scheme : str (default None) Name of a choropleth classification scheme (requires mapclassify). A mapclassify.MapClassifier object will be used under the hood. Supported are all schemes provided by mapclassify (e.g. 'BoxPlot', 'EqualInterval', 'FisherJenks', 'FisherJenksSampled', 'HeadTailBreaks', 'JenksCaspall', 'JenksCaspallForced', 'JenksCaspallSampled', 'MaxP', 'MaximumBreaks', 'NaturalBreaks', 'Quantiles', 'Percentiles', 'StdMean', 'UserDefined'). Arguments can be passed in classification_kwds. k : int (default 5) Number of classes (ignored if scheme is None) vmin : None or float (default None) Minimum value of cmap. If None, the minimum data value in the column to be plotted is used. vmax : None or float (default None) Maximum value of cmap. If None, the maximum data value in the column to be plotted is used. markersize : str or float or sequence (default None) Only applies to point geometries within a frame. If a str, will use the values in the column of the frame specified by markersize to set the size of markers. Otherwise can be a value to apply to all points, or a sequence of the same length as the number of points. figsize : tuple of integers (default None) Size of the resulting matplotlib.figure.Figure. If the argument axes is given explicitly, figsize is ignored. legend_kwds : dict (default None) Keyword arguments to pass to matplotlib.pyplot.legend() or matplotlib.pyplot.colorbar(). Additional accepted keywords when `scheme` is specified: fmt : string A formatting specification for the bin edges of the classes in the legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``. labels : list-like A list of legend labels to override the auto-generated labels. Needs to have the same number of elements as the number of classes (`k`). interval : boolean (default False) An option to control brackets from mapclassify legend. If True, open/closed interval brackets are shown in the legend. categories : list-like Ordered list-like object of categories to be used for categorical plot. classification_kwds : dict (default None) Keyword arguments to pass to mapclassify missing_kwds : dict (default None) Keyword arguments specifying color options (as style_kwds) to be passed on to geometries with missing values in addition to or overwriting other style kwds. If None, geometries with missing values are not plotted. aspect : 'auto', 'equal', None or float (default 'auto') Set aspect of axis. If 'auto', the default aspect for map plots is 'equal'; if however data are not projected (coordinates are long/lat), the aspect is by default set to 1/cos(df_y * pi/180) with df_y the y coordinate of the middle of the GeoDataFrame (the mean of the y range of bounding box) so that a long/lat square appears square in the middle of the plot. This implies an Equirectangular projection. If None, the aspect of `ax` won't be changed. It can also be set manually (float) as the ratio of y-unit to x-unit. **style_kwds : dict Style options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. Returns ------- ax : matplotlib axes instance Examples -------- >>> df = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres")) >>> df.head() # doctest: +SKIP pop_est continent name iso_a3 \ gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 MULTIPOLY\ GON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 POLYGON (\ (33.90371 -0.95000, 34.07262 -1.05982... 2 603253 Africa W. Sahara ESH 906.5 POLYGON (\ (-8.66559 27.65643, -8.66512 27.58948... 3 35623680 North America Canada CAN 1674000.0 MULTIPOLY\ GON (((-122.84000 49.00000, -122.9742... 4 326625791 North America United States of America USA 18560000.0 MULTIPOLY\ GON (((-122.84000 49.00000, -120.0000... >>> df.plot("pop_est", cmap="Blues") # doctest: +SKIP See the User Guide page :doc:`../../user_guide/mapping` for details. """ if "colormap" in style_kwds: warnings.warn( "'colormap' is deprecated, please use 'cmap' instead " "(for consistency with matplotlib)", FutureWarning, ) cmap = style_kwds.pop("colormap") if "axes" in style_kwds: warnings.warn( "'axes' is deprecated, please use 'ax' instead " "(for consistency with pandas)", FutureWarning, ) ax = style_kwds.pop("axes") if column is not None and color is not None: warnings.warn( "Only specify one of 'column' or 'color'. Using 'color'.", UserWarning ) column = None try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "The matplotlib package is required for plotting in geopandas. " "You can install it using 'conda install -c conda-forge matplotlib' or " "'pip install matplotlib'." ) if ax is None: if cax is not None: raise ValueError("'ax' can not be None if 'cax' is not.") fig, ax = plt.subplots(figsize=figsize) if aspect == "auto": if df.crs and df.crs.is_geographic: bounds = df.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) # formula ported from R package sp # https://github.com/edzer/sp/blob/master/R/mapasp.R else: ax.set_aspect("equal") elif aspect is not None: ax.set_aspect(aspect) # GH 1555 # if legend_kwds set, copy so we don't update it in place if legend_kwds is not None: legend_kwds = legend_kwds.copy() if df.empty: warnings.warn( "The GeoDataFrame you are attempting to plot is " "empty. Nothing has been displayed.", UserWarning, ) return ax if isinstance(markersize, str): markersize = df[markersize].values if column is None: return plot_series( df.geometry, cmap=cmap, color=color, ax=ax, figsize=figsize, markersize=markersize, aspect=aspect, **style_kwds, ) # To accept pd.Series and np.arrays as column if isinstance(column, (np.ndarray, pd.Series)): if column.shape[0] != df.shape[0]: raise ValueError( "The dataframe and given column have different number of rows." ) else: values = column # Make sure index of a Series matches index of df if isinstance(values, pd.Series): values = values.reindex(df.index) else: values = df[column] if pd.api.types.is_categorical_dtype(values.dtype): if categories is not None: raise ValueError( "Cannot specify 'categories' when column has categorical dtype" ) categorical = True elif values.dtype is np.dtype("O") or categories: categorical = True nan_idx = np.asarray(pd.isna(values), dtype="bool") if scheme is not None: mc_err = ( "The 'mapclassify' package (>= 2.4.0) is " "required to use the 'scheme' keyword." ) try: import mapclassify except ImportError: raise ImportError(mc_err) if mapclassify.__version__ < LooseVersion("2.4.0"): raise ImportError(mc_err) if classification_kwds is None: classification_kwds = {} if "k" not in classification_kwds: classification_kwds["k"] = k binning = mapclassify.classify( np.asarray(values[~nan_idx]), scheme, **classification_kwds ) # set categorical to True for creating the legend categorical = True if legend_kwds is not None and "labels" in legend_kwds: if len(legend_kwds["labels"]) != binning.k: raise ValueError( "Number of labels must match number of bins, " "received {} labels for {} bins".format( len(legend_kwds["labels"]), binning.k ) ) else: labels = list(legend_kwds.pop("labels")) else: fmt = "{:.2f}" if legend_kwds is not None and "fmt" in legend_kwds: fmt = legend_kwds.pop("fmt") labels = binning.get_legend_classes(fmt) if legend_kwds is not None: show_interval = legend_kwds.pop("interval", False) else: show_interval = False if not show_interval: labels = [c[1:-1] for c in labels] values = pd.Categorical( [np.nan] * len(values), categories=binning.bins, ordered=True ) values[~nan_idx] = pd.Categorical.from_codes( binning.yb, categories=binning.bins, ordered=True ) if cmap is None: cmap = "viridis" # Define `values` as a Series if categorical: if cmap is None: cmap = "tab10" cat = pd.Categorical(values, categories=categories) categories = list(cat.categories) # values missing in the Categorical but not in original values missing = list(np.unique(values[~nan_idx & cat.isna()])) if missing: raise ValueError( "Column contains values not listed in categories. " "Missing categories: {}.".format(missing) ) values = cat.codes[~nan_idx] vmin = 0 if vmin is None else vmin vmax = len(categories) - 1 if vmax is None else vmax # fill values with placeholder where were NaNs originally to map them properly # (after removing them in categorical or scheme) if categorical: for n in np.where(nan_idx)[0]: values = np.insert(values, n, values[0]) mn = values[~np.isnan(values)].min() if vmin is None else vmin mx = values[~np.isnan(values)].max() if vmax is None else vmax # decompose GeometryCollections geoms, multiindex = _flatten_multi_geoms(df.geometry, prefix="Geom") values = np.take(values, multiindex, axis=0) nan_idx = np.take(nan_idx, multiindex, axis=0) expl_series = geopandas.GeoSeries(geoms) geom_types = expl_series.type poly_idx = np.asarray((geom_types == "Polygon") | (geom_types == "MultiPolygon")) line_idx = np.asarray( (geom_types == "LineString") | (geom_types == "MultiLineString") | (geom_types == "LinearRing") ) point_idx = np.asarray((geom_types == "Point") | (geom_types == "MultiPoint")) # plot all Polygons and all MultiPolygon components in the same collection polys = expl_series[poly_idx & np.invert(nan_idx)] subset = values[poly_idx & np.invert(nan_idx)] if not polys.empty: _plot_polygon_collection( ax, polys, subset, vmin=mn, vmax=mx, cmap=cmap, **style_kwds ) # plot all LineStrings and MultiLineString components in same collection lines = expl_series[line_idx & np.invert(nan_idx)] subset = values[line_idx & np.invert(nan_idx)] if not lines.empty: _plot_linestring_collection( ax, lines, subset, vmin=mn, vmax=mx, cmap=cmap, **style_kwds ) # plot all Points in the same collection points = expl_series[point_idx & np.invert(nan_idx)] subset = values[point_idx & np.invert(nan_idx)] if not points.empty: if isinstance(markersize, np.ndarray): markersize = np.take(markersize, multiindex, axis=0) markersize = markersize[point_idx & np.invert(nan_idx)] _plot_point_collection( ax, points, subset, vmin=mn, vmax=mx, markersize=markersize, cmap=cmap, **style_kwds, ) if missing_kwds is not None and not expl_series[nan_idx].empty: if color: if "color" not in missing_kwds: missing_kwds["color"] = color merged_kwds = style_kwds.copy() merged_kwds.update(missing_kwds) plot_series(expl_series[nan_idx], ax=ax, **merged_kwds) if legend and not color: if legend_kwds is None: legend_kwds = {} if "fmt" in legend_kwds: legend_kwds.pop("fmt") from matplotlib.lines import Line2D from matplotlib.colors import Normalize from matplotlib import cm norm = style_kwds.get("norm", None) if not norm: norm = Normalize(vmin=mn, vmax=mx) n_cmap = cm.ScalarMappable(norm=norm, cmap=cmap) if categorical: if scheme is not None: categories = labels patches = [] for value, cat in enumerate(categories): patches.append( Line2D( [0], [0], linestyle="none", marker="o", alpha=style_kwds.get("alpha", 1), markersize=10, markerfacecolor=n_cmap.to_rgba(value), markeredgewidth=0, ) ) if missing_kwds is not None: if "color" in merged_kwds: merged_kwds["facecolor"] = merged_kwds["color"] patches.append( Line2D( [0], [0], linestyle="none", marker="o", alpha=merged_kwds.get("alpha", 1), markersize=10, markerfacecolor=merged_kwds.get("facecolor", None), markeredgecolor=merged_kwds.get("edgecolor", None), markeredgewidth=merged_kwds.get( "linewidth", 1 if merged_kwds.get("edgecolor", False) else 0 ), ) ) categories.append(merged_kwds.get("label", "NaN")) legend_kwds.setdefault("numpoints", 1) legend_kwds.setdefault("loc", "best") ax.legend(patches, categories, **legend_kwds) else: if cax is not None: legend_kwds.setdefault("cax", cax) else: legend_kwds.setdefault("ax", ax) n_cmap.set_array(np.array([])) ax.get_figure().colorbar(n_cmap, **legend_kwds) plt.draw() return ax @doc(plot_dataframe) class GeoplotAccessor(PlotAccessor): _pandas_kinds = PlotAccessor._all_kinds def __call__(self, *args, **kwargs): data = self._parent.copy() kind = kwargs.pop("kind", "geo") if kind == "geo": return plot_dataframe(data, *args, **kwargs) if kind in self._pandas_kinds: # Access pandas plots return PlotAccessor(data)(kind=kind, **kwargs) else: # raise error raise ValueError(f"{kind} is not a valid plot kind") def geo(self, *args, **kwargs): return self(kind="geo", *args, **kwargs) geopandas-0.10.2/geopandas/sindex.py000066400000000000000000000741441413247402600173550ustar00rootroot00000000000000import warnings from shapely.geometry.base import BaseGeometry import pandas as pd import numpy as np from . import _compat as compat from ._decorator import doc def _get_sindex_class(): """Dynamically chooses a spatial indexing backend. Required to comply with _compat.USE_PYGEOS. The selection order goes PyGEOS > RTree > Error. """ if compat.USE_PYGEOS: return PyGEOSSTRTreeIndex if compat.HAS_RTREE: return RTreeIndex raise ImportError( "Spatial indexes require either `rtree` or `pygeos`. " "See installation instructions at https://geopandas.org/install.html" ) class BaseSpatialIndex: @property def valid_query_predicates(self): """Returns valid predicates for this spatial index. Returns ------- set Set of valid predicates for this spatial index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(0, 0), Point(1, 1)]) >>> s.sindex.valid_query_predicates # doctest: +SKIP {'contains', 'crosses', 'intersects', 'within', 'touches', \ 'overlaps', None, 'covers', 'contains_properly'} """ raise NotImplementedError def query(self, geometry, predicate=None, sort=False): """Return the index of all geometries in the tree with extents that intersect the envelope of the input geometry. When using the ``rtree`` package, this is not a vectorized function. If speed is important, please use PyGEOS. Parameters ---------- geometry : shapely geometry A single shapely geometry to query against the spatial index. predicate : {None, 'intersects', 'within', 'contains', \ 'overlaps', 'crosses', 'touches'}, optional If predicate is provided, the input geometry is tested using the predicate function against each item in the tree whose extent intersects the envelope of the input geometry: predicate(input_geometry, tree_geometry). If possible, prepared geometries are used to help speed up the predicate operation. sort : bool, default False If True, the results will be sorted in ascending order. If False, results are often sorted but there is no guarantee. Returns ------- matches : ndarray of shape (n_results, ) Integer indices for matching geometries from the spatial index. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) >>> s.sindex.query(box(1, 1, 3, 3), predicate="contains") array([2]) """ raise NotImplementedError def query_bulk(self, geometry, predicate=None, sort=False): """ Returns all combinations of each input geometry and geometries in the tree where the envelope of each input geometry intersects with the envelope of a tree geometry. In the context of a spatial join, input geometries are the “left” geometries that determine the order of the results, and tree geometries are “right” geometries that are joined against the left geometries. This effectively performs an inner join, where only those combinations of geometries that can be joined based on envelope overlap or optional predicate are returned. When using the ``rtree`` package, this is not a vectorized function and may be slow. If speed is important, please use PyGEOS. Parameters ---------- geometry : {GeoSeries, GeometryArray, numpy.array of PyGEOS geometries} Accepts GeoPandas geometry iterables (GeoSeries, GeometryArray) or a numpy array of PyGEOS geometries. predicate : {None, 'intersects', 'within', 'contains', 'overlaps', \ 'crosses', 'touches'}, optional If predicate is provided, the input geometries are tested using the predicate function against each item in the tree whose extent intersects the envelope of the each input geometry: predicate(input_geometry, tree_geometry). If possible, prepared geometries are used to help speed up the predicate operation. sort : bool, default False If True, results sorted lexicographically using geometry's indexes as the primary key and the sindex's indexes as the secondary key. If False, no additional sorting is applied. Returns ------- ndarray with shape (2, n) The first subarray contains input geometry integer indexes. The second subarray contains tree geometry integer indexes. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s2 = geopandas.GeoSeries([box(2, 2, 4, 4), box(5, 5, 6, 6)]) >>> s2 0 POLYGON ((4.00000 2.00000, 4.00000 4.00000, 2.... 1 POLYGON ((6.00000 5.00000, 6.00000 6.00000, 5.... dtype: geometry >>> s.sindex.query_bulk(s2) array([[0, 0, 0, 1, 1], [2, 3, 4, 5, 6]]) >>> s.sindex.query_bulk(s2, predicate="contains") array([[0], [3]]) """ raise NotImplementedError def nearest( self, geometry, return_all=True, max_distance=None, return_distance=False ): """ Return the nearest geometry in the tree for each input geometry in ``geometry``. .. note:: ``nearest`` currently only works with PyGEOS >= 0.10. Note that if PyGEOS is not available, geopandas will use rtree for the spatial index, where nearest has a different function signature to temporarily preserve existing functionality. See the documentation of :meth:`rtree.index.Index.nearest` for the details on the ``rtree``-based implementation. If multiple tree geometries have the same distance from an input geometry, multiple results will be returned for that input geometry by default. Specify ``return_all=False`` to only get a single nearest geometry (non-deterministic which nearest is returned). In the context of a spatial join, input geometries are the "left" geometries that determine the order of the results, and tree geometries are "right" geometries that are joined against the left geometries. If ``max_distance`` is not set, this will effectively be a left join because every geometry in ``geometry`` will have a nearest geometry in the tree. However, if ``max_distance`` is used, this becomes an inner join, since some geometries in ``geometry`` may not have a match in the tree. For performance reasons, it is highly recommended that you set the ``max_distance`` parameter. Parameters ---------- geometry : {shapely.geometry, GeoSeries, GeometryArray, numpy.array of PyGEOS \ geometries} A single shapely geometry, one of the GeoPandas geometry iterables (GeoSeries, GeometryArray), or a numpy array of PyGEOS geometries to query against the spatial index. return_all : bool, default True If there are multiple equidistant or intersecting nearest geometries, return all those geometries instead of a single nearest geometry. max_distance : float, optional Maximum distance within which to query for nearest items in tree. Must be greater than 0. By default None, indicating no distance limit. return_distance : bool, optional If True, will return distances in addition to indexes. By default False Returns ------- Indices or tuple of (indices, distances) Indices is an ndarray of shape (2,n) and distances (if present) an ndarray of shape (n). The first subarray of indices contains input geometry indices. The second subarray of indices contains tree geometry indices. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s.head() 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) dtype: geometry >>> s.sindex.nearest(Point(1, 1)) array([[0], [1]]) >>> s.sindex.nearest([box(4.9, 4.9, 5.1, 5.1)]) array([[0], [5]]) >>> s2 = geopandas.GeoSeries(geopandas.points_from_xy([7.6, 10], [7.6, 10])) >>> s2 0 POINT (7.60000 7.60000) 1 POINT (10.00000 10.00000) dtype: geometry >>> s.sindex.nearest(s2) array([[0, 1], [8, 9]]) """ raise NotImplementedError def intersection(self, coordinates): """Compatibility wrapper for rtree.index.Index.intersection, use ``query`` instead. Parameters ---------- coordinates : sequence or array Sequence of the form (min_x, min_y, max_x, max_y) to query a rectangle or (x, y) to query a point. Examples -------- >>> from shapely.geometry import Point, box >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s.sindex.intersection(box(1, 1, 3, 3).bounds) array([1, 2, 3]) Alternatively, you can use ``query``: >>> s.sindex.query(box(1, 1, 3, 3)) array([1, 2, 3]) """ raise NotImplementedError @property def size(self): """Size of the spatial index Number of leaves (input geometries) in the index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s.sindex.size 10 """ raise NotImplementedError @property def is_empty(self): """Check if the spatial index is empty Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(10), range(10))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) 3 POINT (3.00000 3.00000) 4 POINT (4.00000 4.00000) 5 POINT (5.00000 5.00000) 6 POINT (6.00000 6.00000) 7 POINT (7.00000 7.00000) 8 POINT (8.00000 8.00000) 9 POINT (9.00000 9.00000) dtype: geometry >>> s.sindex.is_empty False >>> s2 = geopandas.GeoSeries() >>> s2.sindex.is_empty True """ raise NotImplementedError if compat.HAS_RTREE: import rtree.index # noqa from rtree.core import RTreeError # noqa from shapely.prepared import prep # noqa class SpatialIndex(rtree.index.Index, BaseSpatialIndex): """Original rtree wrapper, kept for backwards compatibility.""" def __init__(self, *args): warnings.warn( "Directly using SpatialIndex is deprecated, and the class will be " "removed in a future version. Access the spatial index through the " "`GeoSeries.sindex` attribute, or use `rtree.index.Index` directly.", FutureWarning, stacklevel=2, ) super().__init__(*args) @doc(BaseSpatialIndex.intersection) def intersection(self, coordinates, *args, **kwargs): return super().intersection(coordinates, *args, **kwargs) @doc(BaseSpatialIndex.nearest) def nearest(self, *args, **kwargs): return super().nearest(*args, **kwargs) @property @doc(BaseSpatialIndex.size) def size(self): return len(self.leaves()[0][1]) @property @doc(BaseSpatialIndex.is_empty) def is_empty(self): if len(self.leaves()) > 1: return False return self.size < 1 class RTreeIndex(rtree.index.Index): """A simple wrapper around rtree's RTree Index Parameters ---------- geometry : np.array of Shapely geometries Geometries from which to build the spatial index. """ def __init__(self, geometry): stream = ( (i, item.bounds, None) for i, item in enumerate(geometry) if pd.notnull(item) and not item.is_empty ) try: super().__init__(stream) except RTreeError: # What we really want here is an empty generator error, or # for the bulk loader to log that the generator was empty # and move on. # See https://github.com/Toblerity/rtree/issues/20. super().__init__() # store reference to geometries for predicate queries self.geometries = geometry # create a prepared geometry cache self._prepared_geometries = np.array( [None] * self.geometries.size, dtype=object ) @property @doc(BaseSpatialIndex.valid_query_predicates) def valid_query_predicates(self): return { None, "intersects", "within", "contains", "overlaps", "crosses", "touches", "covers", "contains_properly", } @doc(BaseSpatialIndex.query) def query(self, geometry, predicate=None, sort=False): # handle invalid predicates if predicate not in self.valid_query_predicates: raise ValueError( "Got `predicate` = `{}`, `predicate` must be one of {}".format( predicate, self.valid_query_predicates ) ) # handle empty / invalid geometries if geometry is None: # return an empty integer array, similar to pygeys.STRtree.query. return np.array([], dtype=np.intp) if not isinstance(geometry, BaseGeometry): raise TypeError( "Got `geometry` of type `{}`, `geometry` must be ".format( type(geometry) ) + "a shapely geometry." ) if geometry.is_empty: return np.array([], dtype=np.intp) # query tree bounds = geometry.bounds # rtree operates on bounds tree_idx = list(self.intersection(bounds)) if not tree_idx: return np.array([], dtype=np.intp) # Check predicate # This is checked as input_geometry.predicate(tree_geometry) # When possible, we use prepared geometries. # Prepared geometries only support "intersects" and "contains" # For the special case of "within", we are able to flip the # comparison and check if tree_geometry.contains(input_geometry) # to still take advantage of prepared geometries. if predicate == "within": # To use prepared geometries for within, # we compare tree_geom.contains(input_geom) # Since we are preparing the tree geometries, # we cache them for multiple comparisons. res = [] for index_in_tree in tree_idx: if self._prepared_geometries[index_in_tree] is None: # if not already prepared, prepare and cache self._prepared_geometries[index_in_tree] = prep( self.geometries[index_in_tree] ) if self._prepared_geometries[index_in_tree].contains(geometry): res.append(index_in_tree) tree_idx = res elif predicate is not None: # For the remaining predicates, # we compare input_geom.predicate(tree_geom) if predicate in ( "contains", "intersects", "covers", "contains_properly", ): # prepare this input geometry geometry = prep(geometry) tree_idx = [ index_in_tree for index_in_tree in tree_idx if getattr(geometry, predicate)(self.geometries[index_in_tree]) ] # sort if requested if sort: # sorted return np.sort(np.array(tree_idx, dtype=np.intp)) # unsorted return np.array(tree_idx, dtype=np.intp) @doc(BaseSpatialIndex.query_bulk) def query_bulk(self, geometry, predicate=None, sort=False): # Iterates over geometry, applying func. tree_index = [] input_geometry_index = [] for i, geo in enumerate(geometry): res = self.query(geo, predicate=predicate, sort=sort) tree_index.extend(res) input_geometry_index.extend([i] * len(res)) return np.vstack([input_geometry_index, tree_index]) def nearest(self, coordinates, num_results=1, objects=False): """ Returns the nearest object or objects to the given coordinates. Requires rtree, and passes parameters directly to :meth:`rtree.index.Index.nearest`. This behaviour is deprecated and will be updated to be consistent with the pygeos PyGEOSSTRTreeIndex in a future release. If longer-term compatibility is required, use :meth:`rtree.index.Index.nearest` directly instead. Examples -------- >>> s = geopandas.GeoSeries(geopandas.points_from_xy(range(3), range(3))) >>> s 0 POINT (0.00000 0.00000) 1 POINT (1.00000 1.00000) 2 POINT (2.00000 2.00000) dtype: geometry >>> list(s.sindex.nearest((0, 0))) # doctest: +SKIP [0] >>> list(s.sindex.nearest((0.5, 0.5))) # doctest: +SKIP [0, 1] >>> list(s.sindex.nearest((3, 3), num_results=2)) # doctest: +SKIP [2, 1] >>> list(super(type(s.sindex), s.sindex).nearest((0, 0), ... num_results=2)) # doctest: +SKIP [0, 1] Parameters ---------- coordinates : sequence or array This may be an object that satisfies the numpy array protocol, providing the index’s dimension * 2 coordinate pairs representing the mink and maxk coordinates in each dimension defining the bounds of the query window. num_results : integer The number of results to return nearest to the given coordinates. If two index entries are equidistant, both are returned. This property means that num_results may return more items than specified objects : True / False / ‘raw’ If True, the nearest method will return index objects that were pickled when they were stored with each index entry, as well as the id and bounds of the index entries. If ‘raw’, it will return the object as entered into the database without the rtree.index.Item wrapper. """ warnings.warn( "sindex.nearest using the rtree backend was not previously documented " "and this behavior is deprecated in favor of matching the function " "signature provided by the pygeos backend (see " "PyGEOSSTRTreeIndex.nearest for details). This behavior will be " "updated in a future release.", FutureWarning, ) return super().nearest( coordinates, num_results=num_results, objects=objects ) @doc(BaseSpatialIndex.intersection) def intersection(self, coordinates): return super().intersection(coordinates, objects=False) @property @doc(BaseSpatialIndex.size) def size(self): if hasattr(self, "_size"): size = self._size else: # self.leaves are lists of tuples of (int, lists...) # index [0][1] always has an element, even for empty sindex # for an empty index, it will be an empty list size = len(self.leaves()[0][1]) self._size = size return size @property @doc(BaseSpatialIndex.is_empty) def is_empty(self): return self.geometries.size == 0 or self.size == 0 def __len__(self): return self.size if compat.HAS_PYGEOS: from . import geoseries # noqa from . import array # noqa import pygeos # noqa _PYGEOS_PREDICATES = {p.name for p in pygeos.strtree.BinaryPredicate} | set([None]) class PyGEOSSTRTreeIndex(pygeos.STRtree): """A simple wrapper around pygeos's STRTree. Parameters ---------- geometry : np.array of PyGEOS geometries Geometries from which to build the spatial index. """ def __init__(self, geometry): # set empty geometries to None to avoid segfault on GEOS <= 3.6 # see: # https://github.com/pygeos/pygeos/issues/146 # https://github.com/pygeos/pygeos/issues/147 non_empty = geometry.copy() non_empty[pygeos.is_empty(non_empty)] = None # set empty geometries to None to maintain indexing super().__init__(non_empty) # store geometries, including empty geometries for user access self.geometries = geometry.copy() @property def valid_query_predicates(self): """Returns valid predicates for the used spatial index. Returns ------- set Set of valid predicates for this spatial index. Examples -------- >>> from shapely.geometry import Point >>> s = geopandas.GeoSeries([Point(0, 0), Point(1, 1)]) >>> s.sindex.valid_query_predicates # doctest: +SKIP {'contains', 'crosses', 'covered_by', None, 'intersects', 'within', \ 'touches', 'overlaps', 'contains_properly', 'covers'} """ return _PYGEOS_PREDICATES @doc(BaseSpatialIndex.query) def query(self, geometry, predicate=None, sort=False): if predicate not in self.valid_query_predicates: raise ValueError( "Got `predicate` = `{}`; ".format(predicate) + "`predicate` must be one of {}".format( self.valid_query_predicates ) ) if isinstance(geometry, BaseGeometry): geometry = array._shapely_to_geom(geometry) matches = super().query(geometry=geometry, predicate=predicate) if sort: return np.sort(matches) return matches @staticmethod def _as_geometry_array(geometry): """Convert geometry into a numpy array of PyGEOS geometries. Parameters ---------- geometry An array-like of PyGEOS geometries, a GeoPandas GeoSeries/GeometryArray, shapely.geometry or list of shapely geometries. Returns ------- np.ndarray A numpy array of pygeos geometries. """ if isinstance(geometry, np.ndarray): return geometry elif isinstance(geometry, geoseries.GeoSeries): return geometry.values.data elif isinstance(geometry, array.GeometryArray): return geometry.data elif isinstance(geometry, BaseGeometry): return array._shapely_to_geom(geometry) elif isinstance(geometry, list): return np.asarray( [ array._shapely_to_geom(el) if isinstance(el, BaseGeometry) else el for el in geometry ] ) else: return np.asarray(geometry) @doc(BaseSpatialIndex.query_bulk) def query_bulk(self, geometry, predicate=None, sort=False): if predicate not in self.valid_query_predicates: raise ValueError( "Got `predicate` = `{}`, `predicate` must be one of {}".format( predicate, self.valid_query_predicates ) ) geometry = self._as_geometry_array(geometry) res = super().query_bulk(geometry, predicate) if sort: # sort by first array (geometry) and then second (tree) geo_res, tree_res = res indexing = np.lexsort((tree_res, geo_res)) return np.vstack((geo_res[indexing], tree_res[indexing])) return res @doc(BaseSpatialIndex.nearest) def nearest( self, geometry, return_all=True, max_distance=None, return_distance=False ): if not compat.PYGEOS_GE_010: raise NotImplementedError("sindex.nearest requires pygeos >= 0.10") geometry = self._as_geometry_array(geometry) if not return_all and max_distance is None and not return_distance: return super().nearest(geometry) result = super().nearest_all( geometry, max_distance=max_distance, return_distance=return_distance ) if return_distance: indices, distances = result else: indices = result if not return_all: # first subarray of geometry indices is sorted, so we can use this # trick to get the first of each index value mask = np.diff(indices[0, :]).astype("bool") # always select the first element mask = np.insert(mask, 0, True) indices = indices[:, mask] if return_distance: distances = distances[mask] if return_distance: return indices, distances else: return indices @doc(BaseSpatialIndex.intersection) def intersection(self, coordinates): # convert bounds to geometry # the old API uses tuples of bound, but pygeos uses geometries try: iter(coordinates) except TypeError: # likely not an iterable # this is a check that rtree does, we mimic it # to ensure a useful failure message raise TypeError( "Invalid coordinates, must be iterable in format " "(minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). " "Got `coordinates` = {}.".format(coordinates) ) # need to convert tuple of bounds to a geometry object if len(coordinates) == 4: indexes = super().query(pygeos.box(*coordinates)) elif len(coordinates) == 2: indexes = super().query(pygeos.points(*coordinates)) else: raise TypeError( "Invalid coordinates, must be iterable in format " "(minx, miny, maxx, maxy) (for bounds) or (x, y) (for points). " "Got `coordinates` = {}.".format(coordinates) ) return indexes @property @doc(BaseSpatialIndex.size) def size(self): return len(self) @property @doc(BaseSpatialIndex.is_empty) def is_empty(self): return len(self) == 0 geopandas-0.10.2/geopandas/testing.py000066400000000000000000000245331413247402600175350ustar00rootroot00000000000000""" Testing functionality for geopandas objects. """ import warnings import pandas as pd from geopandas import GeoDataFrame, GeoSeries from geopandas.array import GeometryDtype from geopandas import _vectorized def _isna(this): """isna version that works for both scalars and (Geo)Series""" with warnings.catch_warnings(): # GeoSeries.isna will raise a warning about no longer returning True # for empty geometries. This helper is used below always in combination # with an is_empty check to preserve behaviour, and thus we ignore the # warning here to avoid it bubbling up to the user warnings.filterwarnings( "ignore", r"GeoSeries.isna\(\) previously returned", UserWarning ) if hasattr(this, "isna"): return this.isna() elif hasattr(this, "isnull"): return this.isnull() else: return pd.isnull(this) def _geom_equals_mask(this, that): """ Test for geometric equality. Empty or missing geometries are considered equal. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` attribute) Returns ------- Series boolean Series, True if geometries in left equal geometries in right """ return ( this.geom_equals(that) | (this.is_empty & that.is_empty) | (_isna(this) & _isna(that)) ) def geom_equals(this, that): """ Test for geometric equality. Empty or missing geometries are considered equal. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` attribute) Returns ------- bool True if all geometries in left equal geometries in right """ return _geom_equals_mask(this, that).all() def _geom_almost_equals_mask(this, that): """ Test for 'almost' geometric equality. Empty or missing geometries considered equal. This method allows small difference in the coordinates, but this requires coordinates be in the same order for all components of a geometry. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` property) Returns ------- Series boolean Series, True if geometries in left almost equal geometries in right """ return ( this.geom_almost_equals(that) | (this.is_empty & that.is_empty) | (_isna(this) & _isna(that)) ) def geom_almost_equals(this, that): """ Test for 'almost' geometric equality. Empty or missing geometries considered equal. This method allows small difference in the coordinates, but this requires coordinates be in the same order for all components of a geometry. Parameters ---------- this, that : arrays of Geo objects (or anything that has an `is_empty` property) Returns ------- bool True if all geometries in left almost equal geometries in right """ return _geom_almost_equals_mask(this, that).all() def assert_geoseries_equal( left, right, check_dtype=True, check_index_type=False, check_series_type=True, check_less_precise=False, check_geom_type=False, check_crs=True, normalize=False, ): """ Test util for checking that two GeoSeries are equal. Parameters ---------- left, right : two GeoSeries check_dtype : bool, default False If True, check geo dtype [only included so it's a drop-in replacement for assert_series_equal]. check_index_type : bool, default False Check that index types are equal. check_series_type : bool, default True Check that both are same type (*and* are GeoSeries). If False, will attempt to convert both into GeoSeries. check_less_precise : bool, default False If True, use geom_almost_equals. if False, use geom_equals. check_geom_type : bool, default False If True, check that all the geom types are equal. check_crs: bool, default True If `check_series_type` is True, then also check that the crs matches. normalize: bool, default False If True, normalize the geometries before comparing equality. Typically useful with ``check_less_precise=True``, which uses ``geom_almost_equals`` and requires exact coordinate order. """ assert len(left) == len(right), "%d != %d" % (len(left), len(right)) if check_dtype: msg = "dtype should be a GeometryDtype, got {0}" assert isinstance(left.dtype, GeometryDtype), msg.format(left.dtype) assert isinstance(right.dtype, GeometryDtype), msg.format(left.dtype) if check_index_type: assert isinstance(left.index, type(right.index)) if check_series_type: assert isinstance(left, GeoSeries) assert isinstance(left, type(right)) if check_crs: assert left.crs == right.crs else: if not isinstance(left, GeoSeries): left = GeoSeries(left) if not isinstance(right, GeoSeries): right = GeoSeries(right, index=left.index) assert left.index.equals(right.index), "index: %s != %s" % (left.index, right.index) if check_geom_type: assert (left.type == right.type).all(), "type: %s != %s" % ( left.type, right.type, ) if normalize: left = GeoSeries(_vectorized.normalize(left.array.data)) right = GeoSeries(_vectorized.normalize(right.array.data)) if not check_crs: with warnings.catch_warnings(): warnings.filterwarnings("ignore", "CRS mismatch", UserWarning) _check_equality(left, right, check_less_precise) else: _check_equality(left, right, check_less_precise) def _truncated_string(geom): """Truncated WKT repr of geom""" s = str(geom) if len(s) > 100: return s[:100] + "..." else: return s def _check_equality(left, right, check_less_precise): assert_error_message = ( "{0} out of {1} geometries are not {3}equal.\n" "Indices where geometries are not {3}equal: {2} \n" "The first not {3}equal geometry:\n" "Left: {4}\n" "Right: {5}\n" ) if check_less_precise: precise = "almost " equal = _geom_almost_equals_mask(left, right) else: precise = "" equal = _geom_equals_mask(left, right) if not equal.all(): unequal_left_geoms = left[~equal] unequal_right_geoms = right[~equal] raise AssertionError( assert_error_message.format( len(unequal_left_geoms), len(left), unequal_left_geoms.index.to_list(), precise, _truncated_string(unequal_left_geoms.iloc[0]), _truncated_string(unequal_right_geoms.iloc[0]), ) ) def assert_geodataframe_equal( left, right, check_dtype=True, check_index_type="equiv", check_column_type="equiv", check_frame_type=True, check_like=False, check_less_precise=False, check_geom_type=False, check_crs=True, normalize=False, ): """ Check that two GeoDataFrames are equal/ Parameters ---------- left, right : two GeoDataFrames check_dtype : bool, default True Whether to check the DataFrame dtype is identical. check_index_type, check_column_type : bool, default 'equiv' Check that index types are equal. check_frame_type : bool, default True Check that both are same type (*and* are GeoDataFrames). If False, will attempt to convert both into GeoDataFrame. check_like : bool, default False If true, ignore the order of rows & columns check_less_precise : bool, default False If True, use geom_almost_equals. if False, use geom_equals. check_geom_type : bool, default False If True, check that all the geom types are equal. check_crs: bool, default True If `check_frame_type` is True, then also check that the crs matches. normalize: bool, default False If True, normalize the geometries before comparing equality. Typically useful with ``check_less_precise=True``, which uses ``geom_almost_equals`` and requires exact coordinate order. """ try: # added from pandas 0.20 from pandas.testing import assert_frame_equal, assert_index_equal except ImportError: from pandas.util.testing import assert_frame_equal, assert_index_equal # instance validation if check_frame_type: assert isinstance(left, GeoDataFrame) assert isinstance(left, type(right)) if check_crs: # no crs can be either None or {} if not left.crs and not right.crs: pass else: assert left.crs == right.crs else: if not isinstance(left, GeoDataFrame): left = GeoDataFrame(left) if not isinstance(right, GeoDataFrame): right = GeoDataFrame(right) # shape comparison assert left.shape == right.shape, ( "GeoDataFrame shape mismatch, left: {lshape!r}, right: {rshape!r}.\n" "Left columns: {lcols!r}, right columns: {rcols!r}" ).format( lshape=left.shape, rshape=right.shape, lcols=left.columns, rcols=right.columns ) if check_like: left, right = left.reindex_like(right), right # column comparison assert_index_equal( left.columns, right.columns, exact=check_column_type, obj="GeoDataFrame.columns" ) # geometry comparison for col, dtype in left.dtypes.iteritems(): if isinstance(dtype, GeometryDtype): assert_geoseries_equal( left[col], right[col], normalize=normalize, check_dtype=check_dtype, check_less_precise=check_less_precise, check_geom_type=check_geom_type, check_crs=check_crs, ) # drop geometries and check remaining columns left2 = left.drop([left._geometry_column_name], axis=1) right2 = right.drop([right._geometry_column_name], axis=1) assert_frame_equal( left2, right2, check_dtype=check_dtype, check_index_type=check_index_type, check_column_type=check_column_type, obj="GeoDataFrame", ) 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shapely.affinity import shapely.geometry from shapely.geometry.base import CAP_STYLE, JOIN_STYLE import shapely.wkb from shapely._buildcfg import geos_version import geopandas from geopandas.array import ( GeometryArray, from_shapely, from_wkb, from_wkt, points_from_xy, to_wkb, to_wkt, _check_crs, _crs_mismatch_warn, ) import geopandas._compat as compat import pytest triangle_no_missing = [ shapely.geometry.Polygon([(random.random(), random.random()) for i in range(3)]) for _ in range(10) ] triangles = triangle_no_missing + [shapely.geometry.Polygon(), None] T = from_shapely(triangles) points_no_missing = [ shapely.geometry.Point(random.random(), random.random()) for _ in range(20) ] points = points_no_missing + [None] P = from_shapely(points) def equal_geometries(result, expected): for r, e in zip(result, expected): if r is None or e is None: if not (r is None and e is None): return False elif not r.equals(e): return False return True def test_points(): x = np.arange(10).astype(np.float64) y = np.arange(10).astype(np.float64) ** 2 points = points_from_xy(x, y) assert isinstance(points, GeometryArray) for i in range(10): assert isinstance(points[i], shapely.geometry.Point) assert points[i].x == x[i] assert points[i].y == y[i] def test_points_from_xy(): # testing the top-level interface # using DataFrame column df = pd.DataFrame([{"x": x, "y": x, "z": x} for x in range(10)]) gs = [shapely.geometry.Point(x, x) for x in range(10)] gsz = [shapely.geometry.Point(x, x, x) for x in range(10)] geometry1 = geopandas.points_from_xy(df["x"], df["y"]) geometry2 = geopandas.points_from_xy(df["x"], df["y"], df["z"]) assert isinstance(geometry1, GeometryArray) assert isinstance(geometry2, GeometryArray) assert list(geometry1) == gs assert list(geometry2) == gsz # using Series or numpy arrays or lists for s in [pd.Series(range(10)), np.arange(10), list(range(10))]: geometry1 = geopandas.points_from_xy(s, s) geometry2 = geopandas.points_from_xy(s, s, s) assert isinstance(geometry1, GeometryArray) assert isinstance(geometry2, GeometryArray) assert list(geometry1) == gs assert list(geometry2) == gsz # using different lengths should throw error arr_10 = np.arange(10) arr_20 = np.arange(20) with pytest.raises(ValueError): geopandas.points_from_xy(x=arr_10, y=arr_20) geopandas.points_from_xy(x=arr_10, y=arr_10, z=arr_20) # Using incomplete arguments should throw error with pytest.raises(TypeError): geopandas.points_from_xy(x=s) geopandas.points_from_xy(y=s) geopandas.points_from_xy(z=s) def test_from_shapely(): assert isinstance(T, GeometryArray) assert equal_geometries(T, triangles) def test_from_shapely_geo_interface(): class Point: def __init__(self, x, y): self.x = x self.y = y @property def __geo_interface__(self): return {"type": "Point", "coordinates": (self.x, self.y)} result = from_shapely([Point(1.0, 2.0), Point(3.0, 4.0)]) expected = from_shapely( [shapely.geometry.Point(1.0, 2.0), shapely.geometry.Point(3.0, 4.0)] ) assert all(v.equals(t) for v, t in zip(result, expected)) def test_from_wkb(): # list L_wkb = [p.wkb for p in points_no_missing] res = from_wkb(L_wkb) assert isinstance(res, GeometryArray) assert all(v.equals(t) for v, t in zip(res, points_no_missing)) # array res = from_wkb(np.array(L_wkb, dtype=object)) assert isinstance(res, GeometryArray) assert all(v.equals(t) for v, t in zip(res, points_no_missing)) # missing values # TODO(pygeos) does not support empty strings, np.nan, or pd.NA missing_values = [None] if not compat.USE_PYGEOS: missing_values.extend([b"", np.nan]) if compat.PANDAS_GE_10: missing_values.append(pd.NA) res = from_wkb(missing_values) np.testing.assert_array_equal(res, np.full(len(missing_values), None)) # single MultiPolygon multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) res = from_wkb([multi_poly.wkb]) assert res[0] == multi_poly def test_from_wkb_hex(): geometry_hex = ["0101000000CDCCCCCCCCCC1440CDCCCCCCCC0C4A40"] res = from_wkb(geometry_hex) assert isinstance(res, GeometryArray) # array res = from_wkb(np.array(geometry_hex, dtype=object)) assert isinstance(res, GeometryArray) def test_to_wkb(): P = from_shapely(points_no_missing) res = to_wkb(P) exp = np.array([p.wkb for p in points_no_missing], dtype=object) assert isinstance(res, np.ndarray) np.testing.assert_array_equal(res, exp) res = to_wkb(P, hex=True) exp = np.array([p.wkb_hex for p in points_no_missing], dtype=object) assert isinstance(res, np.ndarray) np.testing.assert_array_equal(res, exp) # missing values a = from_shapely([None, points_no_missing[0]]) res = to_wkb(a) assert res[0] is None @pytest.mark.parametrize("string_type", ["str", "bytes"]) def test_from_wkt(string_type): if string_type == "str": f = six.text_type else: if six.PY3: def f(x): return bytes(x, "utf8") else: def f(x): return x # list L_wkt = [f(p.wkt) for p in points_no_missing] res = from_wkt(L_wkt) assert isinstance(res, GeometryArray) assert all(v.almost_equals(t) for v, t in zip(res, points_no_missing)) # array res = from_wkt(np.array(L_wkt, dtype=object)) assert isinstance(res, GeometryArray) assert all(v.almost_equals(t) for v, t in zip(res, points_no_missing)) # missing values # TODO(pygeos) does not support empty strings, np.nan, or pd.NA missing_values = [None] if not compat.USE_PYGEOS: missing_values.extend([f(""), np.nan]) if compat.PANDAS_GE_10: missing_values.append(pd.NA) res = from_wkb(missing_values) np.testing.assert_array_equal(res, np.full(len(missing_values), None)) # single MultiPolygon multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) res = from_wkt([f(multi_poly.wkt)]) assert res[0] == multi_poly def test_to_wkt(): P = from_shapely(points_no_missing) res = to_wkt(P, rounding_precision=-1) exp = np.array([p.wkt for p in points_no_missing], dtype=object) assert isinstance(res, np.ndarray) np.testing.assert_array_equal(res, exp) # missing values a = from_shapely([None, points_no_missing[0]]) res = to_wkt(a) assert res[0] is None @pytest.mark.parametrize( "attr,args", [ ("contains", ()), ("covers", ()), ("crosses", ()), ("disjoint", ()), ("geom_equals", ()), ("intersects", ()), ("overlaps", ()), ("touches", ()), ("within", ()), ("geom_equals_exact", (0.1,)), ("geom_almost_equals", (3,)), ], ) def test_predicates_vector_scalar(attr, args): na_value = False point = points[0] tri = triangles[0] for other in [point, tri, shapely.geometry.Polygon()]: result = getattr(T, attr)(other, *args) assert isinstance(result, np.ndarray) assert result.dtype == bool expected = [ getattr(tri, attr if "geom" not in attr else attr[5:])(other, *args) if tri is not None else na_value for tri in triangles ] assert result.tolist() == expected # TODO other is missing @pytest.mark.parametrize( "attr,args", [ ("contains", ()), ("covers", ()), ("crosses", ()), ("disjoint", ()), ("geom_equals", ()), ("intersects", ()), ("overlaps", ()), ("touches", ()), ("within", ()), ("geom_equals_exact", (0.1,)), ("geom_almost_equals", (3,)), ], ) def test_predicates_vector_vector(attr, args): na_value = False empty_value = True if attr == "disjoint" else False A = ( [shapely.geometry.Polygon(), None] + [ shapely.geometry.Polygon( [(random.random(), random.random()) for i in range(3)] ) for _ in range(100) ] + [None] ) B = [ shapely.geometry.Polygon([(random.random(), random.random()) for i in range(3)]) for _ in range(100) ] + [shapely.geometry.Polygon(), None, None] vec_A = from_shapely(A) vec_B = from_shapely(B) result = getattr(vec_A, attr)(vec_B, *args) assert isinstance(result, np.ndarray) assert result.dtype == bool expected = [] for a, b in zip(A, B): if a is None or b is None: expected.append(na_value) elif a.is_empty or b.is_empty: expected.append(empty_value) else: expected.append( getattr(a, attr if "geom" not in attr else attr[5:])(b, *args) ) assert result.tolist() == expected @pytest.mark.parametrize( "attr,args", [("equals_exact", (0.1,)), ("almost_equals", (3,))] ) def test_equals_deprecation(attr, args): point = points[0] tri = triangles[0] for other in [point, tri, shapely.geometry.Polygon()]: with pytest.warns(FutureWarning): result = getattr(T, attr)(other, *args) assert result.tolist() == getattr(T, "geom_" + attr)(other, *args).tolist() @pytest.mark.parametrize( "attr", [ "boundary", "centroid", "convex_hull", "envelope", "exterior", # 'interiors', ], ) def test_unary_geo(attr): na_value = None if attr == "boundary": # pygeos returns None for empty geometries if not compat.USE_PYGEOS: # boundary raises for empty geometry with pytest.raises(Exception): T.boundary values = triangle_no_missing + [None] A = from_shapely(values) else: values = triangles A = T result = getattr(A, attr) if attr == "exterior" and compat.USE_PYGEOS: # TODO(pygeos) # empty Polygon() has an exterior with shapely > 1.7, which gives # empty LinearRing instead of None, # but conversion to pygeos still results in empty GeometryCollection expected = [ getattr(t, attr) if t is not None and not t.is_empty else na_value for t in values ] else: expected = [getattr(t, attr) if t is not None else na_value for t in values] assert equal_geometries(result, expected) @pytest.mark.parametrize("attr", ["representative_point"]) def test_unary_geo_callable(attr): na_value = None result = getattr(T, attr)() expected = [getattr(t, attr)() if t is not None else na_value for t in triangles] assert equal_geometries(result, expected) @pytest.mark.parametrize( "attr", ["difference", "symmetric_difference", "union", "intersection"] ) def test_binary_geo_vector(attr): na_value = None quads = [shapely.geometry.Polygon(), None] while len(quads) < 12: geom = shapely.geometry.Polygon( [(random.random(), random.random()) for i in range(4)] ) if geom.is_valid: quads.append(geom) Q = from_shapely(quads) result = getattr(T, attr)(Q) expected = [ getattr(t, attr)(q) if t is not None and q is not None else na_value for t, q in zip(triangles, quads) ] assert equal_geometries(result, expected) @pytest.mark.parametrize( "attr", ["difference", "symmetric_difference", "union", "intersection"] ) def test_binary_geo_scalar(attr): na_value = None quads = [] while len(quads) < 1: geom = shapely.geometry.Polygon( [(random.random(), random.random()) for i in range(4)] ) if geom.is_valid: quads.append(geom) q = quads[0] for other in [q, shapely.geometry.Polygon()]: result = getattr(T, attr)(other) expected = [ getattr(t, attr)(other) if t is not None else na_value for t in triangles ] assert equal_geometries(result, expected) @pytest.mark.parametrize( "attr", [ "is_closed", "is_valid", "is_empty", "is_simple", "has_z", # for is_ring we raise a warning about the value for Polygon changing pytest.param( "is_ring", marks=pytest.mark.filterwarnings("ignore:is_ring:FutureWarning") ), ], ) def test_unary_predicates(attr): na_value = False if attr == "is_simple" and geos_version < (3, 8) and not compat.USE_PYGEOS: # poly.is_simple raises an error for empty polygon for GEOS < 3.8 with pytest.raises(Exception): T.is_simple vals = triangle_no_missing V = from_shapely(vals) else: vals = triangles V = T result = getattr(V, attr) if attr == "is_simple" and (geos_version < (3, 8) or compat.USE_PYGEOS): # poly.is_simple raises an error for empty polygon for GEOS < 3.8 # with shapely, pygeos always returns False for all GEOS versions # But even for Shapely with GEOS >= 3.8, empty GeometryCollection # returns True instead of False expected = [ getattr(t, attr) if t is not None and not t.is_empty else na_value for t in vals ] elif attr == "is_ring": expected = [ getattr(t.exterior, attr) if t is not None and t.exterior is not None else na_value for t in vals ] else: expected = [getattr(t, attr) if t is not None else na_value for t in vals] assert result.tolist() == expected # for is_ring we raise a warning about the value for Polygon changing @pytest.mark.filterwarnings("ignore:is_ring:FutureWarning") def test_is_ring(): g = [ shapely.geometry.LinearRing([(0, 0), (1, 1), (1, -1)]), shapely.geometry.LineString([(0, 0), (1, 1), (1, -1)]), shapely.geometry.LineString([(0, 0), (1, 1), (1, -1), (0, 0)]), shapely.geometry.Polygon([(0, 0), (1, 1), (1, -1)]), shapely.geometry.Polygon(), None, ] expected = [True, False, True, True, False, False] result = from_shapely(g).is_ring assert result.tolist() == expected @pytest.mark.parametrize("attr", ["area", "length"]) def test_unary_float(attr): na_value = np.nan result = getattr(T, attr) assert isinstance(result, np.ndarray) assert result.dtype == np.dtype("float64") expected = [getattr(t, attr) if t is not None else na_value for t in triangles] np.testing.assert_allclose(result, expected) def test_geom_types(): cat = T.geom_type # empty polygon has GeometryCollection type assert list(cat) == ["Polygon"] * (len(T) - 2) + ["GeometryCollection", None] def test_geom_types_null_mixed(): geoms = [ shapely.geometry.Polygon([(0, 0), (0, 1), (1, 1)]), None, shapely.geometry.Point(0, 1), ] G = from_shapely(geoms) cat = G.geom_type assert list(cat) == ["Polygon", None, "Point"] def test_binary_distance(): attr = "distance" na_value = np.nan # also use nan for empty # vector - vector result = P[: len(T)].distance(T[::-1]) expected = [ getattr(p, attr)(t) if not ((t is None or t.is_empty) or (p is None or p.is_empty)) else na_value for t, p in zip(triangles[::-1], points) ] np.testing.assert_allclose(result, expected) # vector - scalar p = points[0] result = T.distance(p) expected = [ getattr(t, attr)(p) if not (t is None or t.is_empty) else na_value for t in triangles ] np.testing.assert_allclose(result, expected) # other is empty result = T.distance(shapely.geometry.Polygon()) expected = [na_value] * len(T) np.testing.assert_allclose(result, expected) # TODO other is None def test_binary_relate(): attr = "relate" na_value = None # vector - vector result = getattr(P[: len(T)], attr)(T[::-1]) expected = [ getattr(p, attr)(t) if t is not None and p is not None else na_value for t, p in zip(triangles[::-1], points) ] assert list(result) == expected # vector - scalar p = points[0] result = getattr(T, attr)(p) expected = [getattr(t, attr)(p) if t is not None else na_value for t in triangles] assert list(result) == expected @pytest.mark.parametrize("normalized", [True, False]) def test_binary_project(normalized): na_value = np.nan lines = ( [None] + [ shapely.geometry.LineString( [(random.random(), random.random()) for _ in range(2)] ) for _ in range(len(P) - 2) ] + [None] ) L = from_shapely(lines) result = L.project(P, normalized=normalized) expected = [ l.project(p, normalized=normalized) if l is not None and p is not None else na_value for p, l in zip(points, lines) ] np.testing.assert_allclose(result, expected) @pytest.mark.parametrize("cap_style", [CAP_STYLE.round, CAP_STYLE.square]) @pytest.mark.parametrize("join_style", [JOIN_STYLE.round, JOIN_STYLE.bevel]) @pytest.mark.parametrize("resolution", [16, 25]) def test_buffer(resolution, cap_style, join_style): if compat.USE_PYGEOS: # TODO(pygeos) need to further investigate why this test fails if cap_style == 1 and join_style == 3: pytest.skip("failing TODO") na_value = None expected = [ p.buffer(0.1, resolution=resolution, cap_style=cap_style, join_style=join_style) if p is not None else na_value for p in points ] result = P.buffer( 0.1, resolution=resolution, cap_style=cap_style, join_style=join_style ) assert equal_geometries(expected, result) dist = np.array([0.1] * len(P)) result = P.buffer( dist, resolution=resolution, cap_style=cap_style, join_style=join_style ) assert equal_geometries(expected, result) def test_simplify(): triangles = [ shapely.geometry.Polygon( [(random.random(), random.random()) for i in range(3)] ).buffer(10) for _ in range(10) ] T = from_shapely(triangles) result = T.simplify(1) expected = [t.simplify(1) for t in triangles] assert all(a.equals(b) for a, b in zip(expected, result)) def test_unary_union(): geoms = [ shapely.geometry.Polygon([(0, 0), (0, 1), (1, 1)]), shapely.geometry.Polygon([(0, 0), (1, 0), (1, 1)]), ] G = from_shapely(geoms) u = G.unary_union() expected = shapely.geometry.Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) assert u.equals(expected) @pytest.mark.parametrize( "attr, arg", [ ("affine_transform", ([0, 1, 1, 0, 0, 0],)), ("translate", ()), ("rotate", (10,)), ("scale", ()), ("skew", ()), ], ) def test_affinity_methods(attr, arg): result = getattr(T, attr)(*arg) expected = [ getattr(shapely.affinity, attr)(t, *arg) if not (t is None or t.is_empty) else t for t in triangles ] assert equal_geometries(result, expected) # def test_coords(): # L = T.exterior.coords # assert L == [tuple(t.exterior.coords) for t in triangles] def test_coords_x_y(): na_value = np.nan result = P.x expected = [p.x if p is not None else na_value for p in points] np.testing.assert_allclose(result, expected) result = P.y expected = [p.y if p is not None else na_value for p in points] np.testing.assert_allclose(result, expected) def test_bounds(): result = T.bounds expected = [ t.bounds if not (t is None or t.is_empty) else [np.nan] * 4 for t in triangles ] np.testing.assert_allclose(result, expected) # additional check for one empty / missing for geom in [None, shapely.geometry.Polygon()]: E = from_shapely([geom]) result = E.bounds assert result.ndim == 2 assert result.dtype == "float64" np.testing.assert_allclose(result, np.array([[np.nan] * 4])) # empty array (https://github.com/geopandas/geopandas/issues/1195) E = from_shapely([]) result = E.bounds assert result.shape == (0, 4) assert result.dtype == "float64" def test_total_bounds(): result = T.total_bounds bounds = np.array( [t.bounds if not (t is None or t.is_empty) else [np.nan] * 4 for t in triangles] ) expected = np.array( [ np.nanmin(bounds[:, 0]), # minx np.nanmin(bounds[:, 1]), # miny np.nanmax(bounds[:, 2]), # maxx np.nanmax(bounds[:, 3]), # maxy ] ) np.testing.assert_allclose(result, expected) # additional check for empty array or one empty / missing for geoms in [[], [None], [shapely.geometry.Polygon()]]: E = from_shapely(geoms) result = E.total_bounds assert result.ndim == 1 assert result.dtype == "float64" np.testing.assert_allclose(result, np.array([np.nan] * 4)) def test_getitem(): points = [shapely.geometry.Point(i, i) for i in range(10)] P = from_shapely(points) P2 = P[P.area > 0.3] assert isinstance(P2, GeometryArray) P3 = P[[1, 3, 5]] assert len(P3) == 3 assert isinstance(P3, GeometryArray) assert [p.x for p in P3] == [1, 3, 5] P4 = P[1::2] assert len(P4) == 5 assert isinstance(P3, GeometryArray) assert [p.x for p in P4] == [1, 3, 5, 7, 9] P5 = P[1] assert isinstance(P5, shapely.geometry.Point) assert P5.equals(points[1]) def test_equality_ops(): with pytest.raises(ValueError): P[:5] == P[:7] a1 = from_shapely([points[1], points[2], points[3]]) a2 = from_shapely([points[1], points[0], points[3]]) res = a1 == a2 assert res.tolist() == [True, False, True] res = a1 != a2 assert res.tolist() == [False, True, False] # check the correct expansion of list-like geometry multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) a3 = from_shapely([points[1], points[2], points[3], multi_poly]) res = a3 == multi_poly assert res.tolist() == [False, False, False, True] def test_dir(): assert "contains" in dir(P) assert "data" in dir(P) def test_chaining(): # contains will give False for empty / missing T = from_shapely(triangle_no_missing) assert T.contains(T.centroid).all() def test_pickle(): import pickle T2 = pickle.loads(pickle.dumps(T)) # assert (T.data != T2.data).all() assert T2[-1] is None assert T2[-2].is_empty assert T[:-2].geom_equals(T2[:-2]).all() def test_raise_on_bad_sizes(): with pytest.raises(ValueError) as info: T.contains(P) assert "lengths" in str(info.value).lower() assert "12" in str(info.value) assert "21" in str(info.value) def test_buffer_single_multipolygon(): # https://github.com/geopandas/geopandas/issues/1130 multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) arr = from_shapely([multi_poly]) result = arr.buffer(1) expected = [multi_poly.buffer(1)] equal_geometries(result, expected) result = arr.buffer(np.array([1])) equal_geometries(result, expected) def test_astype_multipolygon(): # https://github.com/geopandas/geopandas/issues/1145 multi_poly = shapely.geometry.MultiPolygon( [shapely.geometry.box(0, 0, 1, 1), shapely.geometry.box(3, 3, 4, 4)] ) arr = from_shapely([multi_poly]) result = arr.astype(str) assert isinstance(result[0], str) assert result[0] == multi_poly.wkt # astype(object) does not convert to string result = arr.astype(object) assert isinstance(result[0], shapely.geometry.base.BaseGeometry) # astype(np_dtype) honors the dtype result = arr.astype(np.dtype("U10")) assert result.dtype == np.dtype("U10") assert result[0] == multi_poly.wkt[:10] def test_check_crs(): t1 = T.copy() t1.crs = 4326 assert _check_crs(t1, T) is False assert _check_crs(t1, t1) is True assert _check_crs(t1, T, allow_none=True) is True def test_crs_mismatch_warn(): t1 = T.copy() t2 = T.copy() t1.crs = 4326 t2.crs = 3857 # two different CRS with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): _crs_mismatch_warn(t1, t2) # left None with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): _crs_mismatch_warn(T, t2) # right None with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): _crs_mismatch_warn(t1, T) @pytest.mark.parametrize("NA", [None, np.nan]) def test_isna(NA): t1 = T.copy() t1[0] = NA assert t1[0] is None @pytest.mark.skipif(not compat.PANDAS_GE_10, reason="pd.NA introduced in pandas 1.0") def test_isna_pdNA(): t1 = T.copy() t1[0] = pd.NA assert t1[0] is None def test_shift_has_crs(): t = T.copy() t.crs = 4326 assert t.shift(1).crs == t.crs assert t.shift(0).crs == t.crs assert t.shift(-1).crs == t.crs @pytest.mark.skipif( not compat.PANDAS_GE_115, reason="crs only preserved in unique after pandas 1.1.5" ) def test_unique_has_crs(): t = T.copy() t.crs = 4326 assert t.unique().crs == t.crs class TestEstimateUtmCrs: def setup_method(self): self.esb = shapely.geometry.Point(-73.9847, 40.7484) self.sol = shapely.geometry.Point(-74.0446, 40.6893) self.landmarks = from_shapely([self.esb, self.sol], crs="epsg:4326") def test_estimate_utm_crs__geographic(self): if compat.PYPROJ_LT_3: with pytest.raises(RuntimeError, match=r"pyproj 3\+ required"): self.landmarks.estimate_utm_crs() else: assert self.landmarks.estimate_utm_crs() == CRS("EPSG:32618") assert self.landmarks.estimate_utm_crs("NAD83") == CRS("EPSG:26918") @pytest.mark.skipif(compat.PYPROJ_LT_3, reason="requires pyproj 3 or higher") def test_estimate_utm_crs__projected(self): assert self.landmarks.to_crs("EPSG:3857").estimate_utm_crs() == CRS( "EPSG:32618" ) @pytest.mark.skipif(not compat.PYPROJ_GE_31, reason="requires pyproj 3.1 or higher") def test_estimate_utm_crs__antimeridian(self): antimeridian = from_shapely( [ shapely.geometry.Point(1722483.900174921, 5228058.6143420935), shapely.geometry.Point(4624385.494808555, 8692574.544944234), ], crs="EPSG:3851", ) assert antimeridian.estimate_utm_crs() == CRS("EPSG:32760") @pytest.mark.skipif(compat.PYPROJ_LT_3, reason="requires pyproj 3 or higher") def test_estimate_utm_crs__out_of_bounds(self): with pytest.raises(RuntimeError, match="Unable to determine UTM CRS"): from_shapely( [shapely.geometry.Polygon([(0, 90), (1, 90), (2, 90)])], crs="EPSG:4326" ).estimate_utm_crs() @pytest.mark.skipif(compat.PYPROJ_LT_3, reason="requires pyproj 3 or higher") def test_estimate_utm_crs__missing_crs(self): with pytest.raises(RuntimeError, match="crs must be set"): from_shapely( [shapely.geometry.Polygon([(0, 90), (1, 90), (2, 90)])] ).estimate_utm_crs() geopandas-0.10.2/geopandas/tests/test_compat.py000066400000000000000000000016331413247402600215400ustar00rootroot00000000000000import pytest from geopandas._compat import import_optional_dependency def test_import_optional_dependency_present(): # pandas is not optional, but we know it is present pandas = import_optional_dependency("pandas") assert pandas is not None # module imported normally must be same import pandas as pd assert pandas == pd def test_import_optional_dependency_absent(): with pytest.raises(ImportError, match="Missing optional dependency 'foo'"): import_optional_dependency("foo") with pytest.raises(ImportError, match="foo is required"): import_optional_dependency("foo", extra="foo is required") @pytest.mark.parametrize( "bad_import", [["foo"], 0, False, True, {}, {"foo"}, {"foo": "bar"}] ) def test_import_optional_dependency_invalid(bad_import): with pytest.raises(ValueError, match="Invalid module name"): import_optional_dependency(bad_import) geopandas-0.10.2/geopandas/tests/test_config.py000066400000000000000000000014151413247402600215200ustar00rootroot00000000000000import geopandas import pytest def test_options(): assert "display_precision: " in repr(geopandas.options) assert dir(geopandas.options) == ["display_precision", "use_pygeos"] with pytest.raises(AttributeError): geopandas.options.non_existing_option with pytest.raises(AttributeError): geopandas.options.non_existing_option = 10 def test_options_display_precision(): assert geopandas.options.display_precision is None geopandas.options.display_precision = 5 assert geopandas.options.display_precision == 5 with pytest.raises(ValueError): geopandas.options.display_precision = "abc" with pytest.raises(ValueError): geopandas.options.display_precision = -1 geopandas.options.display_precision = None geopandas-0.10.2/geopandas/tests/test_crs.py000066400000000000000000000553121413247402600210470ustar00rootroot00000000000000from distutils.version import LooseVersion import os import random import numpy as np import pandas as pd from shapely.geometry import Point, Polygon, LineString import pyproj from geopandas import GeoSeries, GeoDataFrame, points_from_xy, datasets, read_file from geopandas.array import from_shapely, from_wkb, from_wkt, GeometryArray from geopandas.testing import assert_geodataframe_equal import pytest # pyproj 2.3.1 fixed a segfault for the case working in an environment with # 'init' dicts (https://github.com/pyproj4/pyproj/issues/415) PYPROJ_LT_231 = LooseVersion(pyproj.__version__) < LooseVersion("2.3.1") def _create_df(x, y=None, crs=None): y = y or x x = np.asarray(x) y = np.asarray(y) return GeoDataFrame( {"geometry": points_from_xy(x, y), "value1": x + y, "value2": x * y}, crs=crs ) def df_epsg26918(): # EPSG:26918 # Center coordinates # -1683723.64 6689139.23 return _create_df( x=range(-1683723, -1683723 + 10, 1), y=range(6689139, 6689139 + 10, 1), crs="epsg:26918", ) def test_to_crs_transform(): df = df_epsg26918() lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert_geodataframe_equal(df, utm, check_less_precise=True) def test_to_crs_transform__missing_data(): # https://github.com/geopandas/geopandas/issues/1573 df = df_epsg26918() df.loc[3, "geometry"] = None lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert_geodataframe_equal(df, utm, check_less_precise=True) def test_to_crs_inplace(): df = df_epsg26918() lonlat = df.to_crs(epsg=4326) df.to_crs(epsg=4326, inplace=True) assert_geodataframe_equal(df, lonlat, check_less_precise=True) def test_to_crs_geo_column_name(): # Test to_crs() with different geometry column name (GH#339) df = df_epsg26918() df = df.rename(columns={"geometry": "geom"}) df.set_geometry("geom", inplace=True) lonlat = df.to_crs(epsg=4326) utm = lonlat.to_crs(epsg=26918) assert lonlat.geometry.name == "geom" assert utm.geometry.name == "geom" assert_geodataframe_equal(df, utm, check_less_precise=True) # ----------------------------------------------------------------------------- # Test different supported formats for CRS specification @pytest.fixture( params=[ 4326, "epsg:4326", pytest.param( {"init": "epsg:4326"}, marks=pytest.mark.skipif(PYPROJ_LT_231, reason="segfault"), ), "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs", {"proj": "latlong", "ellps": "WGS84", "datum": "WGS84", "no_defs": True}, ], ids=["epsg_number", "epsg_string", "epsg_dict", "proj4_string", "proj4_dict"], ) def epsg4326(request): if isinstance(request.param, int): return dict(epsg=request.param) return dict(crs=request.param) @pytest.fixture( params=[ 26918, "epsg:26918", pytest.param( {"init": "epsg:26918", "no_defs": True}, marks=pytest.mark.skipif(PYPROJ_LT_231, reason="segfault"), ), "+proj=utm +zone=18 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ", {"proj": "utm", "zone": 18, "datum": "NAD83", "units": "m", "no_defs": True}, ], ids=["epsg_number", "epsg_string", "epsg_dict", "proj4_string", "proj4_dict"], ) def epsg26918(request): if isinstance(request.param, int): return dict(epsg=request.param) return dict(crs=request.param) @pytest.mark.filterwarnings("ignore:'\\+init:DeprecationWarning") @pytest.mark.filterwarnings("ignore:'\\+init:FutureWarning") def test_transform2(epsg4326, epsg26918): # with PROJ >= 7, the transformation using EPSG code vs proj4 string is # slightly different due to use of grid files or not -> turn off network # to not use grid files at all for this test os.environ["PROJ_NETWORK"] = "OFF" df = df_epsg26918() lonlat = df.to_crs(**epsg4326) utm = lonlat.to_crs(**epsg26918) # can't check for CRS equality, as the formats differ although representing # the same CRS assert_geodataframe_equal(df, utm, check_less_precise=True, check_crs=False) def test_crs_axis_order__always_xy(): df = GeoDataFrame(geometry=[Point(-1683723, 6689139)], crs="epsg:26918") lonlat = df.to_crs("epsg:4326") test_lonlat = GeoDataFrame( geometry=[Point(-110.1399901, 55.1350011)], crs="epsg:4326" ) assert_geodataframe_equal(lonlat, test_lonlat, check_less_precise=True) def test_skip_exact_same(): df = df_epsg26918() utm = df.to_crs(df.crs) assert_geodataframe_equal(df, utm, check_less_precise=True) # Test CRS on GeometryArray level class TestGeometryArrayCRS: def setup_method(self): self.osgb = pyproj.CRS(27700) self.wgs = pyproj.CRS(4326) self.geoms = [Point(0, 0), Point(1, 1)] self.polys = [ Polygon([(random.random(), random.random()) for i in range(3)]) for _ in range(10) ] self.arr = from_shapely(self.polys, crs=27700) def test_array(self): arr = from_shapely(self.geoms) arr.crs = 27700 assert arr.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) assert arr.crs == self.osgb arr = GeometryArray(arr) assert arr.crs == self.osgb arr = GeometryArray(arr, crs=4326) assert arr.crs == self.wgs def test_series(self): s = GeoSeries(crs=27700) assert s.crs == self.osgb assert s.values.crs == self.osgb arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) assert s.crs == self.osgb assert s.values.crs == self.osgb # manually change CRS s.crs = 4326 assert s.crs == self.wgs assert s.values.crs == self.wgs s = GeoSeries(self.geoms, crs=27700) assert s.crs == self.osgb assert s.values.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(arr) assert s.crs == self.osgb assert s.values.crs == self.osgb with pytest.warns(FutureWarning): s = GeoSeries(arr, crs=4326) assert s.crs == self.osgb @pytest.mark.filterwarnings("ignore:Assigning CRS") def test_dataframe(self): arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame(geometry=arr) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame(geometry=s) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # different passed CRS than array CRS is ignored with pytest.warns(FutureWarning, match="CRS mismatch"): df = GeoDataFrame(geometry=s, crs=4326) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb with pytest.warns(FutureWarning, match="CRS mismatch"): GeoDataFrame(geometry=s, crs=4326) with pytest.warns(FutureWarning, match="CRS mismatch"): GeoDataFrame({"data": [1, 2], "geometry": s}, crs=4326) with pytest.warns(FutureWarning, match="CRS mismatch"): GeoDataFrame(df, crs=4326).crs # manually change CRS arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame(geometry=s) df.crs = 4326 assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs df = GeoDataFrame(self.geoms, columns=["geom"], crs=27700) assert df.crs == self.osgb df = df.set_geometry("geom") assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb assert df.geom.crs == self.osgb assert df.geom.values.crs == self.osgb df = GeoDataFrame(geometry=self.geoms, crs=27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb df = GeoDataFrame(crs=27700) df = df.set_geometry(self.geoms) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # new geometry with set CRS has priority over GDF CRS df = GeoDataFrame(crs=27700) df = df.set_geometry(self.geoms, crs=4326) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame() df = df.set_geometry(s) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame() df = df.set_geometry(arr) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}, crs=4326) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs arr = from_shapely(self.geoms, crs=4326) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs # geometry column without geometry df = GeoDataFrame({"geometry": [0, 1]}) df.crs = 27700 assert df.crs == self.osgb def test_dataframe_setitem(self): # new geometry CRS has priority over GDF CRS arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) df = GeoDataFrame() df["geometry"] = s assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame() df["geometry"] = arr assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # test to_crs case (GH1960) arr = from_shapely(self.geoms) df = GeoDataFrame({"col1": [1, 2], "geometry": arr}, crs=4326) df["geometry"] = df["geometry"].to_crs(27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # test changing geometry crs not in the geometry column doesn't change the crs arr = from_shapely(self.geoms) df = GeoDataFrame( {"col1": [1, 2], "geometry": arr, "other_geom": arr}, crs=4326 ) df["other_geom"] = from_shapely(self.geoms, crs=27700) assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df["geometry"].crs == self.wgs assert df["other_geom"].crs == self.osgb @pytest.mark.parametrize( "scalar", [None, Point(0, 0), LineString([(0, 0), (1, 1)])] ) def test_scalar(self, scalar): with pytest.warns(FutureWarning): df = GeoDataFrame() df.crs = 4326 df["geometry"] = scalar assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs def test_read_file(self): nybb_filename = datasets.get_path("nybb") df = read_file(nybb_filename) assert df.crs == pyproj.CRS(2263) assert df.geometry.crs == pyproj.CRS(2263) assert df.geometry.values.crs == pyproj.CRS(2263) def test_multiple_geoms(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb assert df.col1.crs == self.wgs assert df.col1.values.crs == self.wgs def test_multiple_geoms_set_geom(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) df = df.set_geometry("col1") assert df.crs == self.wgs assert df.geometry.crs == self.wgs assert df.geometry.values.crs == self.wgs assert df["geometry"].crs == self.osgb assert df["geometry"].values.crs == self.osgb def test_assign_cols(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) df["geom2"] = s df["geom3"] = s.values df["geom4"] = from_shapely(self.geoms) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb assert df.geom2.crs == self.wgs assert df.geom2.values.crs == self.wgs assert df.geom3.crs == self.wgs assert df.geom3.values.crs == self.wgs assert df.geom4.crs is None assert df.geom4.values.crs is None def test_copy(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) arr_copy = arr.copy() assert arr_copy.crs == arr.crs s_copy = s.copy() assert s_copy.crs == s.crs assert s_copy.values.crs == s.values.crs df_copy = df.copy() assert df_copy.crs == df.crs assert df_copy.geometry.crs == df.geometry.crs assert df_copy.geometry.values.crs == df.geometry.values.crs assert df_copy.col1.crs == df.col1.crs assert df_copy.col1.values.crs == df.col1.values.crs def test_rename(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame(s, geometry=arr, columns=["col1"]) df = df.rename(columns={"geometry": "geom"}).set_geometry("geom") assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb df = df.rename_geometry("geom2") assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb df = df.rename(columns={"col1": "column1"}) assert df.column1.crs == self.wgs assert df.column1.values.crs == self.wgs def test_geoseries_to_crs(self): s = GeoSeries(self.geoms, crs=27700) s = s.to_crs(4326) assert s.crs == self.wgs assert s.values.crs == self.wgs df = GeoDataFrame(geometry=s) assert df.crs == self.wgs df = df.to_crs(27700) assert df.crs == self.osgb assert df.geometry.crs == self.osgb assert df.geometry.values.crs == self.osgb # make sure that only active geometry is transformed arr = from_shapely(self.geoms, crs=4326) df["col1"] = arr df = df.to_crs(3857) assert df.col1.crs == self.wgs assert df.col1.values.crs == self.wgs def test_array_to_crs(self): arr = from_shapely(self.geoms, crs=27700) arr = arr.to_crs(4326) assert arr.crs == self.wgs def test_from_shapely(self): arr = from_shapely(self.geoms, crs=27700) assert arr.crs == self.osgb def test_from_wkb(self): L_wkb = [p.wkb for p in self.geoms] arr = from_wkb(L_wkb, crs=27700) assert arr.crs == self.osgb def test_from_wkt(self): L_wkt = [p.wkt for p in self.geoms] arr = from_wkt(L_wkt, crs=27700) assert arr.crs == self.osgb def test_points_from_xy(self): df = pd.DataFrame([{"x": x, "y": x, "z": x} for x in range(10)]) arr = points_from_xy(df["x"], df["y"], crs=27700) assert arr.crs == self.osgb # setting CRS in GeoSeries should not set it in passed array without CRS def test_original(self): arr = from_shapely(self.geoms) s = GeoSeries(arr, crs=27700) assert arr.crs is None assert s.crs == self.osgb def test_ops(self): arr = self.arr bound = arr.boundary assert bound.crs == self.osgb cent = arr.centroid assert cent.crs == self.osgb hull = arr.convex_hull assert hull.crs == self.osgb envelope = arr.envelope assert envelope.crs == self.osgb exterior = arr.exterior assert exterior.crs == self.osgb representative_point = arr.representative_point() assert representative_point.crs == self.osgb def test_binary_ops(self): arr = self.arr quads = [] while len(quads) < 10: geom = Polygon([(random.random(), random.random()) for i in range(4)]) if geom.is_valid: quads.append(geom) arr2 = from_shapely(quads, crs=27700) difference = arr.difference(arr2) assert difference.crs == self.osgb intersection = arr.intersection(arr2) assert intersection.crs == self.osgb symmetric_difference = arr.symmetric_difference(arr2) assert symmetric_difference.crs == self.osgb union = arr.union(arr2) assert union.crs == self.osgb def test_other(self): arr = self.arr buffer = arr.buffer(5) assert buffer.crs == self.osgb interpolate = arr.exterior.interpolate(0.1) assert interpolate.crs == self.osgb simplify = arr.simplify(5) assert simplify.crs == self.osgb @pytest.mark.parametrize( "attr, arg", [ ("affine_transform", ([0, 1, 1, 0, 0, 0],)), ("translate", ()), ("rotate", (10,)), ("scale", ()), ("skew", ()), ], ) def test_affinity_methods(self, attr, arg): result = getattr(self.arr, attr)(*arg) assert result.crs == self.osgb def test_slice(self): s = GeoSeries(self.arr, crs=27700) assert s.iloc[1:].values.crs == self.osgb df = GeoDataFrame({"col1": self.arr}, geometry=s) assert df.iloc[1:].geometry.values.crs == self.osgb assert df.iloc[1:].col1.values.crs == self.osgb def test_concat(self): s = GeoSeries(self.arr, crs=27700) assert pd.concat([s, s]).values.crs == self.osgb df = GeoDataFrame({"col1": from_shapely(self.geoms, crs=4326)}, geometry=s) assert pd.concat([df, df]).geometry.values.crs == self.osgb assert pd.concat([df, df]).col1.values.crs == self.wgs def test_merge(self): arr = from_shapely(self.geoms, crs=27700) s = GeoSeries(self.geoms, crs=4326) df = GeoDataFrame({"col1": s}, geometry=arr) df2 = GeoDataFrame({"col2": s}, geometry=arr).rename_geometry("geom") merged = df.merge(df2, left_index=True, right_index=True) assert merged.col1.values.crs == self.wgs assert merged.geometry.values.crs == self.osgb assert merged.col2.values.crs == self.wgs assert merged.geom.values.crs == self.osgb assert merged.crs == self.osgb # CRS should be assigned to geometry def test_deprecation(self): with pytest.warns(FutureWarning): df = GeoDataFrame([], crs=27700) # https://github.com/geopandas/geopandas/issues/1548 # ensure we still have converted the crs value to a CRS object assert isinstance(df.crs, pyproj.CRS) with pytest.warns(FutureWarning): df = GeoDataFrame([]) df.crs = 27700 assert isinstance(df.crs, pyproj.CRS) # make sure that geometry column from list has CRS (__setitem__) def test_setitem_geometry(self): arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame({"col1": [0, 1]}, geometry=arr) df["geometry"] = [g for g in df.geometry] assert df.geometry.values.crs == self.osgb df2 = GeoDataFrame({"col1": [0, 1]}, geometry=arr) df2["geometry"] = from_shapely(self.geoms, crs=4326) assert df2.geometry.values.crs == self.wgs def test_astype(self): arr = from_shapely(self.geoms, crs=27700) df = GeoDataFrame({"col1": [0, 1]}, geometry=arr) df2 = df.astype({"col1": str}) assert df2.crs == self.osgb def test_apply(self): s = GeoSeries(self.arr) assert s.crs == 27700 # apply preserves the CRS if the result is a GeoSeries result = s.apply(lambda x: x.centroid) assert result.crs == 27700 def test_apply_geodataframe(self): df = GeoDataFrame({"col1": [0, 1]}, geometry=self.geoms, crs=27700) assert df.crs == 27700 # apply preserves the CRS if the result is a GeoDataFrame result = df.apply(lambda col: col, axis=0) assert result.crs == 27700 result = df.apply(lambda row: row, axis=1) assert result.crs == 27700 class TestSetCRS: @pytest.mark.parametrize( "constructor", [ lambda geoms, crs: GeoSeries(geoms, crs=crs), lambda geoms, crs: GeoDataFrame(geometry=geoms, crs=crs), ], ids=["geoseries", "geodataframe"], ) def test_set_crs(self, constructor): naive = constructor([Point(0, 0), Point(1, 1)], crs=None) assert naive.crs is None # by default returns a copy result = naive.set_crs(crs="EPSG:4326") assert result.crs == "EPSG:4326" assert naive.crs is None result = naive.set_crs(epsg=4326) assert result.crs == "EPSG:4326" assert naive.crs is None # with inplace=True result = naive.set_crs(crs="EPSG:4326", inplace=True) assert result is naive assert result.crs == naive.crs == "EPSG:4326" # raise for non-naive when crs would be overridden non_naive = constructor([Point(0, 0), Point(1, 1)], crs="EPSG:4326") assert non_naive.crs == "EPSG:4326" with pytest.raises(ValueError, match="already has a CRS"): non_naive.set_crs("EPSG:3857") # allow for equal crs result = non_naive.set_crs("EPSG:4326") assert result.crs == "EPSG:4326" # replace with allow_override=True result = non_naive.set_crs("EPSG:3857", allow_override=True) assert non_naive.crs == "EPSG:4326" assert result.crs == "EPSG:3857" result = non_naive.set_crs("EPSG:3857", allow_override=True, inplace=True) assert non_naive.crs == "EPSG:3857" assert result.crs == "EPSG:3857" # raise error when no crs is passed with pytest.raises(ValueError): naive.set_crs(crs=None, epsg=None) geopandas-0.10.2/geopandas/tests/test_datasets.py000066400000000000000000000004701413247402600220630ustar00rootroot00000000000000from geopandas import GeoDataFrame, read_file from geopandas.datasets import get_path import pytest @pytest.mark.parametrize( "test_dataset", ["naturalearth_lowres", "naturalearth_cities", "nybb"] ) def test_read_paths(test_dataset): assert isinstance(read_file(get_path(test_dataset)), GeoDataFrame) geopandas-0.10.2/geopandas/tests/test_decorator.py000066400000000000000000000027261413247402600222430ustar00rootroot00000000000000from textwrap import dedent from geopandas._decorator import doc @doc(method="cumsum", operation="sum") def cumsum(whatever): """ This is the {method} method. It computes the cumulative {operation}. """ ... @doc( cumsum, dedent( """ Examples -------- >>> cumavg([1, 2, 3]) 2 """ ), method="cumavg", operation="average", ) def cumavg(whatever): ... @doc(cumsum, method="cummax", operation="maximum") def cummax(whatever): ... @doc(cummax, method="cummin", operation="minimum") def cummin(whatever): ... def test_docstring_formatting(): docstr = dedent( """ This is the cumsum method. It computes the cumulative sum. """ ) assert cumsum.__doc__ == docstr def test_docstring_appending(): docstr = dedent( """ This is the cumavg method. It computes the cumulative average. Examples -------- >>> cumavg([1, 2, 3]) 2 """ ) assert cumavg.__doc__ == docstr def test_doc_template_from_func(): docstr = dedent( """ This is the cummax method. It computes the cumulative maximum. """ ) assert cummax.__doc__ == docstr def test_inherit_doc_template(): docstr = dedent( """ This is the cummin method. It computes the cumulative minimum. """ ) assert cummin.__doc__ == docstr geopandas-0.10.2/geopandas/tests/test_dissolve.py000066400000000000000000000217151413247402600221100ustar00rootroot00000000000000import numpy as np import pandas as pd import geopandas from geopandas import GeoDataFrame, read_file from geopandas import _compat as compat from pandas.testing import assert_frame_equal import pytest @pytest.fixture def nybb_polydf(): nybb_filename = geopandas.datasets.get_path("nybb") nybb_polydf = read_file(nybb_filename) nybb_polydf = nybb_polydf[["geometry", "BoroName", "BoroCode"]] nybb_polydf = nybb_polydf.rename(columns={"geometry": "myshapes"}) nybb_polydf = nybb_polydf.set_geometry("myshapes") nybb_polydf["manhattan_bronx"] = 5 nybb_polydf.loc[3:4, "manhattan_bronx"] = 6 return nybb_polydf @pytest.fixture def merged_shapes(nybb_polydf): # Merged geometry manhattan_bronx = nybb_polydf.loc[3:4] others = nybb_polydf.loc[0:2] collapsed = [others.geometry.unary_union, manhattan_bronx.geometry.unary_union] merged_shapes = GeoDataFrame( {"myshapes": collapsed}, geometry="myshapes", index=pd.Index([5, 6], name="manhattan_bronx"), crs=nybb_polydf.crs, ) return merged_shapes @pytest.fixture def first(merged_shapes): first = merged_shapes.copy() first["BoroName"] = ["Staten Island", "Manhattan"] first["BoroCode"] = [5, 1] return first @pytest.fixture def expected_mean(merged_shapes): test_mean = merged_shapes.copy() test_mean["BoroCode"] = [4, 1.5] return test_mean def test_geom_dissolve(nybb_polydf, first): test = nybb_polydf.dissolve("manhattan_bronx") assert test.geometry.name == "myshapes" assert test.geom_almost_equals(first).all() def test_dissolve_retains_existing_crs(nybb_polydf): assert nybb_polydf.crs is not None test = nybb_polydf.dissolve("manhattan_bronx") assert test.crs is not None def test_dissolve_retains_nonexisting_crs(nybb_polydf): nybb_polydf.crs = None test = nybb_polydf.dissolve("manhattan_bronx") assert test.crs is None def first_dissolve(nybb_polydf, first): test = nybb_polydf.dissolve("manhattan_bronx") assert_frame_equal(first, test, check_column_type=False) def test_mean_dissolve(nybb_polydf, first, expected_mean): test = nybb_polydf.dissolve("manhattan_bronx", aggfunc="mean") assert_frame_equal(expected_mean, test, check_column_type=False) test = nybb_polydf.dissolve("manhattan_bronx", aggfunc=np.mean) assert_frame_equal(expected_mean, test, check_column_type=False) def test_multicolumn_dissolve(nybb_polydf, first): multi = nybb_polydf.copy() multi["dup_col"] = multi.manhattan_bronx multi_test = multi.dissolve(["manhattan_bronx", "dup_col"], aggfunc="first") first_copy = first.copy() first_copy["dup_col"] = first_copy.index first_copy = first_copy.set_index([first_copy.index, "dup_col"]) assert_frame_equal(multi_test, first_copy, check_column_type=False) def test_reset_index(nybb_polydf, first): test = nybb_polydf.dissolve("manhattan_bronx", as_index=False) comparison = first.reset_index() assert_frame_equal(comparison, test, check_column_type=False) def test_dissolve_none(nybb_polydf): test = nybb_polydf.dissolve(by=None) expected = GeoDataFrame( { nybb_polydf.geometry.name: [nybb_polydf.geometry.unary_union], "BoroName": ["Staten Island"], "BoroCode": [5], "manhattan_bronx": [5], }, geometry=nybb_polydf.geometry.name, crs=nybb_polydf.crs, ) assert_frame_equal(expected, test, check_column_type=False) def test_dissolve_none_mean(nybb_polydf): test = nybb_polydf.dissolve(aggfunc="mean") expected = GeoDataFrame( { nybb_polydf.geometry.name: [nybb_polydf.geometry.unary_union], "BoroCode": [3.0], "manhattan_bronx": [5.4], }, geometry=nybb_polydf.geometry.name, crs=nybb_polydf.crs, ) assert_frame_equal(expected, test, check_column_type=False) def test_dissolve_level(): gdf = geopandas.GeoDataFrame( { "a": [1, 1, 2, 2], "b": [3, 4, 4, 4], "c": [3, 4, 5, 6], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)", "POINT (3 3)"] ), } ).set_index(["a", "b", "c"]) expected_a = geopandas.GeoDataFrame( { "a": [1, 2], "geometry": geopandas.array.from_wkt( ["MULTIPOINT (0 0, 1 1)", "MULTIPOINT (2 2, 3 3)"] ), } ).set_index("a") expected_b = geopandas.GeoDataFrame( { "b": [3, 4], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "MULTIPOINT (1 1, 2 2, 3 3)"] ), } ).set_index("b") expected_ab = geopandas.GeoDataFrame( { "a": [1, 1, 2], "b": [3, 4, 4], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "MULTIPOINT (2 2, 3 3)"] ), } ).set_index(["a", "b"]) assert_frame_equal(expected_a, gdf.dissolve(level=0)) assert_frame_equal(expected_a, gdf.dissolve(level="a")) assert_frame_equal(expected_b, gdf.dissolve(level=1)) assert_frame_equal(expected_b, gdf.dissolve(level="b")) assert_frame_equal(expected_ab, gdf.dissolve(level=[0, 1])) assert_frame_equal(expected_ab, gdf.dissolve(level=["a", "b"])) def test_dissolve_sort(): gdf = geopandas.GeoDataFrame( { "a": [2, 1, 1], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)"] ), } ) expected_unsorted = geopandas.GeoDataFrame( { "a": [2, 1], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "MULTIPOINT (1 1, 2 2)"] ), } ).set_index("a") expected_sorted = expected_unsorted.sort_index() assert_frame_equal(expected_sorted, gdf.dissolve("a")) assert_frame_equal(expected_unsorted, gdf.dissolve("a", sort=False)) def test_dissolve_categorical(): gdf = geopandas.GeoDataFrame( { "cat": pd.Categorical(["a", "a", "b", "b"]), "noncat": [1, 1, 1, 2], "to_agg": [1, 2, 3, 4], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)", "POINT (3 3)"] ), } ) # when observed=False we get an additional observation # that wasn't in the original data expected_gdf_observed_false = geopandas.GeoDataFrame( { "cat": pd.Categorical(["a", "a", "b", "b"]), "noncat": [1, 2, 1, 2], "geometry": geopandas.array.from_wkt( [ "MULTIPOINT (0 0, 1 1)", None, "POINT (2 2)", "POINT (3 3)", ] ), "to_agg": [1, None, 3, 4], } ).set_index(["cat", "noncat"]) # when observed=True we do not get any additional observations expected_gdf_observed_true = geopandas.GeoDataFrame( { "cat": pd.Categorical(["a", "b", "b"]), "noncat": [1, 1, 2], "geometry": geopandas.array.from_wkt( ["MULTIPOINT (0 0, 1 1)", "POINT (2 2)", "POINT (3 3)"] ), "to_agg": [1, 3, 4], } ).set_index(["cat", "noncat"]) assert_frame_equal(expected_gdf_observed_false, gdf.dissolve(["cat", "noncat"])) assert_frame_equal( expected_gdf_observed_true, gdf.dissolve(["cat", "noncat"], observed=True) ) @pytest.mark.skipif( not compat.PANDAS_GE_11, reason="dropna groupby kwarg added in pandas 1.1.0" ) def test_dissolve_dropna(): gdf = geopandas.GeoDataFrame( { "a": [1, 1, None], "geometry": geopandas.array.from_wkt( ["POINT (0 0)", "POINT (1 1)", "POINT (2 2)"] ), } ) expected_with_na = geopandas.GeoDataFrame( { "a": [1.0, np.nan], "geometry": geopandas.array.from_wkt( ["MULTIPOINT (0 0, 1 1)", "POINT (2 2)"] ), } ).set_index("a") expected_no_na = geopandas.GeoDataFrame( { "a": [1.0], "geometry": geopandas.array.from_wkt(["MULTIPOINT (0 0, 1 1)"]), } ).set_index("a") assert_frame_equal(expected_with_na, gdf.dissolve("a", dropna=False)) assert_frame_equal(expected_no_na, gdf.dissolve("a")) @pytest.mark.skipif( compat.PANDAS_GE_11, reason="dropna warning is only emitted if pandas < 1.1.0" ) def test_dissolve_dropna_warn(nybb_polydf): # No warning with default params with pytest.warns(None) as record: nybb_polydf.dissolve() for r in record: assert "dropna kwarg is not supported" not in str(r.message) # Warning is emitted with non-default dropna value with pytest.warns( UserWarning, match="dropna kwarg is not supported for pandas < 1.1.0" ): nybb_polydf.dissolve(dropna=False) geopandas-0.10.2/geopandas/tests/test_explore.py000066400000000000000000000711351413247402600217370ustar00rootroot00000000000000import geopandas as gpd import numpy as np import pandas as pd import pytest from distutils.version import LooseVersion folium = pytest.importorskip("folium") branca = pytest.importorskip("branca") matplotlib = pytest.importorskip("matplotlib") mapclassify = pytest.importorskip("mapclassify") import matplotlib.cm as cm # noqa import matplotlib.colors as colors # noqa from branca.colormap import StepColormap # noqa BRANCA_05 = str(branca.__version__) > LooseVersion("0.4.2") class TestExplore: def setup_method(self): self.nybb = gpd.read_file(gpd.datasets.get_path("nybb")) self.world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")) self.cities = gpd.read_file(gpd.datasets.get_path("naturalearth_cities")) self.world["range"] = range(len(self.world)) self.missing = self.world.copy() np.random.seed(42) self.missing.loc[np.random.choice(self.missing.index, 40), "continent"] = np.nan self.missing.loc[np.random.choice(self.missing.index, 40), "pop_est"] = np.nan def _fetch_map_string(self, m): out = m._parent.render() out_str = "".join(out.split()) return out_str def test_simple_pass(self): """Make sure default pass""" self.nybb.explore() self.world.explore() self.cities.explore() self.world.geometry.explore() def test_choropleth_pass(self): """Make sure default choropleth pass""" self.world.explore(column="pop_est") def test_map_settings_default(self): """Check default map settings""" m = self.world.explore() assert m.location == [ pytest.approx(-3.1774349999999956, rel=1e-6), pytest.approx(2.842170943040401e-14, rel=1e-6), ] assert m.options["zoom"] == 10 assert m.options["zoomControl"] is True assert m.position == "relative" assert m.height == (100.0, "%") assert m.width == (100.0, "%") assert m.left == (0, "%") assert m.top == (0, "%") assert m.global_switches.no_touch is False assert m.global_switches.disable_3d is False assert "openstreetmap" in m.to_dict()["children"].keys() def test_map_settings_custom(self): """Check custom map settins""" m = self.nybb.explore( zoom_control=False, width=200, height=200, ) assert m.location == [ pytest.approx(40.70582377450201, rel=1e-6), pytest.approx(-73.9778006856748, rel=1e-6), ] assert m.options["zoom"] == 10 assert m.options["zoomControl"] is False assert m.height == (200.0, "px") assert m.width == (200.0, "px") # custom XYZ tiles m = self.nybb.explore( zoom_control=False, width=200, height=200, tiles="https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}", attr="Google", ) out_str = self._fetch_map_string(m) s = '"https://mt1.google.com/vt/lyrs=m\\u0026x={x}\\u0026y={y}\\u0026z={z}"' assert s in out_str assert '"attribution":"Google"' in out_str m = self.nybb.explore(location=(40, 5)) assert m.location == [40, 5] assert m.options["zoom"] == 10 m = self.nybb.explore(zoom_start=8) assert m.location == [ pytest.approx(40.70582377450201, rel=1e-6), pytest.approx(-73.9778006856748, rel=1e-6), ] assert m.options["zoom"] == 8 m = self.nybb.explore(location=(40, 5), zoom_start=8) assert m.location == [40, 5] assert m.options["zoom"] == 8 def test_simple_color(self): """Check color settings""" # single named color m = self.nybb.explore(color="red") out_str = self._fetch_map_string(m) assert '"fillColor":"red"' in out_str # list of colors colors = ["#333333", "#367324", "#95824f", "#fcaa00", "#ffcc33"] m2 = self.nybb.explore(color=colors) out_str = self._fetch_map_string(m2) for c in colors: assert f'"fillColor":"{c}"' in out_str # column of colors df = self.nybb.copy() df["colors"] = colors m3 = df.explore(color="colors") out_str = self._fetch_map_string(m3) for c in colors: assert f'"fillColor":"{c}"' in out_str # line GeoSeries m4 = self.nybb.boundary.explore(color="red") out_str = self._fetch_map_string(m4) assert '"fillColor":"red"' in out_str def test_choropleth_linear(self): """Check choropleth colors""" # default cmap m = self.nybb.explore(column="Shape_Leng") out_str = self._fetch_map_string(m) assert 'color":"#440154"' in out_str assert 'color":"#fde725"' in out_str assert 'color":"#50c46a"' in out_str assert 'color":"#481467"' in out_str assert 'color":"#3d4e8a"' in out_str # named cmap m = self.nybb.explore(column="Shape_Leng", cmap="PuRd") out_str = self._fetch_map_string(m) assert 'color":"#f7f4f9"' in out_str assert 'color":"#67001f"' in out_str assert 'color":"#d31760"' in out_str assert 'color":"#f0ecf5"' in out_str assert 'color":"#d6bedc"' in out_str def test_choropleth_mapclassify(self): """Mapclassify bins""" # quantiles m = self.nybb.explore(column="Shape_Leng", scheme="quantiles") out_str = self._fetch_map_string(m) assert 'color":"#21918c"' in out_str assert 'color":"#3b528b"' in out_str assert 'color":"#5ec962"' in out_str assert 'color":"#fde725"' in out_str assert 'color":"#440154"' in out_str # headtail m = self.world.explore(column="pop_est", scheme="headtailbreaks") out_str = self._fetch_map_string(m) assert '"fillColor":"#3b528b"' in out_str assert '"fillColor":"#21918c"' in out_str assert '"fillColor":"#5ec962"' in out_str assert '"fillColor":"#fde725"' in out_str assert '"fillColor":"#440154"' in out_str # custom k m = self.world.explore(column="pop_est", scheme="naturalbreaks", k=3) out_str = self._fetch_map_string(m) assert '"fillColor":"#21918c"' in out_str assert '"fillColor":"#fde725"' in out_str assert '"fillColor":"#440154"' in out_str def test_categorical(self): """Categorical maps""" # auto detection m = self.world.explore(column="continent") out_str = self._fetch_map_string(m) assert 'color":"#9467bd","continent":"Europe"' in out_str assert 'color":"#c49c94","continent":"NorthAmerica"' in out_str assert 'color":"#1f77b4","continent":"Africa"' in out_str assert 'color":"#98df8a","continent":"Asia"' in out_str assert 'color":"#ff7f0e","continent":"Antarctica"' in out_str assert 'color":"#9edae5","continent":"SouthAmerica"' in out_str assert 'color":"#7f7f7f","continent":"Oceania"' in out_str assert 'color":"#dbdb8d","continent":"Sevenseas(openocean)"' in out_str # forced categorical m = self.nybb.explore(column="BoroCode", categorical=True) out_str = self._fetch_map_string(m) assert 'color":"#9edae5"' in out_str assert 'color":"#c7c7c7"' in out_str assert 'color":"#8c564b"' in out_str assert 'color":"#1f77b4"' in out_str assert 'color":"#98df8a"' in out_str # pandas.Categorical df = self.world.copy() df["categorical"] = pd.Categorical(df["name"]) m = df.explore(column="categorical") out_str = self._fetch_map_string(m) for c in np.apply_along_axis(colors.to_hex, 1, cm.tab20(range(20))): assert f'"fillColor":"{c}"' in out_str # custom cmap m = self.nybb.explore(column="BoroName", cmap="Set1") out_str = self._fetch_map_string(m) assert 'color":"#999999"' in out_str assert 'color":"#a65628"' in out_str assert 'color":"#4daf4a"' in out_str assert 'color":"#e41a1c"' in out_str assert 'color":"#ff7f00"' in out_str # custom list of colors cmap = ["#333432", "#3b6e8c", "#bc5b4f", "#8fa37e", "#efc758"] m = self.nybb.explore(column="BoroName", cmap=cmap) out_str = self._fetch_map_string(m) for c in cmap: assert f'"fillColor":"{c}"' in out_str # shorter list (to make it repeat) cmap = ["#333432", "#3b6e8c"] m = self.nybb.explore(column="BoroName", cmap=cmap) out_str = self._fetch_map_string(m) for c in cmap: assert f'"fillColor":"{c}"' in out_str with pytest.raises(ValueError, match="'cmap' is invalid."): self.nybb.explore(column="BoroName", cmap="nonsense") def test_categories(self): m = self.nybb[["BoroName", "geometry"]].explore( column="BoroName", categories=["Brooklyn", "Staten Island", "Queens", "Bronx", "Manhattan"], ) out_str = self._fetch_map_string(m) assert '"Bronx","__folium_color":"#c7c7c7"' in out_str assert '"Manhattan","__folium_color":"#9edae5"' in out_str assert '"Brooklyn","__folium_color":"#1f77b4"' in out_str assert '"StatenIsland","__folium_color":"#98df8a"' in out_str assert '"Queens","__folium_color":"#8c564b"' in out_str df = self.nybb.copy() df["categorical"] = pd.Categorical(df["BoroName"]) with pytest.raises(ValueError, match="Cannot specify 'categories'"): df.explore("categorical", categories=["Brooklyn", "Staten Island"]) def test_column_values(self): """ Check that the dataframe plot method returns same values with an input string (column in df), pd.Series, or np.array """ column_array = np.array(self.world["pop_est"]) m1 = self.world.explore(column="pop_est") # column name m2 = self.world.explore(column=column_array) # np.array m3 = self.world.explore(column=self.world["pop_est"]) # pd.Series assert m1.location == m2.location == m3.location m1_fields = self.world.explore(column=column_array, tooltip=True, popup=True) out1_fields_str = self._fetch_map_string(m1_fields) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out1_fields_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out1_fields_str ) m2_fields = self.world.explore( column=self.world["pop_est"], tooltip=True, popup=True ) out2_fields_str = self._fetch_map_string(m2_fields) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out2_fields_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out2_fields_str ) # GeoDataframe and the given list have different number of rows with pytest.raises(ValueError, match="different number of rows"): self.world.explore(column=np.array([1, 2, 3])) def test_no_crs(self): """Naive geometry get no tiles""" df = self.world.copy() df.crs = None m = df.explore() assert "openstreetmap" not in m.to_dict()["children"].keys() def test_style_kwds(self): """Style keywords""" m = self.world.explore( style_kwds=dict(fillOpacity=0.1, weight=0.5, fillColor="orange") ) out_str = self._fetch_map_string(m) assert '"fillColor":"orange","fillOpacity":0.1,"weight":0.5' in out_str m = self.world.explore(column="pop_est", style_kwds=dict(color="black")) assert '"color":"black"' in self._fetch_map_string(m) def test_tooltip(self): """Test tooltip""" # default with no tooltip or popup m = self.world.explore() assert "GeoJsonTooltip" in str(m.to_dict()) assert "GeoJsonPopup" not in str(m.to_dict()) # True m = self.world.explore(tooltip=True, popup=True) assert "GeoJsonTooltip" in str(m.to_dict()) assert "GeoJsonPopup" in str(m.to_dict()) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) # True choropleth m = self.world.explore(column="pop_est", tooltip=True, popup=True) assert "GeoJsonTooltip" in str(m.to_dict()) assert "GeoJsonPopup" in str(m.to_dict()) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert ( 'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) # single column m = self.world.explore(tooltip="pop_est", popup="iso_a3") out_str = self._fetch_map_string(m) assert 'fields=["pop_est"]' in out_str assert 'aliases=["pop_est"]' in out_str assert 'fields=["iso_a3"]' in out_str assert 'aliases=["iso_a3"]' in out_str # list m = self.world.explore( tooltip=["pop_est", "continent"], popup=["iso_a3", "gdp_md_est"] ) out_str = self._fetch_map_string(m) assert 'fields=["pop_est","continent"]' in out_str assert 'aliases=["pop_est","continent"]' in out_str assert 'fields=["iso_a3","gdp_md_est"' in out_str assert 'aliases=["iso_a3","gdp_md_est"]' in out_str # number m = self.world.explore(tooltip=2, popup=2) out_str = self._fetch_map_string(m) assert 'fields=["pop_est","continent"]' in out_str assert 'aliases=["pop_est","continent"]' in out_str # keywords tooltip m = self.world.explore( tooltip=True, popup=False, tooltip_kwds=dict(aliases=[0, 1, 2, 3, 4, 5], sticky=False), ) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert "aliases=[0,1,2,3,4,5]" in out_str assert '"sticky":false' in out_str # keywords popup m = self.world.explore( tooltip=False, popup=True, popup_kwds=dict(aliases=[0, 1, 2, 3, 4, 5]), ) out_str = self._fetch_map_string(m) assert ( 'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str ) assert "aliases=[0,1,2,3,4,5]" in out_str assert "${aliases[i]" in out_str # no labels m = self.world.explore( tooltip=True, popup=True, tooltip_kwds=dict(labels=False), popup_kwds=dict(labels=False), ) out_str = self._fetch_map_string(m) assert "${aliases[i]" not in out_str # named index gdf = self.nybb.set_index("BoroName") m = gdf.explore() out_str = self._fetch_map_string(m) assert "BoroName" in out_str def test_default_markers(self): # check overriden default for points m = self.cities.explore() strings = ['"radius":2', '"fill":true', "CircleMarker(latlng,opts)"] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str m = self.cities.explore(marker_kwds=dict(radius=5, fill=False)) strings = ['"radius":5', '"fill":false', "CircleMarker(latlng,opts)"] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str def test_custom_markers(self): # Markers m = self.cities.explore( marker_type="marker", marker_kwds={"icon": folium.Icon(icon="star")}, ) assert ""","icon":"star",""" in self._fetch_map_string(m) # Circle Markers m = self.cities.explore(marker_type="circle", marker_kwds={"fill_color": "red"}) assert ""","fillColor":"red",""" in self._fetch_map_string(m) # Folium Markers m = self.cities.explore( marker_type=folium.Circle( radius=4, fill_color="orange", fill_opacity=0.4, color="black", weight=1 ), ) assert ""","color":"black",""" in self._fetch_map_string(m) # Circle m = self.cities.explore(marker_type="circle_marker", marker_kwds={"radius": 10}) assert ""","radius":10,""" in self._fetch_map_string(m) # Unsupported Markers with pytest.raises( ValueError, match="Only 'marker', 'circle', and 'circle_marker' are supported", ): self.cities.explore(marker_type="dummy") def test_vmin_vmax(self): df = self.world.copy() df["range"] = range(len(df)) m = df.explore("range", vmin=-100, vmax=1000) out_str = self._fetch_map_string(m) assert 'case"176":return{"color":"#3b528b","fillColor":"#3b528b"' in out_str assert 'case"119":return{"color":"#414287","fillColor":"#414287"' in out_str assert 'case"3":return{"color":"#482173","fillColor":"#482173"' in out_str # test 0 df2 = self.nybb.copy() df2["values"] = df2["BoroCode"] * 10.0 m = df2[df2["values"] >= 30].explore("values", vmin=0) out_str = self._fetch_map_string(m) assert 'case"1":return{"color":"#7ad151","fillColor":"#7ad151"' in out_str assert 'case"2":return{"color":"#22a884","fillColor":"#22a884"' in out_str df2["values_negative"] = df2["BoroCode"] * -10.0 m = df2[df2["values_negative"] <= 30].explore("values_negative", vmax=0) out_str = self._fetch_map_string(m) assert 'case"1":return{"color":"#414487","fillColor":"#414487"' in out_str assert 'case"2":return{"color":"#2a788e","fillColor":"#2a788e"' in out_str def test_missing_vals(self): m = self.missing.explore("continent") assert '"fillColor":null' in self._fetch_map_string(m) m = self.missing.explore("pop_est") assert '"fillColor":null' in self._fetch_map_string(m) m = self.missing.explore("pop_est", missing_kwds=dict(color="red")) assert '"fillColor":"red"' in self._fetch_map_string(m) m = self.missing.explore("continent", missing_kwds=dict(color="red")) assert '"fillColor":"red"' in self._fetch_map_string(m) def test_categorical_legend(self): m = self.world.explore("continent", legend=True) out_str = self._fetch_map_string(m) assert "#1f77b4'>Africa" in out_str assert "#ff7f0e'>Antarctica" in out_str assert "#98df8a'>Asia" in out_str assert "#9467bd'>Europe" in out_str assert "#c49c94'>NorthAmerica" in out_str assert "#7f7f7f'>Oceania" in out_str assert "#dbdb8d'>Sevenseas(openocean)" in out_str assert "#9edae5'>SouthAmerica" in out_str m = self.missing.explore( "continent", legend=True, missing_kwds={"color": "red"} ) out_str = self._fetch_map_string(m) assert "red'>NaN" in out_str def test_colorbar(self): m = self.world.explore("range", legend=True) out_str = self._fetch_map_string(m) assert "attr(\"id\",'legend')" in out_str assert "text('range')" in out_str m = self.world.explore( "range", legend=True, legend_kwds=dict(caption="my_caption") ) out_str = self._fetch_map_string(m) assert "attr(\"id\",'legend')" in out_str assert "text('my_caption')" in out_str m = self.missing.explore("pop_est", legend=True, missing_kwds=dict(color="red")) out_str = self._fetch_map_string(m) assert "red'>NaN" in out_str # do not scale legend m = self.world.explore( "pop_est", legend=True, legend_kwds=dict(scale=False), scheme="Headtailbreaks", ) out_str = self._fetch_map_string(m) assert out_str.count("#440154ff") == 100 assert out_str.count("#3b528bff") == 100 assert out_str.count("#21918cff") == 100 assert out_str.count("#5ec962ff") == 100 assert out_str.count("#fde725ff") == 100 # scale legend accorrdingly m = self.world.explore( "pop_est", legend=True, scheme="Headtailbreaks", ) out_str = self._fetch_map_string(m) assert out_str.count("#440154ff") == 16 assert out_str.count("#3b528bff") == 51 assert out_str.count("#21918cff") == 133 assert out_str.count("#5ec962ff") == 282 assert out_str.count("#fde725ff") == 18 # discrete cmap m = self.world.explore("pop_est", legend=True, cmap="Pastel2") out_str = self._fetch_map_string(m) assert out_str.count("b3e2cdff") == 63 assert out_str.count("fdcdacff") == 62 assert out_str.count("cbd5e8ff") == 63 assert out_str.count("f4cae4ff") == 62 assert out_str.count("e6f5c9ff") == 62 assert out_str.count("fff2aeff") == 63 assert out_str.count("f1e2ccff") == 62 assert out_str.count("ccccccff") == 63 @pytest.mark.skipif(not BRANCA_05, reason="requires branca >= 0.5.0") def test_colorbar_max_labels(self): # linear m = self.world.explore("pop_est", legend_kwds=dict(max_labels=3)) out_str = self._fetch_map_string(m) tick_values = [140.0, 465176713.5921569, 930353287.1843138] for tick in tick_values: assert str(tick) in out_str # scheme m = self.world.explore( "pop_est", scheme="headtailbreaks", legend_kwds=dict(max_labels=3) ) out_str = self._fetch_map_string(m) assert "tickValues([140,'',182567501.0,'',1330619341.0,''])" in out_str # short cmap m = self.world.explore("pop_est", legend_kwds=dict(max_labels=3), cmap="tab10") out_str = self._fetch_map_string(m) tick_values = [140.0, 551721192.4, 1103442244.8] for tick in tick_values: assert str(tick) in out_str def test_xyzservices_providers(self): xyzservices = pytest.importorskip("xyzservices") m = self.nybb.explore(tiles=xyzservices.providers.CartoDB.PositronNoLabels) out_str = self._fetch_map_string(m) assert ( '"https://a.basemaps.cartocdn.com/light_nolabels/{z}/{x}/{y}{r}.png"' in out_str ) assert ( 'attribution":"\\u0026copy;\\u003cahref=\\"https://www.openstreetmap.org' in out_str ) assert '"maxNativeZoom":19,"maxZoom":19,"minZoom":0' in out_str def test_xyzservices_query_name(self): pytest.importorskip("xyzservices") m = self.nybb.explore(tiles="CartoDB Positron No Labels") out_str = self._fetch_map_string(m) assert ( '"https://a.basemaps.cartocdn.com/light_nolabels/{z}/{x}/{y}{r}.png"' in out_str ) assert ( 'attribution":"\\u0026copy;\\u003cahref=\\"https://www.openstreetmap.org' in out_str ) assert '"maxNativeZoom":19,"maxZoom":19,"minZoom":0' in out_str def test_linearrings(self): rings = self.nybb.explode(index_parts=True).exterior m = rings.explore() out_str = self._fetch_map_string(m) assert out_str.count("LineString") == len(rings) def test_mapclassify_categorical_legend(self): m = self.missing.explore( column="pop_est", legend=True, scheme="naturalbreaks", missing_kwds=dict(color="red", label="missing"), legend_kwds=dict(colorbar=False, interval=True), ) out_str = self._fetch_map_string(m) strings = [ "[140.00,33986655.00]", "(33986655.00,105350020.00]", "(105350020.00,207353391.00]", "(207353391.00,326625791.00]", "(326625791.00,1379302771.00]", "missing", ] for s in strings: assert s in out_str # interval=False m = self.missing.explore( column="pop_est", legend=True, scheme="naturalbreaks", missing_kwds=dict(color="red", label="missing"), legend_kwds=dict(colorbar=False, interval=False), ) out_str = self._fetch_map_string(m) strings = [ ">140.00,33986655.00", ">33986655.00,105350020.00", ">105350020.00,207353391.00", ">207353391.00,326625791.00", ">326625791.00,1379302771.00", "missing", ] for s in strings: assert s in out_str # custom labels m = self.world.explore( column="pop_est", legend=True, scheme="naturalbreaks", k=5, legend_kwds=dict(colorbar=False, labels=["s", "m", "l", "xl", "xxl"]), ) out_str = self._fetch_map_string(m) strings = [">s<", ">m<", ">l<", ">xl<", ">xxl<"] for s in strings: assert s in out_str # fmt m = self.missing.explore( column="pop_est", legend=True, scheme="naturalbreaks", missing_kwds=dict(color="red", label="missing"), legend_kwds=dict(colorbar=False, fmt="{:.0f}"), ) out_str = self._fetch_map_string(m) strings = [ ">140,33986655", ">33986655,105350020", ">105350020,207353391", ">207353391,326625791", ">326625791,1379302771", "missing", ] for s in strings: assert s in out_str def test_given_m(self): "Check that geometry is mapped onto a given folium.Map" m = folium.Map() self.nybb.explore(m=m, tooltip=False, highlight=False) out_str = self._fetch_map_string(m) assert out_str.count("BoroCode") == 5 # should not change map settings assert m.options["zoom"] == 1 def test_highlight(self): m = self.nybb.explore(highlight=True) out_str = self._fetch_map_string(m) assert '"fillOpacity":0.75' in out_str m = self.nybb.explore( highlight=True, highlight_kwds=dict(fillOpacity=1, color="red") ) out_str = self._fetch_map_string(m) assert '{"color":"red","fillOpacity":1}' in out_str def test_custom_colormaps(self): step = StepColormap(["green", "yellow", "red"], vmin=0, vmax=100000000) m = self.world.explore("pop_est", cmap=step, tooltip=["name"], legend=True) strings = [ 'fillColor":"#008000ff"', # Green '"fillColor":"#ffff00ff"', # Yellow '"fillColor":"#ff0000ff"', # Red ] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str assert out_str.count("008000ff") == 306 assert out_str.count("ffff00ff") == 187 assert out_str.count("ff0000ff") == 190 # Using custom function colormap def my_color_function(field): """Maps low values to green and high values to red.""" if field > 100000000: return "#ff0000" else: return "#008000" m = self.world.explore("pop_est", cmap=my_color_function, legend=False) strings = [ '"color":"#ff0000","fillColor":"#ff0000"', '"color":"#008000","fillColor":"#008000"', ] for s in strings: assert s in self._fetch_map_string(m) # matplotlib.Colormap cmap = colors.ListedColormap(["red", "green", "blue", "white", "black"]) m = self.nybb.explore("BoroName", cmap=cmap) strings = [ '"fillColor":"#ff0000"', # Red '"fillColor":"#008000"', # Green '"fillColor":"#0000ff"', # Blue '"fillColor":"#ffffff"', # White '"fillColor":"#000000"', # Black ] out_str = self._fetch_map_string(m) for s in strings: assert s in out_str def test_multiple_geoseries(self): """ Additional GeoSeries need to be removed as they cannot be converted to GeoJSON """ gdf = self.nybb gdf["boundary"] = gdf.boundary gdf["centroid"] = gdf.centroid gdf.explore() geopandas-0.10.2/geopandas/tests/test_extension_array.py000066400000000000000000000373011413247402600234700ustar00rootroot00000000000000""" This file contains a minimal set of tests for compliance with the extension array interface test suite (by inheriting the pandas test suite), and should contain no other tests. Other tests (eg related to the spatial functionality or integration with GeoSeries/GeoDataFrame) should be added to test_array.py and others. The tests in this file are inherited from the BaseExtensionTests, and only minimal tweaks should be applied to get the tests passing (by overwriting a parent method). A set of fixtures are defined to provide data for the tests (the fixtures expected to be available to pytest by the inherited pandas tests). """ import operator import numpy as np from numpy.testing import assert_array_equal import pandas as pd from pandas.tests.extension import base as extension_tests import shapely.geometry from geopandas.array import GeometryArray, GeometryDtype, from_shapely from geopandas._compat import ignore_shapely2_warnings import pytest # ----------------------------------------------------------------------------- # Compat with extension tests in older pandas versions # ----------------------------------------------------------------------------- not_yet_implemented = pytest.mark.skip(reason="Not yet implemented") no_sorting = pytest.mark.skip(reason="Sorting not supported") # ----------------------------------------------------------------------------- # Required fixtures # ----------------------------------------------------------------------------- @pytest.fixture def dtype(): """A fixture providing the ExtensionDtype to validate.""" return GeometryDtype() def make_data(): a = np.empty(100, dtype=object) with ignore_shapely2_warnings(): a[:] = [shapely.geometry.Point(i, i) for i in range(100)] ga = from_shapely(a) return ga @pytest.fixture def data(): """Length-100 array for this type. * data[0] and data[1] should both be non missing * data[0] and data[1] should not be equal """ return make_data() @pytest.fixture def data_for_twos(): """Length-100 array in which all the elements are two.""" raise NotImplementedError @pytest.fixture def data_missing(): """Length-2 array with [NA, Valid]""" return from_shapely([None, shapely.geometry.Point(1, 1)]) @pytest.fixture(params=["data", "data_missing"]) def all_data(request, data, data_missing): """Parametrized fixture giving 'data' and 'data_missing'""" if request.param == "data": return data elif request.param == "data_missing": return data_missing @pytest.fixture def data_repeated(data): """ Generate many datasets. Parameters ---------- data : fixture implementing `data` Returns ------- Callable[[int], Generator]: A callable that takes a `count` argument and returns a generator yielding `count` datasets. """ def gen(count): for _ in range(count): yield data return gen @pytest.fixture def data_for_sorting(): """Length-3 array with a known sort order. This should be three items [B, C, A] with A < B < C """ raise NotImplementedError @pytest.fixture def data_missing_for_sorting(): """Length-3 array with a known sort order. This should be three items [B, NA, A] with A < B and NA missing. """ raise NotImplementedError @pytest.fixture def na_cmp(): """Binary operator for comparing NA values. Should return a function of two arguments that returns True if both arguments are (scalar) NA for your type. By default, uses ``operator.or`` """ return lambda x, y: x is None and y is None @pytest.fixture def na_value(): """The scalar missing value for this type. Default 'None'""" return None @pytest.fixture def data_for_grouping(): """Data for factorization, grouping, and unique tests. Expected to be like [B, B, NA, NA, A, A, B, C] Where A < B < C and NA is missing """ return from_shapely( [ shapely.geometry.Point(1, 1), shapely.geometry.Point(1, 1), None, None, shapely.geometry.Point(0, 0), shapely.geometry.Point(0, 0), shapely.geometry.Point(1, 1), shapely.geometry.Point(2, 2), ] ) @pytest.fixture(params=[True, False]) def box_in_series(request): """Whether to box the data in a Series""" return request.param @pytest.fixture( params=[ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def groupby_apply_op(request): """ Functions to test groupby.apply(). """ return request.param @pytest.fixture(params=[True, False]) def as_frame(request): """ Boolean fixture to support Series and Series.to_frame() comparison testing. """ return request.param @pytest.fixture(params=[True, False]) def as_series(request): """ Boolean fixture to support arr and Series(arr) comparison testing. """ return request.param @pytest.fixture(params=[True, False]) def use_numpy(request): """ Boolean fixture to support comparison testing of ExtensionDtype array and numpy array. """ return request.param @pytest.fixture(params=["ffill", "bfill"]) def fillna_method(request): """ Parametrized fixture giving method parameters 'ffill' and 'bfill' for Series.fillna(method=) testing. """ return request.param @pytest.fixture(params=[True, False]) def as_array(request): """ Boolean fixture to support ExtensionDtype _from_sequence method testing. """ return request.param # Fixtures defined in pandas/conftest.py that are also needed: defining them # here instead of importing for compatibility @pytest.fixture( params=["sum", "max", "min", "mean", "prod", "std", "var", "median", "kurt", "skew"] ) def all_numeric_reductions(request): """ Fixture for numeric reduction names """ return request.param @pytest.fixture(params=["all", "any"]) def all_boolean_reductions(request): """ Fixture for boolean reduction names """ return request.param # only == and != are support for GeometryArray # @pytest.fixture(params=["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"]) @pytest.fixture(params=["__eq__", "__ne__"]) def all_compare_operators(request): """ Fixture for dunder names for common compare operations * >= * > * == * != * < * <= """ return request.param # ----------------------------------------------------------------------------- # Inherited tests # ----------------------------------------------------------------------------- class TestDtype(extension_tests.BaseDtypeTests): # additional tests def test_array_type_with_arg(self, data, dtype): assert dtype.construct_array_type() is GeometryArray def test_registry(self, data, dtype): s = pd.Series(np.asarray(data), dtype=object) result = s.astype("geometry") assert isinstance(result.array, GeometryArray) expected = pd.Series(data) self.assert_series_equal(result, expected) class TestInterface(extension_tests.BaseInterfaceTests): def test_array_interface(self, data): # we are overriding this base test because the creation of `expected` # potentionally doesn't work for shapely geometries # TODO can be removed with Shapely 2.0 result = np.array(data) assert result[0] == data[0] result = np.array(data, dtype=object) # expected = np.array(list(data), dtype=object) expected = np.empty(len(data), dtype=object) with ignore_shapely2_warnings(): expected[:] = list(data) assert_array_equal(result, expected) def test_contains(self, data, data_missing): # overridden due to the inconsistency between # GeometryDtype.na_value = np.nan # and None being used as NA in array # ensure data without missing values data = data[~data.isna()] # first elements are non-missing assert data[0] in data assert data_missing[0] in data_missing assert None in data_missing assert None not in data assert pd.NaT not in data_missing class TestConstructors(extension_tests.BaseConstructorsTests): pass class TestReshaping(extension_tests.BaseReshapingTests): pass class TestGetitem(extension_tests.BaseGetitemTests): pass class TestSetitem(extension_tests.BaseSetitemTests): pass class TestMissing(extension_tests.BaseMissingTests): def test_fillna_series(self, data_missing): fill_value = data_missing[1] ser = pd.Series(data_missing) result = ser.fillna(fill_value) expected = pd.Series(data_missing._from_sequence([fill_value, fill_value])) self.assert_series_equal(result, expected) # filling with array-like not yet supported # # Fill with a series # result = ser.fillna(expected) # self.assert_series_equal(result, expected) # # Fill with a series not affecting the missing values # result = ser.fillna(ser) # self.assert_series_equal(result, ser) @pytest.mark.skip("fillna method not supported") def test_fillna_limit_pad(self, data_missing): pass @pytest.mark.skip("fillna method not supported") def test_fillna_limit_backfill(self, data_missing): pass @pytest.mark.skip("fillna method not supported") def test_fillna_series_method(self, data_missing, method): pass @pytest.mark.skip("fillna method not supported") def test_fillna_no_op_returns_copy(self, data): pass class TestReduce(extension_tests.BaseNoReduceTests): @pytest.mark.skip("boolean reduce (any/all) tested in test_pandas_methods") def test_reduce_series_boolean(): pass _all_arithmetic_operators = [ "__add__", "__radd__", # '__sub__', '__rsub__', "__mul__", "__rmul__", "__floordiv__", "__rfloordiv__", "__truediv__", "__rtruediv__", "__pow__", "__rpow__", "__mod__", "__rmod__", ] @pytest.fixture(params=_all_arithmetic_operators) def all_arithmetic_operators(request): """ Fixture for dunder names for common arithmetic operations Adapted to exclude __sub__, as this is implemented as "difference". """ return request.param # an inherited test from pandas creates a Series from a list of geometries, which # triggers the warning from Shapely, out of control of GeoPandas, so ignoring here @pytest.mark.filterwarnings( "ignore:The array interface is deprecated and will no longer work in Shapely 2.0" ) class TestArithmeticOps(extension_tests.BaseArithmeticOpsTests): @pytest.mark.skip(reason="not applicable") def test_divmod_series_array(self, data, data_for_twos): pass @pytest.mark.skip(reason="not applicable") def test_add_series_with_extension_array(self, data): pass # an inherited test from pandas creates a Series from a list of geometries, which # triggers the warning from Shapely, out of control of GeoPandas, so ignoring here @pytest.mark.filterwarnings( "ignore:The array interface is deprecated and will no longer work in Shapely 2.0" ) class TestComparisonOps(extension_tests.BaseComparisonOpsTests): def _compare_other(self, s, data, op_name, other): op = getattr(operator, op_name.strip("_")) result = op(s, other) expected = s.combine(other, op) self.assert_series_equal(result, expected) def test_compare_scalar(self, data, all_compare_operators): # noqa op_name = all_compare_operators s = pd.Series(data) self._compare_other(s, data, op_name, data[0]) def test_compare_array(self, data, all_compare_operators): # noqa op_name = all_compare_operators s = pd.Series(data) other = pd.Series([data[0]] * len(data)) self._compare_other(s, data, op_name, other) class TestMethods(extension_tests.BaseMethodsTests): @no_sorting @pytest.mark.parametrize("dropna", [True, False]) def test_value_counts(self, all_data, dropna): pass @no_sorting def test_value_counts_with_normalize(self, data): pass @no_sorting def test_argsort(self, data_for_sorting): result = pd.Series(data_for_sorting).argsort() expected = pd.Series(np.array([2, 0, 1], dtype=np.int64)) self.assert_series_equal(result, expected) @no_sorting def test_argsort_missing(self, data_missing_for_sorting): result = pd.Series(data_missing_for_sorting).argsort() expected = pd.Series(np.array([1, -1, 0], dtype=np.int64)) self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values(self, data_for_sorting, ascending): ser = pd.Series(data_for_sorting) result = ser.sort_values(ascending=ascending) expected = ser.iloc[[2, 0, 1]] if not ascending: expected = expected[::-1] self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_missing(self, data_missing_for_sorting, ascending): ser = pd.Series(data_missing_for_sorting) result = ser.sort_values(ascending=ascending) if ascending: expected = ser.iloc[[2, 0, 1]] else: expected = ser.iloc[[0, 2, 1]] self.assert_series_equal(result, expected) @no_sorting @pytest.mark.parametrize("ascending", [True, False]) def test_sort_values_frame(self, data_for_sorting, ascending): df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting}) result = df.sort_values(["A", "B"]) expected = pd.DataFrame( {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1] ) self.assert_frame_equal(result, expected) @no_sorting def test_searchsorted(self, data_for_sorting, as_series): pass @not_yet_implemented def test_combine_le(self): pass @pytest.mark.skip(reason="addition not supported") def test_combine_add(self): pass @not_yet_implemented def test_fillna_length_mismatch(self, data_missing): msg = "Length of 'value' does not match." with pytest.raises(ValueError, match=msg): data_missing.fillna(data_missing.take([1])) @no_sorting def test_nargsort(self): pass @no_sorting def test_argsort_missing_array(self): pass @no_sorting def test_argmin_argmax(self): pass @no_sorting def test_argmin_argmax_empty_array(self): pass @no_sorting def test_argmin_argmax_all_na(self): pass @no_sorting def test_argreduce_series(self): pass @no_sorting def test_argmax_argmin_no_skipna_notimplemented(self): pass class TestCasting(extension_tests.BaseCastingTests): pass class TestGroupby(extension_tests.BaseGroupbyTests): @no_sorting @pytest.mark.parametrize("as_index", [True, False]) def test_groupby_extension_agg(self, as_index, data_for_grouping): pass @no_sorting def test_groupby_extension_transform(self, data_for_grouping): pass @no_sorting @pytest.mark.parametrize( "op", [ lambda x: 1, lambda x: [1] * len(x), lambda x: pd.Series([1] * len(x)), lambda x: x, ], ids=["scalar", "list", "series", "object"], ) def test_groupby_extension_apply(self, data_for_grouping, op): pass class TestPrinting(extension_tests.BasePrintingTests): pass @not_yet_implemented class TestParsing(extension_tests.BaseParsingTests): pass geopandas-0.10.2/geopandas/tests/test_geocode.py000066400000000000000000000122621413247402600216620ustar00rootroot00000000000000import pandas as pd from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries from geopandas.tools import geocode, reverse_geocode from geopandas.tools.geocoding import _prepare_geocode_result from geopandas.tests.util import assert_geoseries_equal, mock from pandas.testing import assert_series_equal from geopandas.testing import assert_geodataframe_equal import pytest geopy = pytest.importorskip("geopy") class ForwardMock(mock.MagicMock): """ Mock the forward geocoding function. Returns the passed in address and (p, p+.5) where p increases at each call """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._n = 0.0 def __call__(self, *args, **kwargs): self.return_value = args[0], (self._n, self._n + 0.5) self._n += 1 return super().__call__(*args, **kwargs) class ReverseMock(mock.MagicMock): """ Mock the reverse geocoding function. Returns the passed in point and 'address{p}' where p increases at each call """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._n = 0 def __call__(self, *args, **kwargs): self.return_value = "address{0}".format(self._n), args[0] self._n += 1 return super().__call__(*args, **kwargs) @pytest.fixture def locations(): locations = ["260 Broadway, New York, NY", "77 Massachusetts Ave, Cambridge, MA"] return locations @pytest.fixture def points(): points = [Point(-71.0597732, 42.3584308), Point(-77.0365305, 38.8977332)] return points def test_prepare_result(): # Calls _prepare_result with sample results from the geocoder call # loop p0 = Point(12.3, -45.6) # Treat these as lat/lon p1 = Point(-23.4, 56.7) d = {"a": ("address0", p0.coords[0]), "b": ("address1", p1.coords[0])} df = _prepare_geocode_result(d) assert type(df) is GeoDataFrame assert df.crs == "EPSG:4326" assert len(df) == 2 assert "address" in df coords = df.loc["a"]["geometry"].coords[0] test = p0.coords[0] # Output from the df should be lon/lat assert coords[0] == pytest.approx(test[1]) assert coords[1] == pytest.approx(test[0]) coords = df.loc["b"]["geometry"].coords[0] test = p1.coords[0] assert coords[0] == pytest.approx(test[1]) assert coords[1] == pytest.approx(test[0]) def test_prepare_result_none(): p0 = Point(12.3, -45.6) # Treat these as lat/lon d = {"a": ("address0", p0.coords[0]), "b": (None, None)} df = _prepare_geocode_result(d) assert type(df) is GeoDataFrame assert df.crs == "EPSG:4326" assert len(df) == 2 assert "address" in df row = df.loc["b"] # The shapely.geometry.Point() is actually a GeometryCollection, and thus # gets converted to that in conversion to pygeos. When converting back # on access, you now get a GeometryCollection object instead of Point, # which has no coords # see https://github.com/Toblerity/Shapely/issues/742/#issuecomment-545296708 # TODO we should probably replace this with a missing value instead of point? # assert len(row["geometry"].coords) == 0 assert row["geometry"].is_empty assert row["address"] is None @pytest.mark.parametrize("geocode_result", (None, (None, None))) def test_prepare_geocode_result_when_result_is(geocode_result): result = {0: geocode_result} expected_output = GeoDataFrame( {"geometry": [Point()], "address": [None]}, crs="EPSG:4326", ) output = _prepare_geocode_result(result) assert_geodataframe_equal(output, expected_output) def test_bad_provider_forward(): from geopy.exc import GeocoderNotFound with pytest.raises(GeocoderNotFound): geocode(["cambridge, ma"], "badprovider") def test_bad_provider_reverse(): from geopy.exc import GeocoderNotFound with pytest.raises(GeocoderNotFound): reverse_geocode([Point(0, 0)], "badprovider") def test_forward(locations, points): from geopy.geocoders import Photon for provider in ["photon", Photon]: with mock.patch("geopy.geocoders.Photon.geocode", ForwardMock()) as m: g = geocode(locations, provider=provider, timeout=2) assert len(locations) == m.call_count n = len(locations) assert isinstance(g, GeoDataFrame) expected = GeoSeries( [Point(float(x) + 0.5, float(x)) for x in range(n)], crs="EPSG:4326" ) assert_geoseries_equal(expected, g["geometry"]) assert_series_equal(g["address"], pd.Series(locations, name="address")) def test_reverse(locations, points): from geopy.geocoders import Photon for provider in ["photon", Photon]: with mock.patch("geopy.geocoders.Photon.reverse", ReverseMock()) as m: g = reverse_geocode(points, provider=provider, timeout=2) assert len(points) == m.call_count assert isinstance(g, GeoDataFrame) expected = GeoSeries(points, crs="EPSG:4326") assert_geoseries_equal(expected, g["geometry"]) address = pd.Series( ["address" + str(x) for x in range(len(points))], name="address" ) assert_series_equal(g["address"], address) geopandas-0.10.2/geopandas/tests/test_geodataframe.py000066400000000000000000001203541413247402600226760ustar00rootroot00000000000000import json import os import shutil import tempfile from distutils.version import LooseVersion import numpy as np import pandas as pd import pyproj from pyproj import CRS from pyproj.exceptions import CRSError from shapely.geometry import Point, Polygon import geopandas import geopandas._compat as compat from geopandas import GeoDataFrame, GeoSeries, read_file from geopandas.array import GeometryArray, GeometryDtype, from_shapely from geopandas._compat import ignore_shapely2_warnings from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from geopandas.tests.util import PACKAGE_DIR, validate_boro_df from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal import pytest PYPROJ_LT_3 = LooseVersion(pyproj.__version__) < LooseVersion("3") TEST_NEAREST = compat.PYGEOS_GE_010 and compat.USE_PYGEOS pandas_133 = pd.__version__ == LooseVersion("1.3.3") @pytest.fixture def dfs(request): s1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}) df2 = GeoDataFrame({"col2": [1, 2], "geometry": s2}) return df1, df2 @pytest.fixture( params=["union", "intersection", "difference", "symmetric_difference", "identity"] ) def how(request): if pandas_133 and request.param in ["symmetric_difference", "identity", "union"]: pytest.xfail("Regression in pandas 1.3.3 (GH #2101)") return request.param class TestDataFrame: def setup_method(self): N = 10 nybb_filename = geopandas.datasets.get_path("nybb") self.df = read_file(nybb_filename) self.tempdir = tempfile.mkdtemp() self.crs = "epsg:4326" self.df2 = GeoDataFrame( [ {"geometry": Point(x, y), "value1": x + y, "value2": x * y} for x, y in zip(range(N), range(N)) ], crs=self.crs, ) self.df3 = read_file( os.path.join(PACKAGE_DIR, "geopandas", "tests", "data", "null_geom.geojson") ) def teardown_method(self): shutil.rmtree(self.tempdir) def test_df_init(self): assert type(self.df2) is GeoDataFrame assert self.df2.crs == self.crs def test_different_geo_colname(self): data = { "A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data, crs=self.crs, geometry="location") locs = GeoSeries(data["location"], crs=self.crs) assert_geoseries_equal(df.geometry, locs) assert "geometry" not in df assert df.geometry.name == "location" # internal implementation detail assert df._geometry_column_name == "location" geom2 = [Point(x, y) for x, y in zip(range(5, 10), range(5))] with pytest.raises(CRSError): df.set_geometry(geom2, crs="dummy_crs") @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS") def test_geo_getitem(self): data = { "A": range(5), "B": range(-5, 0), "location": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data, crs=self.crs, geometry="location") assert isinstance(df.geometry, GeoSeries) df["geometry"] = df["A"] assert isinstance(df.geometry, GeoSeries) assert df.geometry[0] == data["location"][0] # good if this changed in the future assert not isinstance(df["geometry"], GeoSeries) assert isinstance(df["location"], GeoSeries) df["buff"] = df.buffer(1) assert isinstance(df["buff"], GeoSeries) df["array"] = from_shapely([Point(x, y) for x, y in zip(range(5), range(5))]) assert isinstance(df["array"], GeoSeries) data["geometry"] = [Point(x + 1, y - 1) for x, y in zip(range(5), range(5))] df = GeoDataFrame(data, crs=self.crs) assert isinstance(df.geometry, GeoSeries) assert isinstance(df["geometry"], GeoSeries) # good if this changed in the future assert not isinstance(df["location"], GeoSeries) def test_getitem_no_geometry(self): res = self.df2[["value1", "value2"]] assert isinstance(res, pd.DataFrame) assert not isinstance(res, GeoDataFrame) # with different name df = self.df2.copy() df = df.rename(columns={"geometry": "geom"}).set_geometry("geom") assert isinstance(df, GeoDataFrame) res = df[["value1", "value2"]] assert isinstance(res, pd.DataFrame) assert not isinstance(res, GeoDataFrame) df["geometry"] = np.arange(len(df)) res = df[["value1", "value2", "geometry"]] assert isinstance(res, pd.DataFrame) assert not isinstance(res, GeoDataFrame) def test_geo_setitem(self): data = { "A": range(5), "B": np.arange(5.0), "geometry": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data) s = GeoSeries([Point(x, y + 1) for x, y in zip(range(5), range(5))]) # setting geometry column for vals in [s, s.values]: df["geometry"] = vals assert_geoseries_equal(df["geometry"], s) assert_geoseries_equal(df.geometry, s) # non-aligned values s2 = GeoSeries([Point(x, y + 1) for x, y in zip(range(6), range(6))]) df["geometry"] = s2 assert_geoseries_equal(df["geometry"], s) assert_geoseries_equal(df.geometry, s) # setting other column with geometry values -> preserve geometry type for vals in [s, s.values]: df["other_geom"] = vals assert isinstance(df["other_geom"].values, GeometryArray) # overwriting existing non-geometry column -> preserve geometry type data = { "A": range(5), "B": np.arange(5.0), "other_geom": range(5), "geometry": [Point(x, y) for x, y in zip(range(5), range(5))], } df = GeoDataFrame(data) for vals in [s, s.values]: df["other_geom"] = vals assert isinstance(df["other_geom"].values, GeometryArray) def test_geometry_property(self): assert_geoseries_equal( self.df.geometry, self.df["geometry"], check_dtype=True, check_index_type=True, ) df = self.df.copy() new_geom = [ Point(x, y) for x, y in zip(range(len(self.df)), range(len(self.df))) ] df.geometry = new_geom new_geom = GeoSeries(new_geom, index=df.index, crs=df.crs) assert_geoseries_equal(df.geometry, new_geom) assert_geoseries_equal(df["geometry"], new_geom) # new crs gs = new_geom.to_crs(crs="epsg:3857") df.geometry = gs assert df.crs == "epsg:3857" def test_geometry_property_errors(self): with pytest.raises(AttributeError): df = self.df.copy() del df["geometry"] df.geometry # list-like error with pytest.raises(ValueError): df = self.df2.copy() df.geometry = "value1" # list-like error with pytest.raises(ValueError): df = self.df.copy() df.geometry = "apple" # non-geometry error with pytest.raises(TypeError): df = self.df.copy() df.geometry = list(range(df.shape[0])) with pytest.raises(KeyError): df = self.df.copy() del df["geometry"] df["geometry"] # ndim error with pytest.raises(ValueError): df = self.df.copy() df.geometry = df def test_rename_geometry(self): assert self.df.geometry.name == "geometry" df2 = self.df.rename_geometry("new_name") assert df2.geometry.name == "new_name" df2 = self.df.rename_geometry("new_name", inplace=True) assert df2 is None assert self.df.geometry.name == "new_name" # existing column error msg = "Column named Shape_Area already exists" with pytest.raises(ValueError, match=msg): df2 = self.df.rename_geometry("Shape_Area") with pytest.raises(ValueError, match=msg): self.df.rename_geometry("Shape_Area", inplace=True) def test_set_geometry(self): geom = GeoSeries([Point(x, y) for x, y in zip(range(5), range(5))]) original_geom = self.df.geometry df2 = self.df.set_geometry(geom) assert self.df is not df2 assert_geoseries_equal(df2.geometry, geom, check_crs=False) assert_geoseries_equal(self.df.geometry, original_geom) assert_geoseries_equal(self.df["geometry"], self.df.geometry) # unknown column with pytest.raises(ValueError): self.df.set_geometry("nonexistent-column") # ndim error with pytest.raises(ValueError): self.df.set_geometry(self.df) # new crs - setting should default to GeoSeries' crs gs = GeoSeries(geom, crs="epsg:3857") new_df = self.df.set_geometry(gs) assert new_df.crs == "epsg:3857" # explicit crs overrides self and dataframe new_df = self.df.set_geometry(gs, crs="epsg:26909") assert new_df.crs == "epsg:26909" assert new_df.geometry.crs == "epsg:26909" # Series should use dataframe's new_df = self.df.set_geometry(geom.values) assert new_df.crs == self.df.crs assert new_df.geometry.crs == self.df.crs def test_set_geometry_col(self): g = self.df.geometry g_simplified = g.simplify(100) self.df["simplified_geometry"] = g_simplified df2 = self.df.set_geometry("simplified_geometry") # Drop is false by default assert "simplified_geometry" in df2 assert_geoseries_equal(df2.geometry, g_simplified) # If True, drops column and renames to geometry df3 = self.df.set_geometry("simplified_geometry", drop=True) assert "simplified_geometry" not in df3 assert_geoseries_equal(df3.geometry, g_simplified) def test_set_geometry_inplace(self): geom = [Point(x, y) for x, y in zip(range(5), range(5))] ret = self.df.set_geometry(geom, inplace=True) assert ret is None geom = GeoSeries(geom, index=self.df.index, crs=self.df.crs) assert_geoseries_equal(self.df.geometry, geom) def test_set_geometry_series(self): # Test when setting geometry with a Series that # alignment will occur # # Reverse the index order # Set the Series to be Point(i,i) where i is the index self.df.index = range(len(self.df) - 1, -1, -1) d = {} for i in range(len(self.df)): d[i] = Point(i, i) g = GeoSeries(d) # At this point, the DataFrame index is [4,3,2,1,0] and the # GeoSeries index is [0,1,2,3,4]. Make sure set_geometry aligns # them to match indexes df = self.df.set_geometry(g) for i, r in df.iterrows(): assert i == r["geometry"].x assert i == r["geometry"].y def test_set_geometry_empty(self): df = pd.DataFrame(columns=["a", "geometry"], index=pd.DatetimeIndex([])) result = df.set_geometry("geometry") assert isinstance(result, GeoDataFrame) assert isinstance(result.index, pd.DatetimeIndex) def test_align(self): df = self.df2 res1, res2 = df.align(df) assert_geodataframe_equal(res1, df) assert_geodataframe_equal(res2, df) res1, res2 = df.align(df.copy()) assert_geodataframe_equal(res1, df) assert_geodataframe_equal(res2, df) # assert crs is / is not preserved on mixed dataframes df_nocrs = df.copy() df_nocrs.crs = None res1, res2 = df.align(df_nocrs) assert_geodataframe_equal(res1, df) assert res1.crs is not None assert_geodataframe_equal(res2, df_nocrs) assert res2.crs is None # mixed GeoDataFrame / DataFrame df_nogeom = pd.DataFrame(df.drop("geometry", axis=1)) res1, res2 = df.align(df_nogeom, axis=0) assert_geodataframe_equal(res1, df) assert type(res2) == pd.DataFrame assert_frame_equal(res2, df_nogeom) # same as above but now with actual alignment df1 = df.iloc[1:].copy() df2 = df.iloc[:-1].copy() exp1 = df.copy() exp1.iloc[0] = np.nan exp2 = df.copy() exp2.iloc[-1] = np.nan res1, res2 = df1.align(df2) assert_geodataframe_equal(res1, exp1) assert_geodataframe_equal(res2, exp2) df2_nocrs = df2.copy() df2_nocrs.crs = None exp2_nocrs = exp2.copy() exp2_nocrs.crs = None res1, res2 = df1.align(df2_nocrs) assert_geodataframe_equal(res1, exp1) assert res1.crs is not None assert_geodataframe_equal(res2, exp2_nocrs) assert res2.crs is None df2_nogeom = pd.DataFrame(df2.drop("geometry", axis=1)) exp2_nogeom = pd.DataFrame(exp2.drop("geometry", axis=1)) res1, res2 = df1.align(df2_nogeom, axis=0) assert_geodataframe_equal(res1, exp1) assert type(res2) == pd.DataFrame assert_frame_equal(res2, exp2_nogeom) def test_to_json(self): text = self.df.to_json() data = json.loads(text) assert data["type"] == "FeatureCollection" assert len(data["features"]) == 5 assert "id" in data["features"][0].keys() @pytest.mark.filterwarnings( "ignore:Geometry column does not contain geometry:UserWarning" ) def test_to_json_geom_col(self): df = self.df.copy() df["geom"] = df["geometry"] df["geometry"] = np.arange(len(df)) df.set_geometry("geom", inplace=True) text = df.to_json() data = json.loads(text) assert data["type"] == "FeatureCollection" assert len(data["features"]) == 5 def test_to_json_only_geom_column(self): text = self.df[["geometry"]].to_json() data = json.loads(text) assert len(data["features"]) == 5 assert "id" in data["features"][0].keys() def test_to_json_na(self): # Set a value as nan and make sure it's written self.df.loc[self.df["BoroName"] == "Queens", "Shape_Area"] = np.nan text = self.df.to_json() data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: props = f["properties"] assert len(props) == 4 if props["BoroName"] == "Queens": assert props["Shape_Area"] is None def test_to_json_bad_na(self): # Check that a bad na argument raises error with pytest.raises(ValueError): self.df.to_json(na="garbage") def test_to_json_dropna(self): self.df.loc[self.df["BoroName"] == "Queens", "Shape_Area"] = np.nan self.df.loc[self.df["BoroName"] == "Bronx", "Shape_Leng"] = np.nan text = self.df.to_json(na="drop") data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: props = f["properties"] if props["BoroName"] == "Queens": assert len(props) == 3 assert "Shape_Area" not in props # Just make sure setting it to nan in a different row # doesn't affect this one assert "Shape_Leng" in props elif props["BoroName"] == "Bronx": assert len(props) == 3 assert "Shape_Leng" not in props assert "Shape_Area" in props else: assert len(props) == 4 def test_to_json_keepna(self): self.df.loc[self.df["BoroName"] == "Queens", "Shape_Area"] = np.nan self.df.loc[self.df["BoroName"] == "Bronx", "Shape_Leng"] = np.nan text = self.df.to_json(na="keep") data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: props = f["properties"] assert len(props) == 4 if props["BoroName"] == "Queens": assert np.isnan(props["Shape_Area"]) # Just make sure setting it to nan in a different row # doesn't affect this one assert "Shape_Leng" in props elif props["BoroName"] == "Bronx": assert np.isnan(props["Shape_Leng"]) assert "Shape_Area" in props def test_to_json_drop_id(self): text = self.df.to_json(drop_id=True) data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: assert "id" not in f.keys() def test_to_json_drop_id_only_geom_column(self): text = self.df[["geometry"]].to_json(drop_id=True) data = json.loads(text) assert len(data["features"]) == 5 for f in data["features"]: assert "id" not in f.keys() def test_to_json_with_duplicate_columns(self): df = GeoDataFrame( data=[[1, 2, 3]], columns=["a", "b", "a"], geometry=[Point(1, 1)] ) with pytest.raises( ValueError, match="GeoDataFrame cannot contain duplicated column names." ): df.to_json() def test_copy(self): df2 = self.df.copy() assert type(df2) is GeoDataFrame assert self.df.crs == df2.crs def test_to_file_crs(self): """ Ensure that the file is written according to the crs if it is specified """ tempfilename = os.path.join(self.tempdir, "crs.shp") # save correct CRS self.df.to_file(tempfilename) df = GeoDataFrame.from_file(tempfilename) assert df.crs == self.df.crs # overwrite CRS self.df.to_file(tempfilename, crs=3857) df = GeoDataFrame.from_file(tempfilename) assert df.crs == "epsg:3857" # specify CRS for gdf without one df2 = self.df.copy() df2.crs = None df2.to_file(tempfilename, crs=2263) df = GeoDataFrame.from_file(tempfilename) assert df.crs == "epsg:2263" def test_to_file_with_duplicate_columns(self): df = GeoDataFrame( data=[[1, 2, 3]], columns=["a", "b", "a"], geometry=[Point(1, 1)] ) with pytest.raises( ValueError, match="GeoDataFrame cannot contain duplicated column names." ): tempfilename = os.path.join(self.tempdir, "crs.shp") df.to_file(tempfilename) def test_bool_index(self): # Find boros with 'B' in their name df = self.df[self.df["BoroName"].str.contains("B")] assert len(df) == 2 boros = df["BoroName"].values assert "Brooklyn" in boros assert "Bronx" in boros assert type(df) is GeoDataFrame def test_coord_slice_points(self): assert self.df2.cx[-2:-1, -2:-1].empty assert_frame_equal(self.df2, self.df2.cx[:, :]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[5:, :]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[:, 5:]) assert_frame_equal(self.df2.loc[5:], self.df2.cx[5:, 5:]) def test_from_dict(self): data = {"A": [1], "geometry": [Point(0.0, 0.0)]} df = GeoDataFrame.from_dict(data, crs=3857) assert df.crs == "epsg:3857" assert df._geometry_column_name == "geometry" data = {"B": [1], "location": [Point(0.0, 0.0)]} df = GeoDataFrame.from_dict(data, geometry="location") assert df._geometry_column_name == "location" def test_from_features(self): fiona = pytest.importorskip("fiona") nybb_filename = geopandas.datasets.get_path("nybb") with fiona.open(nybb_filename) as f: features = list(f) crs = f.crs_wkt df = GeoDataFrame.from_features(features, crs=crs) validate_boro_df(df, case_sensitive=True) assert df.crs == crs def test_from_features_unaligned_properties(self): p1 = Point(1, 1) f1 = { "type": "Feature", "properties": {"a": 0}, "geometry": p1.__geo_interface__, } p2 = Point(2, 2) f2 = { "type": "Feature", "properties": {"b": 1}, "geometry": p2.__geo_interface__, } p3 = Point(3, 3) f3 = { "type": "Feature", "properties": {"a": 2}, "geometry": p3.__geo_interface__, } df = GeoDataFrame.from_features([f1, f2, f3]) result = df[["a", "b"]] expected = pd.DataFrame.from_dict( [{"a": 0, "b": np.nan}, {"a": np.nan, "b": 1}, {"a": 2, "b": np.nan}] ) assert_frame_equal(expected, result) def test_from_features_geom_interface_feature(self): class Placemark(object): def __init__(self, geom, val): self.__geo_interface__ = { "type": "Feature", "properties": {"a": val}, "geometry": geom.__geo_interface__, } p1 = Point(1, 1) f1 = Placemark(p1, 0) p2 = Point(3, 3) f2 = Placemark(p2, 0) df = GeoDataFrame.from_features([f1, f2]) assert sorted(df.columns) == ["a", "geometry"] assert df.geometry.tolist() == [p1, p2] def test_from_feature_collection(self): data = { "name": ["a", "b", "c"], "lat": [45, 46, 47.5], "lon": [-120, -121.2, -122.9], } df = pd.DataFrame(data) geometry = [Point(xy) for xy in zip(df["lon"], df["lat"])] gdf = GeoDataFrame(df, geometry=geometry) # from_features returns sorted columns expected = gdf[["geometry", "lat", "lon", "name"]] # test FeatureCollection res = GeoDataFrame.from_features(gdf.__geo_interface__) assert_frame_equal(res, expected) # test list of Features res = GeoDataFrame.from_features(gdf.__geo_interface__["features"]) assert_frame_equal(res, expected) # test __geo_interface__ attribute (a GeoDataFrame has one) res = GeoDataFrame.from_features(gdf) assert_frame_equal(res, expected) def test_dataframe_to_geodataframe(self): df = pd.DataFrame( {"A": range(len(self.df)), "location": np.array(self.df.geometry)}, index=self.df.index, ) gf = df.set_geometry("location", crs=self.df.crs) assert isinstance(df, pd.DataFrame) assert isinstance(gf, GeoDataFrame) assert_geoseries_equal(gf.geometry, self.df.geometry) assert gf.geometry.name == "location" assert "geometry" not in gf gf2 = df.set_geometry("location", crs=self.df.crs, drop=True) assert isinstance(df, pd.DataFrame) assert isinstance(gf2, GeoDataFrame) assert gf2.geometry.name == "geometry" assert "geometry" in gf2 assert "location" not in gf2 assert "location" in df # should be a copy df.loc[0, "A"] = 100 assert gf.loc[0, "A"] == 0 assert gf2.loc[0, "A"] == 0 with pytest.raises(ValueError): df.set_geometry("location", inplace=True) def test_geodataframe_geointerface(self): assert self.df.__geo_interface__["type"] == "FeatureCollection" assert len(self.df.__geo_interface__["features"]) == self.df.shape[0] def test_geodataframe_iterfeatures(self): df = self.df.iloc[:1].copy() df.loc[0, "BoroName"] = np.nan # when containing missing values # null: output the missing entries as JSON null result = list(df.iterfeatures(na="null"))[0]["properties"] assert result["BoroName"] is None # drop: remove the property from the feature. result = list(df.iterfeatures(na="drop"))[0]["properties"] assert "BoroName" not in result.keys() # keep: output the missing entries as NaN result = list(df.iterfeatures(na="keep"))[0]["properties"] assert np.isnan(result["BoroName"]) # test for checking that the (non-null) features are python scalars and # not numpy scalars assert type(df.loc[0, "Shape_Leng"]) is np.float64 # null result = list(df.iterfeatures(na="null"))[0] assert type(result["properties"]["Shape_Leng"]) is float # drop result = list(df.iterfeatures(na="drop"))[0] assert type(result["properties"]["Shape_Leng"]) is float # keep result = list(df.iterfeatures(na="keep"))[0] assert type(result["properties"]["Shape_Leng"]) is float # when only having numerical columns df_only_numerical_cols = df[["Shape_Leng", "Shape_Area", "geometry"]] assert type(df_only_numerical_cols.loc[0, "Shape_Leng"]) is np.float64 # null result = list(df_only_numerical_cols.iterfeatures(na="null"))[0] assert type(result["properties"]["Shape_Leng"]) is float # drop result = list(df_only_numerical_cols.iterfeatures(na="drop"))[0] assert type(result["properties"]["Shape_Leng"]) is float # keep result = list(df_only_numerical_cols.iterfeatures(na="keep"))[0] assert type(result["properties"]["Shape_Leng"]) is float with pytest.raises( ValueError, match="GeoDataFrame cannot contain duplicated column names." ): df_with_duplicate_columns = df[ ["Shape_Leng", "Shape_Leng", "Shape_Area", "geometry"] ] list(df_with_duplicate_columns.iterfeatures()) # geometry not set df = GeoDataFrame({"values": [0, 1], "geom": [Point(0, 1), Point(1, 0)]}) with pytest.raises(AttributeError): list(df.iterfeatures()) def test_geodataframe_geojson_no_bbox(self): geo = self.df._to_geo(na="null", show_bbox=False) assert "bbox" not in geo.keys() for feature in geo["features"]: assert "bbox" not in feature.keys() def test_geodataframe_geojson_bbox(self): geo = self.df._to_geo(na="null", show_bbox=True) assert "bbox" in geo.keys() assert len(geo["bbox"]) == 4 assert isinstance(geo["bbox"], tuple) for feature in geo["features"]: assert "bbox" in feature.keys() def test_pickle(self): import pickle df2 = pickle.loads(pickle.dumps(self.df)) assert_geodataframe_equal(self.df, df2) def test_pickle_method(self): filename = os.path.join(self.tempdir, "df.pkl") self.df.to_pickle(filename) unpickled = pd.read_pickle(filename) assert_frame_equal(self.df, unpickled) assert self.df.crs == unpickled.crs def test_estimate_utm_crs(self): if PYPROJ_LT_3: with pytest.raises(RuntimeError, match=r"pyproj 3\+ required"): self.df.estimate_utm_crs() else: assert self.df.estimate_utm_crs() == CRS("EPSG:32618") assert self.df.estimate_utm_crs("NAD83") == CRS("EPSG:26918") def test_to_wkb(self): wkbs0 = [ ( b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" ), # POINT (0 0) ( b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?" ), # POINT (1 1) ] wkbs1 = [ ( b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\x00@\x00\x00\x00\x00\x00\x00\x00@" ), # POINT (2 2) ( b"\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00" b"\x00\x08@\x00\x00\x00\x00\x00\x00\x08@" ), # POINT (3 3) ] gs0 = GeoSeries.from_wkb(wkbs0) gs1 = GeoSeries.from_wkb(wkbs1) gdf = GeoDataFrame({"geom_col0": gs0, "geom_col1": gs1}) expected_df = pd.DataFrame({"geom_col0": wkbs0, "geom_col1": wkbs1}) assert_frame_equal(expected_df, gdf.to_wkb()) def test_to_wkt(self): wkts0 = ["POINT (0 0)", "POINT (1 1)"] wkts1 = ["POINT (2 2)", "POINT (3 3)"] gs0 = GeoSeries.from_wkt(wkts0) gs1 = GeoSeries.from_wkt(wkts1) gdf = GeoDataFrame({"gs0": gs0, "gs1": gs1}) expected_df = pd.DataFrame({"gs0": wkts0, "gs1": wkts1}) assert_frame_equal(expected_df, gdf.to_wkt()) @pytest.mark.parametrize("how", ["left", "inner", "right"]) @pytest.mark.parametrize("predicate", ["intersects", "within", "contains"]) @pytest.mark.skipif( not compat.USE_PYGEOS and not compat.HAS_RTREE, reason="sjoin needs `rtree` or `pygeos` dependency", ) def test_sjoin(self, how, predicate): """ Basic test for availability of the GeoDataFrame method. Other sjoin tests are located in /tools/tests/test_sjoin.py """ left = read_file(geopandas.datasets.get_path("naturalearth_cities")) right = read_file(geopandas.datasets.get_path("naturalearth_lowres")) expected = geopandas.sjoin(left, right, how=how, predicate=predicate) result = left.sjoin(right, how=how, predicate=predicate) assert_geodataframe_equal(result, expected) @pytest.mark.parametrize("how", ["left", "inner", "right"]) @pytest.mark.parametrize("max_distance", [None, 1]) @pytest.mark.parametrize("distance_col", [None, "distance"]) @pytest.mark.skipif( not TEST_NEAREST, reason=( "PyGEOS >= 0.10.0" " must be installed and activated via the geopandas.compat module to" " test sjoin_nearest" ), ) def test_sjoin_nearest(self, how, max_distance, distance_col): """ Basic test for availability of the GeoDataFrame method. Other sjoin tests are located in /tools/tests/test_sjoin.py """ left = read_file(geopandas.datasets.get_path("naturalearth_cities")) right = read_file(geopandas.datasets.get_path("naturalearth_lowres")) expected = geopandas.sjoin_nearest( left, right, how=how, max_distance=max_distance, distance_col=distance_col ) result = left.sjoin_nearest( right, how=how, max_distance=max_distance, distance_col=distance_col ) assert_geodataframe_equal(result, expected) @pytest.mark.skip_no_sindex def test_clip(self): """ Basic test for availability of the GeoDataFrame method. Other clip tests are located in /tools/tests/test_clip.py """ left = read_file(geopandas.datasets.get_path("naturalearth_cities")) world = read_file(geopandas.datasets.get_path("naturalearth_lowres")) south_america = world[world["continent"] == "South America"] expected = geopandas.clip(left, south_america) result = left.clip(south_america) assert_geodataframe_equal(result, expected) @pytest.mark.skip_no_sindex def test_overlay(self, dfs, how): """ Basic test for availability of the GeoDataFrame method. Other overlay tests are located in tests/test_overlay.py """ df1, df2 = dfs expected = geopandas.overlay(df1, df2, how=how) result = df1.overlay(df2, how=how) assert_geodataframe_equal(result, expected) def check_geodataframe(df, geometry_column="geometry"): assert isinstance(df, GeoDataFrame) assert isinstance(df.geometry, GeoSeries) assert isinstance(df[geometry_column], GeoSeries) assert df._geometry_column_name == geometry_column assert df.geometry.name == geometry_column assert isinstance(df.geometry.values, GeometryArray) assert isinstance(df.geometry.dtype, GeometryDtype) class TestConstructor: def test_dict(self): data = { "A": range(3), "B": np.arange(3.0), "geometry": [Point(x, x) for x in range(3)], } df = GeoDataFrame(data) check_geodataframe(df) # with specifying other kwargs df = GeoDataFrame(data, index=list("abc")) check_geodataframe(df) assert_index_equal(df.index, pd.Index(list("abc"))) df = GeoDataFrame(data, columns=["B", "A", "geometry"]) check_geodataframe(df) assert_index_equal(df.columns, pd.Index(["B", "A", "geometry"])) df = GeoDataFrame(data, columns=["A", "geometry"]) check_geodataframe(df) assert_index_equal(df.columns, pd.Index(["A", "geometry"])) assert_series_equal(df["A"], pd.Series(range(3), name="A")) def test_dict_of_series(self): data = { "A": pd.Series(range(3)), "B": pd.Series(np.arange(3.0)), "geometry": GeoSeries([Point(x, x) for x in range(3)]), } df = GeoDataFrame(data) check_geodataframe(df) df = GeoDataFrame(data, index=pd.Index([1, 2])) check_geodataframe(df) assert_index_equal(df.index, pd.Index([1, 2])) assert df["A"].tolist() == [1, 2] # one non-series -> length is not correct data = { "A": pd.Series(range(3)), "B": np.arange(3.0), "geometry": GeoSeries([Point(x, x) for x in range(3)]), } with pytest.raises(ValueError): GeoDataFrame(data, index=[1, 2]) def test_dict_specified_geometry(self): data = { "A": range(3), "B": np.arange(3.0), "other_geom": [Point(x, x) for x in range(3)], } df = GeoDataFrame(data, geometry="other_geom") check_geodataframe(df, "other_geom") with pytest.raises(ValueError): df = GeoDataFrame(data, geometry="geometry") # when no geometry specified -> works but raises error once # trying to access geometry df = GeoDataFrame(data) with pytest.raises(AttributeError): _ = df.geometry df = df.set_geometry("other_geom") check_geodataframe(df, "other_geom") # combined with custom args df = GeoDataFrame(data, geometry="other_geom", columns=["B", "other_geom"]) check_geodataframe(df, "other_geom") assert_index_equal(df.columns, pd.Index(["B", "other_geom"])) assert_series_equal(df["B"], pd.Series(np.arange(3.0), name="B")) df = GeoDataFrame(data, geometry="other_geom", columns=["other_geom", "A"]) check_geodataframe(df, "other_geom") assert_index_equal(df.columns, pd.Index(["other_geom", "A"])) assert_series_equal(df["A"], pd.Series(range(3), name="A")) def test_array(self): data = { "A": range(3), "B": np.arange(3.0), "geometry": [Point(x, x) for x in range(3)], } with ignore_shapely2_warnings(): a = np.array([data["A"], data["B"], data["geometry"]], dtype=object).T df = GeoDataFrame(a, columns=["A", "B", "geometry"]) check_geodataframe(df) df = GeoDataFrame(a, columns=["A", "B", "other_geom"], geometry="other_geom") check_geodataframe(df, "other_geom") def test_from_frame(self): data = { "A": range(3), "B": np.arange(3.0), "geometry": [Point(x, x) for x in range(3)], } gpdf = GeoDataFrame(data) with ignore_shapely2_warnings(): pddf = pd.DataFrame(data) check_geodataframe(gpdf) assert type(pddf) == pd.DataFrame for df in [gpdf, pddf]: res = GeoDataFrame(df) check_geodataframe(res) res = GeoDataFrame(df, index=pd.Index([0, 2])) check_geodataframe(res) assert_index_equal(res.index, pd.Index([0, 2])) assert res["A"].tolist() == [0, 2] res = GeoDataFrame(df, columns=["geometry", "B"]) check_geodataframe(res) assert_index_equal(res.columns, pd.Index(["geometry", "B"])) with pytest.raises(ValueError): GeoDataFrame(df, geometry="other_geom") def test_from_frame_specified_geometry(self): data = { "A": range(3), "B": np.arange(3.0), "other_geom": [Point(x, x) for x in range(3)], } gpdf = GeoDataFrame(data, geometry="other_geom") check_geodataframe(gpdf, "other_geom") with ignore_shapely2_warnings(): pddf = pd.DataFrame(data) for df in [gpdf, pddf]: res = GeoDataFrame(df, geometry="other_geom") check_geodataframe(res, "other_geom") # gdf from gdf should preserve active geometry column name df = GeoDataFrame(gpdf) check_geodataframe(df, "other_geom") def test_only_geometry(self): exp = GeoDataFrame( {"geometry": [Point(x, x) for x in range(3)], "other": range(3)} )[["geometry"]] df = GeoDataFrame(geometry=[Point(x, x) for x in range(3)]) check_geodataframe(df) assert_geodataframe_equal(df, exp) df = GeoDataFrame({"geometry": [Point(x, x) for x in range(3)]}) check_geodataframe(df) assert_geodataframe_equal(df, exp) df = GeoDataFrame( {"other_geom": [Point(x, x) for x in range(3)]}, geometry="other_geom" ) check_geodataframe(df, "other_geom") exp = exp.rename(columns={"geometry": "other_geom"}).set_geometry("other_geom") assert_geodataframe_equal(df, exp) def test_no_geometries(self): # keeps GeoDataFrame class (no DataFrame) data = {"A": range(3), "B": np.arange(3.0)} df = GeoDataFrame(data) assert type(df) == GeoDataFrame gdf = GeoDataFrame({"x": [1]}) assert list(gdf.x) == [1] def test_empty(self): df = GeoDataFrame() assert type(df) == GeoDataFrame df = GeoDataFrame({"A": [], "B": []}, geometry=[]) assert type(df) == GeoDataFrame def test_column_ordering(self): geoms = [Point(1, 1), Point(2, 2), Point(3, 3)] gs = GeoSeries(geoms) gdf = GeoDataFrame( {"a": [1, 2, 3], "geometry": gs}, columns=["geometry", "a"], geometry="geometry", ) check_geodataframe(gdf) gdf.columns == ["geometry", "a"] # with non-default index gdf = GeoDataFrame( {"a": [1, 2, 3], "geometry": gs}, columns=["geometry", "a"], index=pd.Index([0, 0, 1]), geometry="geometry", ) check_geodataframe(gdf) gdf.columns == ["geometry", "a"] @pytest.mark.xfail def test_preserve_series_name(self): geoms = [Point(1, 1), Point(2, 2), Point(3, 3)] gs = GeoSeries(geoms) gdf = GeoDataFrame({"a": [1, 2, 3]}, geometry=gs) check_geodataframe(gdf, geometry_column="geometry") geoms = [Point(1, 1), Point(2, 2), Point(3, 3)] gs = GeoSeries(geoms, name="my_geom") gdf = GeoDataFrame({"a": [1, 2, 3]}, geometry=gs) check_geodataframe(gdf, geometry_column="my_geom") def test_overwrite_geometry(self): # GH602 data = pd.DataFrame({"geometry": [1, 2, 3], "col1": [4, 5, 6]}) with ignore_shapely2_warnings(): geoms = pd.Series([Point(i, i) for i in range(3)]) # passed geometry kwarg should overwrite geometry column in data res = GeoDataFrame(data, geometry=geoms) assert_geoseries_equal(res.geometry, GeoSeries(geoms)) def test_repeat_geo_col(self): df = pd.DataFrame( [ {"geometry": Point(x, y), "geom": Point(x, y)} for x, y in zip(range(3), range(3)) ], ) # explicitly prevent construction of gdf with repeat geometry column names # two columns called "geometry", geom col inferred df2 = df.rename(columns={"geom": "geometry"}) with pytest.raises(ValueError): GeoDataFrame(df2) # ensure case is caught when custom geom column name is used # two columns called "geom", geom col explicit df3 = df.rename(columns={"geometry": "geom"}) with pytest.raises(ValueError): GeoDataFrame(df3, geometry="geom") def test_geodataframe_crs(): gdf = GeoDataFrame(columns=["geometry"]) gdf.crs = "IGNF:ETRS89UTM28" assert gdf.crs.to_authority() == ("IGNF", "ETRS89UTM28") geopandas-0.10.2/geopandas/tests/test_geom_methods.py000066400000000000000000001250141413247402600227270ustar00rootroot00000000000000import string import numpy as np from numpy.testing import assert_array_equal from pandas import DataFrame, Index, MultiIndex, Series from shapely.geometry import LinearRing, LineString, MultiPoint, Point, Polygon from shapely.geometry.collection import GeometryCollection from shapely.ops import unary_union from geopandas import GeoDataFrame, GeoSeries from geopandas.base import GeoPandasBase from geopandas.testing import assert_geodataframe_equal from geopandas.tests.util import assert_geoseries_equal, geom_almost_equals, geom_equals from geopandas import _compat as compat from pandas.testing import assert_frame_equal, assert_series_equal import pytest def assert_array_dtype_equal(a, b, *args, **kwargs): a = np.asanyarray(a) b = np.asanyarray(b) assert a.dtype == b.dtype assert_array_equal(a, b, *args, **kwargs) class TestGeomMethods: def setup_method(self): self.t1 = Polygon([(0, 0), (1, 0), (1, 1)]) self.t2 = Polygon([(0, 0), (1, 1), (0, 1)]) self.t3 = Polygon([(2, 0), (3, 0), (3, 1)]) self.tz = Polygon([(1, 1, 1), (2, 2, 2), (3, 3, 3)]) self.tz1 = Polygon([(2, 2, 2), (1, 1, 1), (3, 3, 3)]) self.sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) self.sqz = Polygon([(1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]) self.t4 = Polygon([(0, 0), (3, 0), (3, 3), (0, 2)]) self.t5 = Polygon([(2, 0), (3, 0), (3, 3), (2, 3)]) self.inner_sq = Polygon( [(0.25, 0.25), (0.75, 0.25), (0.75, 0.75), (0.25, 0.75)] ) self.nested_squares = Polygon(self.sq.boundary, [self.inner_sq.boundary]) self.p0 = Point(5, 5) self.p3d = Point(5, 5, 5) self.g0 = GeoSeries( [ self.t1, self.t2, self.sq, self.inner_sq, self.nested_squares, self.p0, None, ] ) self.g1 = GeoSeries([self.t1, self.sq]) self.g2 = GeoSeries([self.sq, self.t1]) self.g3 = GeoSeries([self.t1, self.t2]) self.gz = GeoSeries([self.tz, self.sqz, self.tz1]) self.g3.crs = "epsg:4326" self.g4 = GeoSeries([self.t2, self.t1]) self.g4.crs = "epsg:4326" self.g_3d = GeoSeries([self.p0, self.p3d]) self.na = GeoSeries([self.t1, self.t2, Polygon()]) self.na_none = GeoSeries([self.t1, None]) self.a1 = self.g1.copy() self.a1.index = ["A", "B"] self.a2 = self.g2.copy() self.a2.index = ["B", "C"] self.esb = Point(-73.9847, 40.7484, 30.3244) self.sol = Point(-74.0446, 40.6893, 31.2344) self.landmarks = GeoSeries([self.esb, self.sol], crs="epsg:4326") self.pt2d = Point(-73.9847, 40.7484) self.landmarks_mixed = GeoSeries([self.esb, self.sol, self.pt2d], crs=4326) self.l1 = LineString([(0, 0), (0, 1), (1, 1)]) self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g5 = GeoSeries([self.l1, self.l2]) self.g6 = GeoSeries([self.p0, self.t3]) self.g7 = GeoSeries([self.sq, self.t4]) self.g8 = GeoSeries([self.t1, self.t5]) self.empty = GeoSeries([]) self.all_none = GeoSeries([None, None]) self.empty_poly = Polygon() self.g9 = GeoSeries(self.g0, index=range(1, 8)) # Crossed lines self.l3 = LineString([(0, 0), (1, 1)]) self.l4 = LineString([(0, 1), (1, 0)]) self.crossed_lines = GeoSeries([self.l3, self.l4]) # Placeholder for testing, will just drop in different geometries # when needed self.gdf1 = GeoDataFrame( {"geometry": self.g1, "col0": [1.0, 2.0], "col1": ["geo", "pandas"]} ) self.gdf2 = GeoDataFrame( {"geometry": self.g1, "col3": [4, 5], "col4": ["rand", "string"]} ) self.gdf3 = GeoDataFrame( {"geometry": self.g3, "col3": [4, 5], "col4": ["rand", "string"]} ) self.gdfz = GeoDataFrame( {"geometry": self.gz, "col3": [4, 5, 6], "col4": ["rand", "string", "geo"]} ) def _test_unary_real(self, op, expected, a): """Tests for 'area', 'length', 'is_valid', etc.""" fcmp = assert_series_equal self._test_unary(op, expected, a, fcmp) def _test_unary_topological(self, op, expected, a): if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert a.equals(b) self._test_unary(op, expected, a, fcmp) def _test_binary_topological(self, op, expected, a, b, *args, **kwargs): """Tests for 'intersection', 'union', 'symmetric_difference', etc.""" if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert geom_equals(a, b) if isinstance(b, GeoPandasBase): right_df = True else: right_df = False self._binary_op_test(op, expected, a, b, fcmp, True, right_df, *args, **kwargs) def _test_binary_real(self, op, expected, a, b, *args, **kwargs): fcmp = assert_series_equal self._binary_op_test(op, expected, a, b, fcmp, True, False, *args, **kwargs) def _test_binary_operator(self, op, expected, a, b): """ The operators only have GeoSeries on the left, but can have GeoSeries or GeoDataFrame on the right. If GeoDataFrame is on the left, geometry column is used. """ if isinstance(expected, GeoPandasBase): fcmp = assert_geoseries_equal else: def fcmp(a, b): assert geom_equals(a, b) if isinstance(b, GeoPandasBase): right_df = True else: right_df = False self._binary_op_test(op, expected, a, b, fcmp, False, right_df) def _binary_op_test( self, op, expected, left, right, fcmp, left_df, right_df, *args, **kwargs ): """ This is a helper to call a function on GeoSeries and GeoDataFrame arguments. For example, 'intersection' is a member of both GeoSeries and GeoDataFrame and can take either GeoSeries or GeoDataFrame inputs. This function has the ability to test all four combinations of input types. Parameters ---------- expected : str The operation to be tested. e.g., 'intersection' left: GeoSeries right: GeoSeries fcmp: function Called with the result of the operation and expected. It should assert if the result is incorrect left_df: bool If the left input should also be called with a GeoDataFrame right_df: bool Indicates whether the right input should be called with a GeoDataFrame """ def _make_gdf(s): n = len(s) col1 = string.ascii_lowercase[:n] col2 = range(n) return GeoDataFrame( {"geometry": s.values, "col1": col1, "col2": col2}, index=s.index, crs=s.crs, ) # Test GeoSeries.op(GeoSeries) result = getattr(left, op)(right, *args, **kwargs) fcmp(result, expected) if left_df: # Test GeoDataFrame.op(GeoSeries) gdf_left = _make_gdf(left) result = getattr(gdf_left, op)(right, *args, **kwargs) fcmp(result, expected) if right_df: # Test GeoSeries.op(GeoDataFrame) gdf_right = _make_gdf(right) result = getattr(left, op)(gdf_right, *args, **kwargs) fcmp(result, expected) if left_df: # Test GeoDataFrame.op(GeoDataFrame) result = getattr(gdf_left, op)(gdf_right, *args, **kwargs) fcmp(result, expected) def _test_unary(self, op, expected, a, fcmp): # GeoSeries, (GeoSeries or geometry) result = getattr(a, op) fcmp(result, expected) # GeoDataFrame, (GeoSeries or geometry) gdf = self.gdf1.set_geometry(a) result = getattr(gdf, op) fcmp(result, expected) # TODO re-enable for all operations once we use pyproj > 2 # def test_crs_warning(self): # # operations on geometries should warn for different CRS # no_crs_g3 = self.g3.copy() # no_crs_g3.crs = None # with pytest.warns(UserWarning): # self._test_binary_topological('intersection', self.g3, # self.g3, no_crs_g3) def test_intersection(self): self._test_binary_topological("intersection", self.t1, self.g1, self.g2) with pytest.warns(UserWarning, match="The indices .+ different"): self._test_binary_topological( "intersection", self.all_none, self.g1, self.empty ) with pytest.warns(UserWarning, match="The indices .+ different"): assert len(self.g0.intersection(self.g9, align=True) == 8) assert len(self.g0.intersection(self.g9, align=False) == 7) def test_union_series(self): self._test_binary_topological("union", self.sq, self.g1, self.g2) with pytest.warns(UserWarning, match="The indices .+ different"): assert len(self.g0.union(self.g9, align=True) == 8) assert len(self.g0.union(self.g9, align=False) == 7) def test_union_polygon(self): self._test_binary_topological("union", self.sq, self.g1, self.t2) def test_symmetric_difference_series(self): self._test_binary_topological("symmetric_difference", self.sq, self.g3, self.g4) with pytest.warns(UserWarning, match="The indices .+ different"): assert len(self.g0.symmetric_difference(self.g9, align=True) == 8) assert len(self.g0.symmetric_difference(self.g9, align=False) == 7) def test_symmetric_difference_poly(self): expected = GeoSeries([GeometryCollection(), self.sq], crs=self.g3.crs) self._test_binary_topological( "symmetric_difference", expected, self.g3, self.t1 ) def test_difference_series(self): expected = GeoSeries([GeometryCollection(), self.t2]) self._test_binary_topological("difference", expected, self.g1, self.g2) with pytest.warns(UserWarning, match="The indices .+ different"): assert len(self.g0.difference(self.g9, align=True) == 8) assert len(self.g0.difference(self.g9, align=False) == 7) def test_difference_poly(self): expected = GeoSeries([self.t1, self.t1]) self._test_binary_topological("difference", expected, self.g1, self.t2) def test_geo_op_empty_result(self): l1 = LineString([(0, 0), (1, 1)]) l2 = LineString([(2, 2), (3, 3)]) expected = GeoSeries([GeometryCollection()]) # binary geo resulting in empty geometry result = GeoSeries([l1]).intersection(l2) assert_geoseries_equal(result, expected) # binary geo empty result with right GeoSeries result = GeoSeries([l1]).intersection(GeoSeries([l2])) assert_geoseries_equal(result, expected) # unary geo resulting in empty geometry result = GeoSeries([GeometryCollection()]).convex_hull assert_geoseries_equal(result, expected) def test_boundary(self): l1 = LineString([(0, 0), (1, 0), (1, 1), (0, 0)]) l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)]) expected = GeoSeries([l1, l2], index=self.g1.index, crs=self.g1.crs) self._test_unary_topological("boundary", expected, self.g1) def test_area(self): expected = Series(np.array([0.5, 1.0]), index=self.g1.index) self._test_unary_real("area", expected, self.g1) expected = Series(np.array([0.5, np.nan]), index=self.na_none.index) self._test_unary_real("area", expected, self.na_none) def test_area_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.area def test_bounds(self): # Set columns to get the order right expected = DataFrame( { "minx": [0.0, 0.0], "miny": [0.0, 0.0], "maxx": [1.0, 1.0], "maxy": [1.0, 1.0], }, index=self.g1.index, columns=["minx", "miny", "maxx", "maxy"], ) result = self.g1.bounds assert_frame_equal(expected, result) gdf = self.gdf1.set_geometry(self.g1) result = gdf.bounds assert_frame_equal(expected, result) def test_bounds_empty(self): # test bounds of empty GeoSeries # https://github.com/geopandas/geopandas/issues/1195 s = GeoSeries([]) result = s.bounds expected = DataFrame( columns=["minx", "miny", "maxx", "maxy"], index=s.index, dtype="float64" ) assert_frame_equal(result, expected) def test_unary_union(self): p1 = self.t1 p2 = Polygon([(2, 0), (3, 0), (3, 1)]) expected = unary_union([p1, p2]) g = GeoSeries([p1, p2]) self._test_unary_topological("unary_union", expected, g) g2 = GeoSeries([p1, None]) self._test_unary_topological("unary_union", p1, g2) g3 = GeoSeries([None, None]) assert g3.unary_union is None def test_cascaded_union_deprecated(self): p1 = self.t1 p2 = Polygon([(2, 0), (3, 0), (3, 1)]) g = GeoSeries([p1, p2]) with pytest.warns( FutureWarning, match="The 'cascaded_union' attribute is deprecated" ): result = g.cascaded_union assert result == g.unary_union def test_contains(self): expected = [True, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.contains(self.t1)) expected = [False, True, True, True, True, True, False, False] with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.contains(self.g9, align=True)) expected = [False, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.contains(self.g9, align=False)) def test_length(self): expected = Series(np.array([2 + np.sqrt(2), 4]), index=self.g1.index) self._test_unary_real("length", expected, self.g1) expected = Series(np.array([2 + np.sqrt(2), np.nan]), index=self.na_none.index) self._test_unary_real("length", expected, self.na_none) def test_length_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.length def test_crosses(self): expected = [False, False, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.crosses(self.t1)) expected = [False, True] assert_array_dtype_equal(expected, self.crossed_lines.crosses(self.l3)) expected = [False] * 8 with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.crosses(self.g9, align=True)) expected = [False] * 7 assert_array_dtype_equal(expected, self.g0.crosses(self.g9, align=False)) def test_disjoint(self): expected = [False, False, False, False, False, True, False] assert_array_dtype_equal(expected, self.g0.disjoint(self.t1)) expected = [False] * 8 with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.disjoint(self.g9, align=True)) expected = [False, False, False, False, True, False, False] assert_array_dtype_equal(expected, self.g0.disjoint(self.g9, align=False)) def test_relate(self): expected = Series( [ "212101212", "212101212", "212FF1FF2", "2FFF1FFF2", "FF2F112F2", "FF0FFF212", None, ], index=self.g0.index, ) assert_array_dtype_equal(expected, self.g0.relate(self.inner_sq)) expected = Series(["FF0FFF212", None], index=self.g6.index) assert_array_dtype_equal(expected, self.g6.relate(self.na_none)) expected = Series( [ None, "2FFF1FFF2", "2FFF1FFF2", "2FFF1FFF2", "2FFF1FFF2", "0FFFFFFF2", None, None, ], index=range(8), ) with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.relate(self.g9, align=True)) expected = Series( [ "FF2F11212", "2FF11F212", "212FF1FF2", "FF2F1F212", "FF2FF10F2", None, None, ], index=self.g0.index, ) assert_array_dtype_equal(expected, self.g0.relate(self.g9, align=False)) def test_distance(self): expected = Series( np.array([np.sqrt((5 - 1) ** 2 + (5 - 1) ** 2), np.nan]), self.na_none.index ) assert_array_dtype_equal(expected, self.na_none.distance(self.p0)) expected = Series(np.array([np.sqrt(4 ** 2 + 4 ** 2), np.nan]), self.g6.index) assert_array_dtype_equal(expected, self.g6.distance(self.na_none)) expected = Series(np.array([np.nan, 0, 0, 0, 0, 0, np.nan, np.nan]), range(8)) with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.distance(self.g9, align=True)) val = self.g0.iloc[4].distance(self.g9.iloc[4]) expected = Series(np.array([0, 0, 0, 0, val, np.nan, np.nan]), self.g0.index) assert_array_dtype_equal(expected, self.g0.distance(self.g9, align=False)) def test_distance_crs_warning(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.distance(self.p0) def test_intersects(self): expected = [True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.intersects(self.t1)) expected = [True, False] assert_array_dtype_equal(expected, self.na_none.intersects(self.t2)) expected = np.array([], dtype=bool) assert_array_dtype_equal(expected, self.empty.intersects(self.t1)) expected = np.array([], dtype=bool) assert_array_dtype_equal(expected, self.empty.intersects(self.empty_poly)) expected = [False] * 7 assert_array_dtype_equal(expected, self.g0.intersects(self.empty_poly)) expected = [False, True, True, True, True, True, False, False] with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.intersects(self.g9, align=True)) expected = [True, True, True, True, False, False, False] assert_array_dtype_equal(expected, self.g0.intersects(self.g9, align=False)) def test_overlaps(self): expected = [True, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.overlaps(self.inner_sq)) expected = [False, False] assert_array_dtype_equal(expected, self.g4.overlaps(self.t1)) expected = [False] * 8 with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.overlaps(self.g9, align=True)) expected = [False] * 7 assert_array_dtype_equal(expected, self.g0.overlaps(self.g9, align=False)) def test_touches(self): expected = [False, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.touches(self.t1)) expected = [False] * 8 with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.touches(self.g9, align=True)) expected = [True, False, False, True, False, False, False] assert_array_dtype_equal(expected, self.g0.touches(self.g9, align=False)) def test_within(self): expected = [True, False, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.within(self.t1)) expected = [True, True, True, True, True, False, False] assert_array_dtype_equal(expected, self.g0.within(self.sq)) expected = [False, True, True, True, True, True, False, False] with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.within(self.g9, align=True)) expected = [False, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.within(self.g9, align=False)) def test_covers_itself(self): # Each polygon in a Series covers itself res = self.g1.covers(self.g1) exp = Series([True, True]) assert_series_equal(res, exp) def test_covers(self): res = self.g7.covers(self.g8) exp = Series([True, False]) assert_series_equal(res, exp) expected = [False, True, True, True, True, True, False, False] with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.covers(self.g9, align=True)) expected = [False, False, True, False, False, False, False] assert_array_dtype_equal(expected, self.g0.covers(self.g9, align=False)) def test_covers_inverse(self): res = self.g8.covers(self.g7) exp = Series([False, False]) assert_series_equal(res, exp) @pytest.mark.skipif( not compat.USE_PYGEOS, reason="covered_by is only implemented for pygeos, not shapely", ) def test_covered_by(self): res = self.g1.covered_by(self.g1) exp = Series([True, True]) assert_series_equal(res, exp) expected = [False, True, True, True, True, True, False, False] with pytest.warns(UserWarning, match="The indices .+ different"): assert_array_dtype_equal(expected, self.g0.covered_by(self.g9, align=True)) expected = [False, True, False, False, False, False, False] assert_array_dtype_equal(expected, self.g0.covered_by(self.g9, align=False)) def test_is_valid(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real("is_valid", expected, self.g1) def test_is_empty(self): expected = Series(np.array([False] * len(self.g1)), self.g1.index) self._test_unary_real("is_empty", expected, self.g1) # for is_ring we raise a warning about the value for Polygon changing @pytest.mark.filterwarnings("ignore:is_ring:FutureWarning") def test_is_ring(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real("is_ring", expected, self.g1) def test_is_simple(self): expected = Series(np.array([True] * len(self.g1)), self.g1.index) self._test_unary_real("is_simple", expected, self.g1) def test_has_z(self): expected = Series([False, True], self.g_3d.index) self._test_unary_real("has_z", expected, self.g_3d) def test_xyz_points(self): expected_x = [-73.9847, -74.0446] expected_y = [40.7484, 40.6893] expected_z = [30.3244, 31.2344] assert_array_dtype_equal(expected_x, self.landmarks.geometry.x) assert_array_dtype_equal(expected_y, self.landmarks.geometry.y) assert_array_dtype_equal(expected_z, self.landmarks.geometry.z) # mixed dimensions expected_z = [30.3244, 31.2344, np.nan] assert_array_dtype_equal(expected_z, self.landmarks_mixed.geometry.z) def test_xyz_polygons(self): # accessing x attribute in polygon geoseries should raise an error with pytest.raises(ValueError): _ = self.gdf1.geometry.x # and same for accessing y attribute in polygon geoseries with pytest.raises(ValueError): _ = self.gdf1.geometry.y # and same for accessing z attribute in polygon geoseries with pytest.raises(ValueError): _ = self.gdfz.geometry.z def test_centroid(self): polygon = Polygon([(-1, -1), (1, -1), (1, 1), (-1, 1)]) point = Point(0, 0) polygons = GeoSeries([polygon for i in range(3)]) points = GeoSeries([point for i in range(3)]) assert_geoseries_equal(polygons.centroid, points) def test_centroid_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.centroid def test_convex_hull(self): # the convex hull of a square should be the same as the square squares = GeoSeries([self.sq for i in range(3)]) assert_geoseries_equal(squares, squares.convex_hull) def test_exterior(self): exp_exterior = GeoSeries([LinearRing(p.boundary) for p in self.g3]) for expected, computed in zip(exp_exterior, self.g3.exterior): assert computed.equals(expected) def test_interiors(self): original = GeoSeries([self.t1, self.nested_squares]) # This is a polygon with no interior. expected = [] assert original.interiors[0] == expected # This is a polygon with an interior. expected = LinearRing(self.inner_sq.boundary) assert original.interiors[1][0].equals(expected) def test_interpolate(self): expected = GeoSeries([Point(0.5, 1.0), Point(0.75, 1.0)]) self._test_binary_topological( "interpolate", expected, self.g5, 0.75, normalized=True ) expected = GeoSeries([Point(0.5, 1.0), Point(1.0, 0.5)]) self._test_binary_topological("interpolate", expected, self.g5, 1.5) def test_interpolate_distance_array(self): expected = GeoSeries([Point(0.0, 0.75), Point(1.0, 0.5)]) self._test_binary_topological( "interpolate", expected, self.g5, np.array([0.75, 1.5]) ) expected = GeoSeries([Point(0.5, 1.0), Point(0.0, 1.0)]) self._test_binary_topological( "interpolate", expected, self.g5, np.array([0.75, 1.5]), normalized=True ) def test_interpolate_distance_wrong_length(self): distances = np.array([1, 2, 3]) with pytest.raises(ValueError): self.g5.interpolate(distances) def test_interpolate_distance_wrong_index(self): distances = Series([1, 2], index=[99, 98]) with pytest.raises(ValueError): self.g5.interpolate(distances) def test_interpolate_crs_warning(self): g5_crs = self.g5.copy() g5_crs.crs = 4326 with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): g5_crs.interpolate(1) def test_project(self): expected = Series([2.0, 1.5], index=self.g5.index) p = Point(1.0, 0.5) self._test_binary_real("project", expected, self.g5, p) expected = Series([1.0, 0.5], index=self.g5.index) self._test_binary_real("project", expected, self.g5, p, normalized=True) s = GeoSeries([Point(2, 2), Point(0.5, 0.5)], index=[1, 2]) expected = Series([np.nan, 2.0, np.nan]) with pytest.warns(UserWarning, match="The indices .+ different"): assert_series_equal(self.g5.project(s), expected) expected = Series([2.0, 0.5], index=self.g5.index) assert_series_equal(self.g5.project(s, align=False), expected) def test_affine_transform(self): # 45 degree reflection matrix matrix = [0, 1, 1, 0, 0, 0] expected = self.g4 res = self.g3.affine_transform(matrix) assert_geoseries_equal(expected, res) def test_translate_tuple(self): trans = self.sol.x - self.esb.x, self.sol.y - self.esb.y assert self.landmarks.translate(*trans)[0].equals(self.sol) res = self.gdf1.set_geometry(self.landmarks).translate(*trans)[0] assert res.equals(self.sol) def test_rotate(self): angle = 98 expected = self.g4 o = Point(0, 0) res = self.g4.rotate(angle, origin=o).rotate(-angle, origin=o) assert geom_almost_equals(self.g4, res) res = self.gdf1.set_geometry(self.g4).rotate(angle, origin=Point(0, 0)) assert geom_almost_equals(expected, res.rotate(-angle, origin=o)) def test_scale(self): expected = self.g4 scale = 2.0, 1.0 inv = tuple(1.0 / i for i in scale) o = Point(0, 0) res = self.g4.scale(*scale, origin=o).scale(*inv, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).scale(*scale, origin=o) res = res.scale(*inv, origin=o) assert geom_almost_equals(expected, res) def test_skew(self): expected = self.g4 skew = 45.0 o = Point(0, 0) # Test xs res = self.g4.skew(xs=skew, origin=o).skew(xs=-skew, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).skew(xs=skew, origin=o) res = res.skew(xs=-skew, origin=o) assert geom_almost_equals(expected, res) # Test ys res = self.g4.skew(ys=skew, origin=o).skew(ys=-skew, origin=o) assert geom_almost_equals(expected, res) res = self.gdf1.set_geometry(self.g4).skew(ys=skew, origin=o) res = res.skew(ys=-skew, origin=o) assert geom_almost_equals(expected, res) def test_buffer(self): original = GeoSeries([Point(0, 0)]) expected = GeoSeries([Polygon(((5, 0), (0, -5), (-5, 0), (0, 5), (5, 0)))]) calculated = original.buffer(5, resolution=1) assert geom_almost_equals(expected, calculated) def test_buffer_args(self): args = dict(cap_style=3, join_style=2, mitre_limit=2.5) calculated_series = self.g0.buffer(10, **args) for original, calculated in zip(self.g0, calculated_series): if original is None: assert calculated is None else: expected = original.buffer(10, **args) assert calculated.equals(expected) def test_buffer_distance_array(self): original = GeoSeries([self.p0, self.p0]) expected = GeoSeries( [ Polygon(((6, 5), (5, 4), (4, 5), (5, 6), (6, 5))), Polygon(((10, 5), (5, 0), (0, 5), (5, 10), (10, 5))), ] ) calculated = original.buffer(np.array([1, 5]), resolution=1) assert_geoseries_equal(calculated, expected, check_less_precise=True) def test_buffer_distance_wrong_length(self): original = GeoSeries([self.p0, self.p0]) distances = np.array([1, 2, 3]) with pytest.raises(ValueError): original.buffer(distances) def test_buffer_distance_wrong_index(self): original = GeoSeries([self.p0, self.p0], index=[0, 1]) distances = Series(data=[1, 2], index=[99, 98]) with pytest.raises(ValueError): original.buffer(distances) def test_buffer_empty_none(self): p = Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]) s = GeoSeries([p, GeometryCollection(), None]) result = s.buffer(0) assert_geoseries_equal(result, s) result = s.buffer(np.array([0, 0, 0])) assert_geoseries_equal(result, s) def test_buffer_crs_warn(self): with pytest.warns(UserWarning, match="Geometry is in a geographic CRS"): self.g4.buffer(1) with pytest.warns(None) as record: # do not warn for 0 self.g4.buffer(0) for r in record: assert "Geometry is in a geographic CRS." not in str(r.message) def test_envelope(self): e = self.g3.envelope assert np.all(e.geom_equals(self.sq)) assert isinstance(e, GeoSeries) assert self.g3.crs == e.crs def test_total_bounds(self): bbox = self.sol.x, self.sol.y, self.esb.x, self.esb.y assert isinstance(self.landmarks.total_bounds, np.ndarray) assert tuple(self.landmarks.total_bounds) == bbox df = GeoDataFrame( {"geometry": self.landmarks, "col1": range(len(self.landmarks))} ) assert tuple(df.total_bounds) == bbox def test_explode_geoseries(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])], crs=4326, ) s.index.name = "test_index_name" expected_index_name = ["test_index_name", None] index = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 2)] expected = GeoSeries( [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4)], index=MultiIndex.from_tuples(index, names=expected_index_name), crs=4326, ) with pytest.warns(FutureWarning, match="Currently, index_parts defaults"): assert_geoseries_equal(expected, s.explode()) @pytest.mark.parametrize("index_name", [None, "test"]) def test_explode_geodataframe(self, index_name): s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"col": [1, 2], "geometry": s}) df.index.name = index_name with pytest.warns(FutureWarning, match="Currently, index_parts defaults"): test_df = df.explode() expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)]) expected_df = GeoDataFrame({"col": [1, 1, 2], "geometry": expected_s}) expected_index = MultiIndex( [[0, 1], [0, 1]], # levels [[0, 0, 1], [0, 1, 0]], # labels/codes names=[index_name, None], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) @pytest.mark.parametrize("index_name", [None, "test"]) def test_explode_geodataframe_level_1(self, index_name): # GH1393 s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"level_1": [1, 2], "geometry": s}) df.index.name = index_name test_df = df.explode(index_parts=True) expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)]) expected_df = GeoDataFrame({"level_1": [1, 1, 2], "geometry": expected_s}) expected_index = MultiIndex( [[0, 1], [0, 1]], # levels [[0, 0, 1], [0, 1, 0]], # labels/codes names=[index_name, None], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) @pytest.mark.parametrize("index_name", [None, "test"]) def test_explode_geodataframe_no_multiindex(self, index_name): # GH1393 s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)]) df = GeoDataFrame({"level_1": [1, 2], "geometry": s}) df.index.name = index_name test_df = df.explode(index_parts=False) expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)]) expected_df = GeoDataFrame({"level_1": [1, 1, 2], "geometry": expected_s}) expected_index = Index([0, 0, 1], name=index_name) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) def test_explode_pandas_fallback(self): d = { "col1": [["name1", "name2"], ["name3", "name4"]], "geometry": [MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)])], } gdf = GeoDataFrame(d, crs=4326) expected_df = GeoDataFrame( { "col1": ["name1", "name2", "name3", "name4"], "geometry": [ MultiPoint([(1, 2), (3, 4)]), MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)]), MultiPoint([(2, 1), (0, 0)]), ], }, index=[0, 0, 1, 1], crs=4326, ) # Test with column provided as arg exploded_df = gdf.explode("col1") assert_geodataframe_equal(exploded_df, expected_df) # Test with column provided as kwarg exploded_df = gdf.explode(column="col1") assert_geodataframe_equal(exploded_df, expected_df) @pytest.mark.skipif( not compat.PANDAS_GE_11, reason="ignore_index keyword introduced in pandas 1.1.0", ) def test_explode_pandas_fallback_ignore_index(self): d = { "col1": [["name1", "name2"], ["name3", "name4"]], "geometry": [MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)])], } gdf = GeoDataFrame(d, crs=4326) expected_df = GeoDataFrame( { "col1": ["name1", "name2", "name3", "name4"], "geometry": [ MultiPoint([(1, 2), (3, 4)]), MultiPoint([(1, 2), (3, 4)]), MultiPoint([(2, 1), (0, 0)]), MultiPoint([(2, 1), (0, 0)]), ], }, crs=4326, ) # Test with column provided as arg exploded_df = gdf.explode("col1", ignore_index=True) assert_geodataframe_equal(exploded_df, expected_df) # Test with column provided as kwarg exploded_df = gdf.explode(column="col1", ignore_index=True) assert_geodataframe_equal(exploded_df, expected_df) @pytest.mark.parametrize("outer_index", [1, (1, 2), "1"]) def test_explode_pandas_multi_index(self, outer_index): index = MultiIndex.from_arrays( [[outer_index, outer_index, outer_index], [1, 2, 3]], names=("first", "second"), ) df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=index, ) test_df = df.explode(index_parts=True) expected_s = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ] ) expected_df = GeoDataFrame({"vals": [1, 1, 2, 2, 3, 3], "geometry": expected_s}) expected_index = MultiIndex.from_tuples( [ (outer_index, *pair) for pair in [(1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)] ], names=["first", "second", None], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) @pytest.mark.parametrize("outer_index", [1, (1, 2), "1"]) def test_explode_pandas_multi_index_false(self, outer_index): index = MultiIndex.from_arrays( [[outer_index, outer_index, outer_index], [1, 2, 3]], names=("first", "second"), ) df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=index, ) test_df = df.explode(index_parts=False) expected_s = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ] ) expected_df = GeoDataFrame({"vals": [1, 1, 2, 2, 3, 3], "geometry": expected_s}) expected_index = MultiIndex.from_tuples( [ (outer_index, 1), (outer_index, 1), (outer_index, 2), (outer_index, 2), (outer_index, 3), (outer_index, 3), ], names=["first", "second"], ) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) @pytest.mark.parametrize("outer_index", [1, (1, 2), "1"]) def test_explode_pandas_multi_index_ignore_index(self, outer_index): index = MultiIndex.from_arrays( [[outer_index, outer_index, outer_index], [1, 2, 3]], names=("first", "second"), ) df = GeoDataFrame( {"vals": [1, 2, 3]}, geometry=[MultiPoint([(x, x), (x, 0)]) for x in range(3)], index=index, ) test_df = df.explode(ignore_index=True) expected_s = GeoSeries( [ Point(0, 0), Point(0, 0), Point(1, 1), Point(1, 0), Point(2, 2), Point(2, 0), ] ) expected_df = GeoDataFrame({"vals": [1, 1, 2, 2, 3, 3], "geometry": expected_s}) expected_index = Index(range(len(expected_df))) expected_df = expected_df.set_index(expected_index) assert_frame_equal(test_df, expected_df) # index_parts is ignored if ignore_index=True test_df = df.explode(ignore_index=True, index_parts=True) assert_frame_equal(test_df, expected_df) # # Test '&', '|', '^', and '-' # def test_intersection_operator(self): with pytest.warns(DeprecationWarning): self._test_binary_operator("__and__", self.t1, self.g1, self.g2) with pytest.warns(DeprecationWarning): self._test_binary_operator("__and__", self.t1, self.gdf1, self.g2) def test_union_operator(self): with pytest.warns(DeprecationWarning): self._test_binary_operator("__or__", self.sq, self.g1, self.g2) with pytest.warns(DeprecationWarning): self._test_binary_operator("__or__", self.sq, self.gdf1, self.g2) def test_union_operator_polygon(self): with pytest.warns(DeprecationWarning): self._test_binary_operator("__or__", self.sq, self.g1, self.t2) with pytest.warns(DeprecationWarning): self._test_binary_operator("__or__", self.sq, self.gdf1, self.t2) def test_symmetric_difference_operator(self): with pytest.warns(DeprecationWarning): self._test_binary_operator("__xor__", self.sq, self.g3, self.g4) with pytest.warns(DeprecationWarning): self._test_binary_operator("__xor__", self.sq, self.gdf3, self.g4) def test_difference_series2(self): expected = GeoSeries([GeometryCollection(), self.t2]) with pytest.warns(DeprecationWarning): self._test_binary_operator("__sub__", expected, self.g1, self.g2) with pytest.warns(DeprecationWarning): self._test_binary_operator("__sub__", expected, self.gdf1, self.g2) def test_difference_poly2(self): expected = GeoSeries([self.t1, self.t1]) with pytest.warns(DeprecationWarning): self._test_binary_operator("__sub__", expected, self.g1, self.t2) with pytest.warns(DeprecationWarning): self._test_binary_operator("__sub__", expected, self.gdf1, self.t2) geopandas-0.10.2/geopandas/tests/test_geoseries.py000066400000000000000000000505351413247402600222470ustar00rootroot00000000000000import json import os import random import shutil import tempfile import numpy as np from numpy.testing import assert_array_equal import pandas as pd from pandas.util.testing import assert_index_equal from pyproj import CRS from shapely.geometry import ( LineString, MultiLineString, MultiPoint, MultiPolygon, Point, Polygon, ) from shapely.geometry.base import BaseGeometry from geopandas import GeoSeries, GeoDataFrame, read_file, datasets, clip from geopandas._compat import PYPROJ_LT_3, ignore_shapely2_warnings from geopandas.array import GeometryArray, GeometryDtype from geopandas.testing import assert_geoseries_equal from geopandas.tests.util import geom_equals from pandas.testing import assert_series_equal import pytest class TestSeries: def setup_method(self): self.tempdir = tempfile.mkdtemp() self.t1 = Polygon([(0, 0), (1, 0), (1, 1)]) self.t2 = Polygon([(0, 0), (1, 1), (0, 1)]) self.sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g1 = GeoSeries([self.t1, self.sq]) self.g2 = GeoSeries([self.sq, self.t1]) self.g3 = GeoSeries([self.t1, self.t2]) self.g3.crs = "epsg:4326" self.g4 = GeoSeries([self.t2, self.t1]) self.na = GeoSeries([self.t1, self.t2, Polygon()]) self.na_none = GeoSeries([self.t1, self.t2, None]) self.a1 = self.g1.copy() self.a1.index = ["A", "B"] self.a2 = self.g2.copy() self.a2.index = ["B", "C"] self.esb = Point(-73.9847, 40.7484) self.sol = Point(-74.0446, 40.6893) self.landmarks = GeoSeries([self.esb, self.sol], crs="epsg:4326") self.l1 = LineString([(0, 0), (0, 1), (1, 1)]) self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)]) self.g5 = GeoSeries([self.l1, self.l2]) def teardown_method(self): shutil.rmtree(self.tempdir) def test_copy(self): gc = self.g3.copy() assert type(gc) is GeoSeries assert self.g3.name == gc.name assert self.g3.crs == gc.crs def test_in(self): assert self.t1 in self.g1 assert self.sq in self.g1 assert self.t1 in self.a1 assert self.t2 in self.g3 assert self.sq not in self.g3 assert 5 not in self.g3 def test_align(self): a1, a2 = self.a1.align(self.a2) assert isinstance(a1, GeoSeries) assert isinstance(a2, GeoSeries) assert a2["A"] is None assert a1["B"].equals(a2["B"]) assert a1["C"] is None def test_align_crs(self): a1 = self.a1 a1.crs = "epsg:4326" a2 = self.a2 a2.crs = "epsg:31370" res1, res2 = a1.align(a2) assert res1.crs == "epsg:4326" assert res2.crs == "epsg:31370" a2.crs = None res1, res2 = a1.align(a2) assert res1.crs == "epsg:4326" assert res2.crs is None def test_align_mixed(self): a1 = self.a1 s2 = pd.Series([1, 2], index=["B", "C"]) res1, res2 = a1.align(s2) exp2 = pd.Series([np.nan, 1, 2], index=["A", "B", "C"]) assert_series_equal(res2, exp2) def test_warning_if_not_aligned(self): # GH-816 # Test that warning is issued when operating on non-aligned series # _series_op with pytest.warns(UserWarning, match="The indices .+ different"): self.a1.contains(self.a2) # _geo_op with pytest.warns(UserWarning, match="The indices .+ different"): self.a1.union(self.a2) def test_no_warning_if_aligned(self): # GH-816 # Test that warning is not issued when operating on aligned series a1, a2 = self.a1.align(self.a2) with pytest.warns(None) as warnings: a1.contains(a2) # _series_op, explicitly aligned self.g1.intersects(self.g2) # _series_op, implicitly aligned a2.union(a1) # _geo_op, explicitly aligned self.g2.intersection(self.g1) # _geo_op, implicitly aligned user_warnings = [w for w in warnings if w.category is UserWarning] assert not user_warnings, user_warnings[0].message def test_geom_equals(self): assert np.all(self.g1.geom_equals(self.g1)) assert_array_equal(self.g1.geom_equals(self.sq), [False, True]) def test_geom_equals_align(self): with pytest.warns(UserWarning, match="The indices .+ different"): a = self.a1.geom_equals(self.a2, align=True) exp = pd.Series([False, True, False], index=["A", "B", "C"]) assert_series_equal(a, exp) a = self.a1.geom_equals(self.a2, align=False) exp = pd.Series([False, False], index=["A", "B"]) assert_series_equal(a, exp) def test_geom_almost_equals(self): # TODO: test decimal parameter assert np.all(self.g1.geom_almost_equals(self.g1)) assert_array_equal(self.g1.geom_almost_equals(self.sq), [False, True]) assert_array_equal( self.a1.geom_almost_equals(self.a2, align=True), [False, True, False] ) assert_array_equal( self.a1.geom_almost_equals(self.a2, align=False), [False, False] ) def test_geom_equals_exact(self): # TODO: test tolerance parameter assert np.all(self.g1.geom_equals_exact(self.g1, 0.001)) assert_array_equal(self.g1.geom_equals_exact(self.sq, 0.001), [False, True]) assert_array_equal( self.a1.geom_equals_exact(self.a2, 0.001, align=True), [False, True, False] ) assert_array_equal( self.a1.geom_equals_exact(self.a2, 0.001, align=False), [False, False] ) def test_equal_comp_op(self): s = GeoSeries([Point(x, x) for x in range(3)]) res = s == Point(1, 1) exp = pd.Series([False, True, False]) assert_series_equal(res, exp) def test_to_file(self): """Test to_file and from_file""" tempfilename = os.path.join(self.tempdir, "test.shp") self.g3.to_file(tempfilename) # Read layer back in? s = GeoSeries.from_file(tempfilename) assert all(self.g3.geom_equals(s)) # TODO: compare crs def test_to_json(self): """ Test whether GeoSeries.to_json works and returns an actual json file. """ json_str = self.g3.to_json() json.loads(json_str) # TODO : verify the output is a valid GeoJSON. def test_representative_point(self): assert np.all(self.g1.contains(self.g1.representative_point())) assert np.all(self.g2.contains(self.g2.representative_point())) assert np.all(self.g3.contains(self.g3.representative_point())) assert np.all(self.g4.contains(self.g4.representative_point())) def test_transform(self): utm18n = self.landmarks.to_crs(epsg=26918) lonlat = utm18n.to_crs(epsg=4326) assert np.all(self.landmarks.geom_almost_equals(lonlat)) with pytest.raises(ValueError): self.g1.to_crs(epsg=4326) with pytest.raises(ValueError): self.landmarks.to_crs(crs=None, epsg=None) def test_estimate_utm_crs__geographic(self): if PYPROJ_LT_3: with pytest.raises(RuntimeError, match=r"pyproj 3\+ required"): self.landmarks.estimate_utm_crs() else: assert self.landmarks.estimate_utm_crs() == CRS("EPSG:32618") assert self.landmarks.estimate_utm_crs("NAD83") == CRS("EPSG:26918") @pytest.mark.skipif(PYPROJ_LT_3, reason="requires pyproj 3 or higher") def test_estimate_utm_crs__projected(self): assert self.landmarks.to_crs("EPSG:3857").estimate_utm_crs() == CRS( "EPSG:32618" ) @pytest.mark.skipif(PYPROJ_LT_3, reason="requires pyproj 3 or higher") def test_estimate_utm_crs__out_of_bounds(self): with pytest.raises(RuntimeError, match="Unable to determine UTM CRS"): GeoSeries( [Polygon([(0, 90), (1, 90), (2, 90)])], crs="EPSG:4326" ).estimate_utm_crs() @pytest.mark.skipif(PYPROJ_LT_3, reason="requires pyproj 3 or higher") def test_estimate_utm_crs__missing_crs(self): with pytest.raises(RuntimeError, match="crs must be set"): GeoSeries([Polygon([(0, 90), (1, 90), (2, 90)])]).estimate_utm_crs() def test_fillna(self): # default is to fill with empty geometry na = self.na_none.fillna() assert isinstance(na[2], BaseGeometry) assert na[2].is_empty assert geom_equals(self.na_none[:2], na[:2]) # XXX: method works inconsistently for different pandas versions # self.na_none.fillna(method='backfill') def test_coord_slice(self): """Test CoordinateSlicer""" # need some better test cases assert geom_equals(self.g3, self.g3.cx[:, :]) assert geom_equals(self.g3[[True, False]], self.g3.cx[0.9:, :0.1]) assert geom_equals(self.g3[[False, True]], self.g3.cx[0:0.1, 0.9:1.0]) def test_coord_slice_with_zero(self): # Test that CoordinateSlice correctly handles zero slice (#GH477). gs = GeoSeries([Point(x, x) for x in range(-3, 4)]) assert geom_equals(gs.cx[:0, :0], gs.loc[:3]) assert geom_equals(gs.cx[:, :0], gs.loc[:3]) assert geom_equals(gs.cx[:0, :], gs.loc[:3]) assert geom_equals(gs.cx[0:, 0:], gs.loc[3:]) assert geom_equals(gs.cx[0:, :], gs.loc[3:]) assert geom_equals(gs.cx[:, 0:], gs.loc[3:]) def test_geoseries_geointerface(self): assert self.g1.__geo_interface__["type"] == "FeatureCollection" assert len(self.g1.__geo_interface__["features"]) == self.g1.shape[0] def test_proj4strings(self): # As string reprojected = self.g3.to_crs("+proj=utm +zone=30") reprojected_back = reprojected.to_crs(epsg=4326) assert np.all(self.g3.geom_almost_equals(reprojected_back)) # As dict reprojected = self.g3.to_crs({"proj": "utm", "zone": "30"}) reprojected_back = reprojected.to_crs(epsg=4326) assert np.all(self.g3.geom_almost_equals(reprojected_back)) # Set to equivalent string, convert, compare to original copy = self.g3.copy() copy.crs = "epsg:4326" reprojected = copy.to_crs({"proj": "utm", "zone": "30"}) reprojected_back = reprojected.to_crs(epsg=4326) assert np.all(self.g3.geom_almost_equals(reprojected_back)) # Conversions by different format reprojected_string = self.g3.to_crs("+proj=utm +zone=30") reprojected_dict = self.g3.to_crs({"proj": "utm", "zone": "30"}) assert np.all(reprojected_string.geom_almost_equals(reprojected_dict)) def test_from_wkb(self): assert_geoseries_equal(self.g1, GeoSeries.from_wkb([self.t1.wkb, self.sq.wkb])) def test_from_wkb_series(self): s = pd.Series([self.t1.wkb, self.sq.wkb], index=[1, 2]) expected = self.g1.copy() expected.index = pd.Index([1, 2]) assert_geoseries_equal(expected, GeoSeries.from_wkb(s)) def test_from_wkb_series_with_index(self): index = [0] s = pd.Series([self.t1.wkb, self.sq.wkb], index=[0, 2]) expected = self.g1.reindex(index) assert_geoseries_equal(expected, GeoSeries.from_wkb(s, index=index)) def test_from_wkt(self): assert_geoseries_equal(self.g1, GeoSeries.from_wkt([self.t1.wkt, self.sq.wkt])) def test_from_wkt_series(self): s = pd.Series([self.t1.wkt, self.sq.wkt], index=[1, 2]) expected = self.g1.copy() expected.index = pd.Index([1, 2]) assert_geoseries_equal(expected, GeoSeries.from_wkt(s)) def test_from_wkt_series_with_index(self): index = [0] s = pd.Series([self.t1.wkt, self.sq.wkt], index=[0, 2]) expected = self.g1.reindex(index) assert_geoseries_equal(expected, GeoSeries.from_wkt(s, index=index)) def test_to_wkb(self): assert_series_equal(pd.Series([self.t1.wkb, self.sq.wkb]), self.g1.to_wkb()) assert_series_equal( pd.Series([self.t1.wkb_hex, self.sq.wkb_hex]), self.g1.to_wkb(hex=True) ) def test_to_wkt(self): assert_series_equal(pd.Series([self.t1.wkt, self.sq.wkt]), self.g1.to_wkt()) @pytest.mark.skip_no_sindex def test_clip(self): left = read_file(datasets.get_path("naturalearth_cities")) world = read_file(datasets.get_path("naturalearth_lowres")) south_america = world[world["continent"] == "South America"] expected = clip(left.geometry, south_america) result = left.geometry.clip(south_america) assert_geoseries_equal(result, expected) def test_from_xy_points(self): x = self.landmarks.x.values y = self.landmarks.y.values index = self.landmarks.index.tolist() crs = self.landmarks.crs assert_geoseries_equal( self.landmarks, GeoSeries.from_xy(x, y, index=index, crs=crs) ) assert_geoseries_equal( self.landmarks, GeoSeries.from_xy(self.landmarks.x, self.landmarks.y, crs=crs), ) def test_from_xy_points_w_z(self): index_values = [5, 6, 7] x = pd.Series([0, -1, 2], index=index_values) y = pd.Series([8, 3, 1], index=index_values) z = pd.Series([5, -6, 7], index=index_values) expected = GeoSeries( [Point(0, 8, 5), Point(-1, 3, -6), Point(2, 1, 7)], index=index_values ) assert_geoseries_equal(expected, GeoSeries.from_xy(x, y, z)) def test_from_xy_points_unequal_index(self): x = self.landmarks.x y = self.landmarks.y y.index = -np.arange(len(y)) crs = self.landmarks.crs assert_geoseries_equal( self.landmarks, GeoSeries.from_xy(x, y, index=x.index, crs=crs) ) unindexed_landmarks = self.landmarks.copy() unindexed_landmarks.reset_index(inplace=True, drop=True) assert_geoseries_equal( unindexed_landmarks, GeoSeries.from_xy(x, y, crs=crs), ) def test_from_xy_points_indexless(self): x = np.array([0.0, 3.0]) y = np.array([2.0, 5.0]) z = np.array([-1.0, 4.0]) expected = GeoSeries([Point(0, 2, -1), Point(3, 5, 4)]) assert_geoseries_equal(expected, GeoSeries.from_xy(x, y, z)) def test_missing_values_empty_warning(): s = GeoSeries([Point(1, 1), None, np.nan, BaseGeometry(), Polygon()]) with pytest.warns(UserWarning): s.isna() with pytest.warns(UserWarning): s.notna() @pytest.mark.filterwarnings("ignore::UserWarning") def test_missing_values(): s = GeoSeries([Point(1, 1), None, np.nan, BaseGeometry(), Polygon()]) # construction -> missing values get normalized to None assert s[1] is None assert s[2] is None assert s[3].is_empty assert s[4].is_empty # isna / is_empty assert s.isna().tolist() == [False, True, True, False, False] assert s.is_empty.tolist() == [False, False, False, True, True] assert s.notna().tolist() == [True, False, False, True, True] # fillna defaults to fill with empty geometry -> no missing values anymore assert not s.fillna().isna().any() # dropna drops the missing values assert not s.dropna().isna().any() assert len(s.dropna()) == 3 def test_isna_empty_geoseries(): # ensure that isna() result for emtpy GeoSeries has the correct bool dtype s = GeoSeries([]) result = s.isna() assert_series_equal(result, pd.Series([], dtype="bool")) def test_geoseries_crs(): gs = GeoSeries() gs.crs = "IGNF:ETRS89UTM28" assert gs.crs.to_authority() == ("IGNF", "ETRS89UTM28") # ----------------------------------------------------------------------------- # # Constructor tests # ----------------------------------------------------------------------------- def check_geoseries(s): assert isinstance(s, GeoSeries) assert isinstance(s.geometry, GeoSeries) assert isinstance(s.dtype, GeometryDtype) assert isinstance(s.values, GeometryArray) class TestConstructor: def test_constructor(self): s = GeoSeries([Point(x, x) for x in range(3)]) check_geoseries(s) def test_single_geom_constructor(self): p = Point(1, 2) line = LineString([(2, 3), (4, 5), (5, 6)]) poly = Polygon( [(0, 0), (1, 0), (1, 1), (0, 1)], [[(0.1, 0.1), (0.9, 0.1), (0.9, 0.9)]] ) mp = MultiPoint([(1, 2), (3, 4), (5, 6)]) mline = MultiLineString([[(1, 2), (3, 4), (5, 6)], [(7, 8), (9, 10)]]) poly2 = Polygon( [(0, 0), (0, -1), (-1, -1), (-1, 0)], [[(-0.1, -0.1), (-0.1, -0.5), (-0.5, -0.5), (-0.5, -0.1)]], ) mpoly = MultiPolygon([poly, poly2]) geoms = [p, line, poly, mp, mline, mpoly] index = ["a", "b", "c", "d"] for g in geoms: gs = GeoSeries(g) assert len(gs) == 1 # accessing elements no longer give identical objects assert gs.iloc[0].equals(g) gs = GeoSeries(g, index=index) assert len(gs) == len(index) for x in gs: assert x.equals(g) def test_no_geometries_fallback(self): with pytest.warns(FutureWarning): s = GeoSeries([True, False, True]) assert not isinstance(s, GeoSeries) assert type(s) == pd.Series with pytest.warns(FutureWarning): s = GeoSeries(["a", "b", "c"]) assert not isinstance(s, GeoSeries) assert type(s) == pd.Series with pytest.warns(FutureWarning): s = GeoSeries([[1, 2], [3, 4]]) assert not isinstance(s, GeoSeries) assert type(s) == pd.Series def test_empty(self): s = GeoSeries([]) check_geoseries(s) s = GeoSeries() check_geoseries(s) def test_data_is_none(self): s = GeoSeries(index=range(3)) check_geoseries(s) def test_empty_array(self): # with empty data that have an explicit dtype, we use the fallback or # not depending on the dtype arr = np.array([], dtype="bool") # dtypes that can never hold geometry-like data for arr in [ np.array([], dtype="bool"), np.array([], dtype="int64"), np.array([], dtype="float32"), # this gets converted to object dtype by pandas # np.array([], dtype="str"), ]: with pytest.warns(FutureWarning): s = GeoSeries(arr) assert not isinstance(s, GeoSeries) assert type(s) == pd.Series # dtypes that can potentially hold geometry-like data (object) or # can come from empty data (float64) for arr in [ np.array([], dtype="object"), np.array([], dtype="float64"), np.array([], dtype="str"), ]: with pytest.warns(None) as record: s = GeoSeries(arr) assert not record assert isinstance(s, GeoSeries) def test_from_series(self): shapes = [ Polygon([(random.random(), random.random()) for _ in range(3)]) for _ in range(10) ] with ignore_shapely2_warnings(): # the warning here is not suppressed by GeoPandas, as this is a pure # pandas construction call s = pd.Series(shapes, index=list("abcdefghij"), name="foo") g = GeoSeries(s) check_geoseries(g) assert [a.equals(b) for a, b in zip(s, g)] assert s.name == g.name assert s.index is g.index # GH 1216 def test_expanddim(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ) s = s.explode(index_parts=True) df = s.reset_index() assert type(df) == GeoDataFrame def test_explode_without_multiindex(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ) s = s.explode(index_parts=False) expected_index = pd.Index([0, 0, 1, 1, 1]) assert_index_equal(s.index, expected_index) def test_explode_ignore_index(self): s = GeoSeries( [MultiPoint([(0, 0), (1, 1)]), MultiPoint([(2, 2), (3, 3), (4, 4)])] ) s = s.explode(ignore_index=True) expected_index = pd.Index(range(len(s))) print(expected_index) assert_index_equal(s.index, expected_index) # index_parts is ignored if ignore_index=True s = s.explode(index_parts=True, ignore_index=True) assert_index_equal(s.index, expected_index) geopandas-0.10.2/geopandas/tests/test_merge.py000066400000000000000000000073451413247402600213620ustar00rootroot00000000000000import pandas as pd import pytest from geopandas.testing import assert_geodataframe_equal from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries class TestMerging: def setup_method(self): self.gseries = GeoSeries([Point(i, i) for i in range(3)]) self.series = pd.Series([1, 2, 3]) self.gdf = GeoDataFrame({"geometry": self.gseries, "values": range(3)}) self.df = pd.DataFrame({"col1": [1, 2, 3], "col2": [0.1, 0.2, 0.3]}) def _check_metadata(self, gdf, geometry_column_name="geometry", crs=None): assert gdf._geometry_column_name == geometry_column_name assert gdf.crs == crs def test_merge(self): res = self.gdf.merge(self.df, left_on="values", right_on="col1") # check result is a GeoDataFrame assert isinstance(res, GeoDataFrame) # check geometry property gives GeoSeries assert isinstance(res.geometry, GeoSeries) # check metadata self._check_metadata(res) # test that crs and other geometry name are preserved self.gdf.crs = "epsg:4326" self.gdf = self.gdf.rename(columns={"geometry": "points"}).set_geometry( "points" ) res = self.gdf.merge(self.df, left_on="values", right_on="col1") assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res, "points", self.gdf.crs) def test_concat_axis0(self): # frame res = pd.concat([self.gdf, self.gdf]) assert res.shape == (6, 2) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res) exp = GeoDataFrame(pd.concat([pd.DataFrame(self.gdf), pd.DataFrame(self.gdf)])) assert_geodataframe_equal(exp, res) # check metadata comes from first gdf res4 = pd.concat([self.gdf.set_crs("epsg:4326"), self.gdf], axis=0) # Note: this behaviour potentially does not make sense. If geom cols are # concatenated but have different CRS, then the CRS will be overridden. self._check_metadata(res4, crs="epsg:4326") # series res = pd.concat([self.gdf.geometry, self.gdf.geometry]) assert res.shape == (6,) assert isinstance(res, GeoSeries) assert isinstance(res.geometry, GeoSeries) def test_concat_axis1(self): res = pd.concat([self.gdf, self.df], axis=1) assert res.shape == (3, 4) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) self._check_metadata(res) def test_concat_axis1_multiple_geodataframes(self): # https://github.com/geopandas/geopandas/issues/1230 # Expect that concat should fail gracefully if duplicate column names belonging # to geometry columns are introduced. expected_err = ( "GeoDataFrame does not support multiple columns using the geometry" " column name 'geometry'" ) with pytest.raises(ValueError, match=expected_err): pd.concat([self.gdf, self.gdf], axis=1) # Check case is handled if custom geometry column name is used df2 = self.gdf.rename_geometry("geom") expected_err2 = ( "Concat operation has resulted in multiple columns using the geometry " "column name 'geom'." ) with pytest.raises(ValueError, match=expected_err2): pd.concat([df2, df2], axis=1) # Check that two geometry columns is fine, if they have different names res3 = pd.concat([df2.set_crs("epsg:4326"), self.gdf], axis=1) # check metadata comes from first df self._check_metadata(res3, geometry_column_name="geom", crs="epsg:4326") geopandas-0.10.2/geopandas/tests/test_overlay.py000066400000000000000000000631571413247402600217470ustar00rootroot00000000000000import os from distutils.version import LooseVersion import numpy as np import pandas as pd from shapely.geometry import Point, Polygon, LineString, GeometryCollection, box from fiona.errors import DriverError import geopandas from geopandas import GeoDataFrame, GeoSeries, overlay, read_file from geopandas import _compat from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal import pytest DATA = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data", "overlay") pytestmark = pytest.mark.skip_no_sindex pandas_133 = pd.__version__ == LooseVersion("1.3.3") @pytest.fixture def dfs(request): s1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}) df2 = GeoDataFrame({"col2": [1, 2], "geometry": s2}) return df1, df2 @pytest.fixture(params=["default-index", "int-index", "string-index"]) def dfs_index(request, dfs): df1, df2 = dfs if request.param == "int-index": df1.index = [1, 2] df2.index = [0, 2] if request.param == "string-index": df1.index = ["row1", "row2"] return df1, df2 @pytest.fixture( params=["union", "intersection", "difference", "symmetric_difference", "identity"] ) def how(request): if pandas_133 and request.param in ["symmetric_difference", "identity", "union"]: pytest.xfail("Regression in pandas 1.3.3 (GH #2101)") return request.param @pytest.fixture(params=[True, False]) def keep_geom_type(request): return request.param def test_overlay(dfs_index, how): """ Basic overlay test with small dummy example dataframes (from docs). Results obtained using QGIS 2.16 (Vector -> Geoprocessing Tools -> Intersection / Union / ...), saved to GeoJSON """ df1, df2 = dfs_index result = overlay(df1, df2, how=how) # construction of result def _read(name): expected = read_file( os.path.join(DATA, "polys", "df1_df2-{0}.geojson".format(name)) ) expected.crs = None return expected if how == "identity": expected_intersection = _read("intersection") expected_difference = _read("difference") expected = pd.concat( [expected_intersection, expected_difference], ignore_index=True, sort=False ) expected["col1"] = expected["col1"].astype(float) else: expected = _read(how) # TODO needed adaptations to result if how == "union": result = result.sort_values(["col1", "col2"]).reset_index(drop=True) elif how == "difference": result = result.reset_index(drop=True) assert_geodataframe_equal(result, expected, check_column_type=False) # for difference also reversed if how == "difference": result = overlay(df2, df1, how=how) result = result.reset_index(drop=True) expected = _read("difference-inverse") assert_geodataframe_equal(result, expected, check_column_type=False) @pytest.mark.filterwarnings("ignore:GeoSeries crs mismatch:UserWarning") def test_overlay_nybb(how): polydf = read_file(geopandas.datasets.get_path("nybb")) # The circles have been constructed and saved at the time the expected # results were created (exact output of buffer algorithm can slightly # change over time -> use saved ones) # # construct circles dataframe # N = 10 # b = [int(x) for x in polydf.total_bounds] # polydf2 = GeoDataFrame( # [ # {"geometry": Point(x, y).buffer(10000), "value1": x + y, "value2": x - y} # for x, y in zip( # range(b[0], b[2], int((b[2] - b[0]) / N)), # range(b[1], b[3], int((b[3] - b[1]) / N)), # ) # ], # crs=polydf.crs, # ) polydf2 = read_file(os.path.join(DATA, "nybb_qgis", "polydf2.shp")) result = overlay(polydf, polydf2, how=how) cols = ["BoroCode", "BoroName", "Shape_Leng", "Shape_Area", "value1", "value2"] if how == "difference": cols = cols[:-2] # expected result if how == "identity": # read union one, further down below we take the appropriate subset expected = read_file(os.path.join(DATA, "nybb_qgis", "qgis-union.shp")) else: expected = read_file( os.path.join(DATA, "nybb_qgis", "qgis-{0}.shp".format(how)) ) # The result of QGIS for 'union' contains incorrect geometries: # 24 is a full original circle overlapping with unioned geometries, and # 27 is a completely duplicated row) if how == "union": expected = expected.drop([24, 27]) expected.reset_index(inplace=True, drop=True) # Eliminate observations without geometries (issue from QGIS) expected = expected[expected.is_valid] expected.reset_index(inplace=True, drop=True) if how == "identity": expected = expected[expected.BoroCode.notnull()].copy() # Order GeoDataFrames expected = expected.sort_values(cols).reset_index(drop=True) # TODO needed adaptations to result result = result.sort_values(cols).reset_index(drop=True) if how in ("union", "identity"): # concat < 0.23 sorts, so changes the order of the columns # but at least we ensure 'geometry' is the last column assert result.columns[-1] == "geometry" assert len(result.columns) == len(expected.columns) result = result.reindex(columns=expected.columns) # the ordering of the spatial index results causes slight deviations # in the resultant geometries for multipolygons # for more details on the discussion, see: # https://github.com/geopandas/geopandas/pull/1338 # https://github.com/geopandas/geopandas/issues/1337 # Temporary workaround below: # simplify multipolygon geometry comparison # since the order of the constituent polygons depends on # the ordering of spatial indexing results, we cannot # compare symmetric_difference results directly when the # resultant geometry is a multipolygon # first, check that all bounds and areas are approx equal # this is a very rough check for multipolygon equality if not _compat.PANDAS_GE_11: kwargs = dict(check_less_precise=True) else: kwargs = {} pd.testing.assert_series_equal( result.geometry.area, expected.geometry.area, **kwargs ) pd.testing.assert_frame_equal( result.geometry.bounds, expected.geometry.bounds, **kwargs ) # There are two cases where the multipolygon have a different number # of sub-geometries -> not solved by normalize (and thus drop for now) if how == "symmetric_difference": expected.loc[9, "geometry"] = None result.loc[9, "geometry"] = None if how == "union": expected.loc[24, "geometry"] = None result.loc[24, "geometry"] = None assert_geodataframe_equal( result, expected, normalize=True, check_crs=False, check_column_type=False, check_less_precise=True, ) def test_overlay_overlap(how): """ Overlay test with overlapping geometries in both dataframes. Test files are created with:: import geopandas from geopandas import GeoSeries, GeoDataFrame from shapely.geometry import Point, Polygon, LineString s1 = GeoSeries([Point(0, 0), Point(1.5, 0)]).buffer(1, resolution=2) s2 = GeoSeries([Point(1, 1), Point(2, 2)]).buffer(1, resolution=2) df1 = GeoDataFrame({'geometry': s1, 'col1':[1,2]}) df2 = GeoDataFrame({'geometry': s2, 'col2':[1, 2]}) ax = df1.plot(alpha=0.5) df2.plot(alpha=0.5, ax=ax, color='C1') df1.to_file('geopandas/geopandas/tests/data/df1_overlap.geojson', driver='GeoJSON') df2.to_file('geopandas/geopandas/tests/data/df2_overlap.geojson', driver='GeoJSON') and then overlay results are obtained from using QGIS 2.16 (Vector -> Geoprocessing Tools -> Intersection / Union / ...), saved to GeoJSON. """ df1 = read_file(os.path.join(DATA, "overlap", "df1_overlap.geojson")) df2 = read_file(os.path.join(DATA, "overlap", "df2_overlap.geojson")) result = overlay(df1, df2, how=how) if how == "identity": raise pytest.skip() expected = read_file( os.path.join(DATA, "overlap", "df1_df2_overlap-{0}.geojson".format(how)) ) if how == "union": # the QGIS result has the last row duplicated, so removing this expected = expected.iloc[:-1] # TODO needed adaptations to result result = result.reset_index(drop=True) if how == "union": result = result.sort_values(["col1", "col2"]).reset_index(drop=True) assert_geodataframe_equal( result, expected, normalize=True, check_column_type=False, check_less_precise=True, ) @pytest.mark.parametrize("other_geometry", [False, True]) def test_geometry_not_named_geometry(dfs, how, other_geometry): # Issue #306 # Add points and flip names df1, df2 = dfs df3 = df1.copy() df3 = df3.rename(columns={"geometry": "polygons"}) df3 = df3.set_geometry("polygons") if other_geometry: df3["geometry"] = df1.centroid.geometry assert df3.geometry.name == "polygons" res1 = overlay(df1, df2, how=how) res2 = overlay(df3, df2, how=how) assert df3.geometry.name == "polygons" if how == "difference": # in case of 'difference', column names of left frame are preserved assert res2.geometry.name == "polygons" if other_geometry: assert "geometry" in res2.columns assert_geoseries_equal( res2["geometry"], df3["geometry"], check_series_type=False ) res2 = res2.drop(["geometry"], axis=1) res2 = res2.rename(columns={"polygons": "geometry"}) res2 = res2.set_geometry("geometry") # TODO if existing column is overwritten -> geometry not last column if other_geometry and how == "intersection": res2 = res2.reindex(columns=res1.columns) assert_geodataframe_equal(res1, res2) df4 = df2.copy() df4 = df4.rename(columns={"geometry": "geom"}) df4 = df4.set_geometry("geom") if other_geometry: df4["geometry"] = df2.centroid.geometry assert df4.geometry.name == "geom" res1 = overlay(df1, df2, how=how) res2 = overlay(df1, df4, how=how) assert_geodataframe_equal(res1, res2) def test_bad_how(dfs): df1, df2 = dfs with pytest.raises(ValueError): overlay(df1, df2, how="spandex") def test_duplicate_column_name(dfs, how): if how == "difference": pytest.skip("Difference uses columns from one df only.") df1, df2 = dfs df2r = df2.rename(columns={"col2": "col1"}) res = overlay(df1, df2r, how=how) assert ("col1_1" in res.columns) and ("col1_2" in res.columns) def test_geoseries_warning(dfs): df1, df2 = dfs # Issue #305 with pytest.raises(NotImplementedError): overlay(df1, df2.geometry, how="union") def test_preserve_crs(dfs, how): df1, df2 = dfs result = overlay(df1, df2, how=how) assert result.crs is None crs = "epsg:4326" df1.crs = crs df2.crs = crs result = overlay(df1, df2, how=how) assert result.crs == crs def test_crs_mismatch(dfs, how): df1, df2 = dfs df1.crs = 4326 df2.crs = 3857 with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): overlay(df1, df2, how=how) def test_empty_intersection(dfs): df1, df2 = dfs polys3 = GeoSeries( [ Polygon([(-1, -1), (-3, -1), (-3, -3), (-1, -3)]), Polygon([(-3, -3), (-5, -3), (-5, -5), (-3, -5)]), ] ) df3 = GeoDataFrame({"geometry": polys3, "col3": [1, 2]}) expected = GeoDataFrame([], columns=["col1", "col3", "geometry"]) result = overlay(df1, df3) assert_geodataframe_equal(result, expected, check_dtype=False) def test_correct_index(dfs): # GH883 - case where the index was not properly reset df1, df2 = dfs polys3 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df3 = GeoDataFrame({"geometry": polys3, "col3": [1, 2, 3]}) i1 = Polygon([(1, 1), (1, 3), (3, 3), (3, 1), (1, 1)]) i2 = Polygon([(3, 3), (3, 5), (5, 5), (5, 3), (3, 3)]) expected = GeoDataFrame( [[1, 1, i1], [3, 2, i2]], columns=["col3", "col2", "geometry"] ) result = overlay(df3, df2, keep_geom_type=True) assert_geodataframe_equal(result, expected) def test_warn_on_keep_geom_type(dfs): df1, df2 = dfs polys3 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df3 = GeoDataFrame({"geometry": polys3}) with pytest.warns(UserWarning, match="`keep_geom_type=True` in overlay"): overlay(df2, df3, keep_geom_type=None) @pytest.mark.parametrize( "geom_types", ["polys", "poly_line", "poly_point", "line_poly", "point_poly"] ) def test_overlay_strict(how, keep_geom_type, geom_types): """ Test of mixed geometry types on input and output. Expected results initially generated using following snippet. polys1 = gpd.GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])]) df1 = gpd.GeoDataFrame({'col1': [1, 2], 'geometry': polys1}) polys2 = gpd.GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])]) df2 = gpd.GeoDataFrame({'geometry': polys2, 'col2': [1, 2, 3]}) lines1 = gpd.GeoSeries([LineString([(2, 0), (2, 4), (6, 4)]), LineString([(0, 3), (6, 3)])]) df3 = gpd.GeoDataFrame({'col3': [1, 2], 'geometry': lines1}) points1 = gpd.GeoSeries([Point((2, 2)), Point((3, 3))]) df4 = gpd.GeoDataFrame({'col4': [1, 2], 'geometry': points1}) params=["union", "intersection", "difference", "symmetric_difference", "identity"] stricts = [True, False] for p in params: for s in stricts: exp = gpd.overlay(df1, df2, how=p, keep_geom_type=s) if not exp.empty: exp.to_file('polys_{p}_{s}.geojson'.format(p=p, s=s), driver='GeoJSON') for p in params: for s in stricts: exp = gpd.overlay(df1, df3, how=p, keep_geom_type=s) if not exp.empty: exp.to_file('poly_line_{p}_{s}.geojson'.format(p=p, s=s), driver='GeoJSON') for p in params: for s in stricts: exp = gpd.overlay(df1, df4, how=p, keep_geom_type=s) if not exp.empty: exp.to_file('poly_point_{p}_{s}.geojson'.format(p=p, s=s), driver='GeoJSON') """ polys1 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": polys1}) polys2 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df2 = GeoDataFrame({"geometry": polys2, "col2": [1, 2, 3]}) lines1 = GeoSeries( [LineString([(2, 0), (2, 4), (6, 4)]), LineString([(0, 3), (6, 3)])] ) df3 = GeoDataFrame({"col3": [1, 2], "geometry": lines1}) points1 = GeoSeries([Point((2, 2)), Point((3, 3))]) df4 = GeoDataFrame({"col4": [1, 2], "geometry": points1}) if geom_types == "polys": result = overlay(df1, df2, how=how, keep_geom_type=keep_geom_type) elif geom_types == "poly_line": result = overlay(df1, df3, how=how, keep_geom_type=keep_geom_type) elif geom_types == "poly_point": result = overlay(df1, df4, how=how, keep_geom_type=keep_geom_type) elif geom_types == "line_poly": result = overlay(df3, df1, how=how, keep_geom_type=keep_geom_type) elif geom_types == "point_poly": result = overlay(df4, df1, how=how, keep_geom_type=keep_geom_type) try: expected = read_file( os.path.join( DATA, "strict", "{t}_{h}_{s}.geojson".format(t=geom_types, h=how, s=keep_geom_type), ) ) # the order depends on the spatial index used # so we sort the resultant dataframes to get a consistent order # independently of the spatial index implementation assert all(expected.columns == result.columns), "Column name mismatch" cols = list(set(result.columns) - set(["geometry"])) expected = expected.sort_values(cols, axis=0).reset_index(drop=True) result = result.sort_values(cols, axis=0).reset_index(drop=True) assert_geodataframe_equal( result, expected, normalize=True, check_column_type=False, check_less_precise=True, check_crs=False, check_dtype=False, ) except DriverError: # fiona >= 1.8 assert result.empty except OSError: # fiona < 1.8 assert result.empty def test_mixed_geom_error(): polys1 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": polys1}) mixed = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), LineString([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) dfmixed = GeoDataFrame({"col1": [1, 2], "geometry": mixed}) with pytest.raises(NotImplementedError): overlay(df1, dfmixed, keep_geom_type=True) def test_keep_geom_type_error(): gcol = GeoSeries( GeometryCollection( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), LineString([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) ) dfcol = GeoDataFrame({"col1": [2], "geometry": gcol}) polys1 = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) df1 = GeoDataFrame({"col1": [1, 2], "geometry": polys1}) with pytest.raises(TypeError): overlay(dfcol, df1, keep_geom_type=True) def test_keep_geom_type_geometry_collection(): # GH 1581 df1 = read_file(os.path.join(DATA, "geom_type", "df1.geojson")) df2 = read_file(os.path.join(DATA, "geom_type", "df2.geojson")) with pytest.warns(UserWarning, match="`keep_geom_type=True` in overlay"): intersection = overlay(df1, df2, keep_geom_type=None) assert len(intersection) == 1 assert (intersection.geom_type == "Polygon").all() intersection = overlay(df1, df2, keep_geom_type=True) assert len(intersection) == 1 assert (intersection.geom_type == "Polygon").all() intersection = overlay(df1, df2, keep_geom_type=False) assert len(intersection) == 1 assert (intersection.geom_type == "GeometryCollection").all() def test_keep_geom_type_geometry_collection2(): polys1 = [ box(0, 0, 1, 1), box(1, 1, 3, 3).union(box(1, 3, 5, 5)), ] polys2 = [ box(0, 0, 1, 1), box(3, 1, 4, 2).union(box(4, 1, 5, 4)), ] df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1}) df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2}) result1 = overlay(df1, df2, keep_geom_type=True) expected1 = GeoDataFrame( { "left": [0, 1], "right": [0, 1], "geometry": [box(0, 0, 1, 1), box(4, 3, 5, 4)], } ) assert_geodataframe_equal(result1, expected1) result1 = overlay(df1, df2, keep_geom_type=False) expected1 = GeoDataFrame( { "left": [0, 1, 1], "right": [0, 0, 1], "geometry": [ box(0, 0, 1, 1), Point(1, 1), GeometryCollection([box(4, 3, 5, 4), LineString([(3, 1), (3, 2)])]), ], } ) assert_geodataframe_equal(result1, expected1) def test_keep_geom_type_geomcoll_different_types(): polys1 = [box(0, 1, 1, 3), box(10, 10, 12, 12)] polys2 = [ Polygon([(1, 0), (3, 0), (3, 3), (1, 3), (1, 2), (2, 2), (2, 1), (1, 1)]), box(11, 11, 13, 13), ] df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1}) df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2}) result1 = overlay(df1, df2, keep_geom_type=True) expected1 = GeoDataFrame( { "left": [1], "right": [1], "geometry": [box(11, 11, 12, 12)], } ) assert_geodataframe_equal(result1, expected1) result2 = overlay(df1, df2, keep_geom_type=False) expected2 = GeoDataFrame( { "left": [0, 1], "right": [0, 1], "geometry": [ GeometryCollection([LineString([(1, 2), (1, 3)]), Point(1, 1)]), box(11, 11, 12, 12), ], } ) assert_geodataframe_equal(result2, expected2) def test_keep_geom_type_geometry_collection_difference(): # GH 2163 polys1 = [ box(0, 0, 1, 1), box(1, 1, 2, 2), ] # the tiny sliver in the second geometry may be converted to a # linestring during the overlay process due to floating point errors # on some platforms polys2 = [ box(0, 0, 1, 1), box(1, 1, 2, 3).union(box(2, 2, 3, 2.00000000000000001)), ] df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1}) df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2}) result1 = overlay(df2, df1, keep_geom_type=True, how="difference") expected1 = GeoDataFrame( { "right": [1], "geometry": [box(1, 2, 2, 3)], }, ) assert_geodataframe_equal(result1, expected1) @pytest.mark.parametrize("make_valid", [True, False]) def test_overlap_make_valid(make_valid): bowtie = Polygon([(1, 1), (9, 9), (9, 1), (1, 9), (1, 1)]) assert not bowtie.is_valid fixed_bowtie = bowtie.buffer(0) assert fixed_bowtie.is_valid df1 = GeoDataFrame({"col1": ["region"], "geometry": GeoSeries([box(0, 0, 10, 10)])}) df_bowtie = GeoDataFrame( {"col1": ["invalid", "valid"], "geometry": GeoSeries([bowtie, fixed_bowtie])} ) if make_valid: df_overlay_bowtie = overlay(df1, df_bowtie, make_valid=make_valid) assert df_overlay_bowtie.at[0, "geometry"].equals(fixed_bowtie) assert df_overlay_bowtie.at[1, "geometry"].equals(fixed_bowtie) else: with pytest.raises(ValueError, match="1 invalid input geometries"): overlay(df1, df_bowtie, make_valid=make_valid) def test_empty_overlay_return_non_duplicated_columns(): nybb = geopandas.read_file(geopandas.datasets.get_path("nybb")) nybb2 = nybb.copy() nybb2.geometry = nybb2.translate(20000000) result = geopandas.overlay(nybb, nybb2) expected = GeoDataFrame( columns=[ "BoroCode_1", "BoroName_1", "Shape_Leng_1", "Shape_Area_1", "BoroCode_2", "BoroName_2", "Shape_Leng_2", "Shape_Area_2", "geometry", ], crs=nybb.crs, ) assert_geodataframe_equal(result, expected, check_dtype=False) def test_non_overlapping(how): p1 = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]) p2 = Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]) df1 = GeoDataFrame({"col1": [1], "geometry": [p1]}) df2 = GeoDataFrame({"col2": [2], "geometry": [p2]}) result = overlay(df1, df2, how=how) if how == "intersection": expected = GeoDataFrame( { "col1": np.array([], dtype="int64"), "col2": np.array([], dtype="int64"), "geometry": [], }, index=pd.Index([], dtype="object"), ) elif how == "union": expected = GeoDataFrame( { "col1": [1, np.nan], "col2": [np.nan, 2], "geometry": [p1, p2], } ) elif how == "identity": expected = GeoDataFrame( { "col1": [1.0], "col2": [np.nan], "geometry": [p1], } ) elif how == "symmetric_difference": expected = GeoDataFrame( { "col1": [1, np.nan], "col2": [np.nan, 2], "geometry": [p1, p2], } ) elif how == "difference": expected = GeoDataFrame( { "col1": [1], "geometry": [p1], } ) assert_geodataframe_equal(result, expected) def test_no_intersection(): # overlapping bounds but non-overlapping geometries gs = GeoSeries([Point(x, x).buffer(0.1) for x in range(3)]) gdf1 = GeoDataFrame({"foo": ["a", "b", "c"]}, geometry=gs) gdf2 = GeoDataFrame({"bar": ["1", "3", "5"]}, geometry=gs.translate(1)) expected = GeoDataFrame(columns=["foo", "bar", "geometry"]) result = overlay(gdf1, gdf2, how="intersection") assert_geodataframe_equal(result, expected, check_index_type=False) geopandas-0.10.2/geopandas/tests/test_pandas_methods.py000066400000000000000000000472011413247402600232470ustar00rootroot00000000000000import os import numpy as np from numpy.testing import assert_array_equal import pandas as pd import shapely from shapely.geometry import Point, GeometryCollection, LineString import geopandas from geopandas import GeoDataFrame, GeoSeries import geopandas._compat as compat from geopandas.array import from_shapely from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal from pandas.testing import assert_frame_equal, assert_series_equal import pytest @pytest.fixture def s(): return GeoSeries([Point(x, y) for x, y in zip(range(3), range(3))]) @pytest.fixture def df(): return GeoDataFrame( { "geometry": [Point(x, x) for x in range(3)], "value1": np.arange(3, dtype="int64"), "value2": np.array([1, 2, 1], dtype="int64"), } ) def test_repr(s, df): assert "POINT" in repr(s) assert "POINT" in repr(df) assert "POINT" in df._repr_html_() def test_repr_boxed_display_precision(): # geographic coordinates p1 = Point(10.123456789, 50.123456789) p2 = Point(4.123456789, 20.123456789) s1 = GeoSeries([p1, p2, None]) assert "POINT (10.12346 50.12346)" in repr(s1) # geographic coordinates 4326 s3 = GeoSeries([p1, p2], crs=4326) assert "POINT (10.12346 50.12346)" in repr(s3) # projected coordinates p1 = Point(3000.123456789, 3000.123456789) p2 = Point(4000.123456789, 4000.123456789) s2 = GeoSeries([p1, p2, None]) assert "POINT (3000.123 3000.123)" in repr(s2) # projected geographic coordinate s4 = GeoSeries([p1, p2], crs=3857) assert "POINT (3000.123 3000.123)" in repr(s4) geopandas.options.display_precision = 1 assert "POINT (10.1 50.1)" in repr(s1) geopandas.options.display_precision = 9 assert "POINT (10.123456789 50.123456789)" in repr(s1) def test_repr_all_missing(): # https://github.com/geopandas/geopandas/issues/1195 s = GeoSeries([None, None, None]) assert "None" in repr(s) df = GeoDataFrame({"a": [1, 2, 3], "geometry": s}) assert "None" in repr(df) assert "geometry" in df._repr_html_() def test_repr_empty(): # https://github.com/geopandas/geopandas/issues/1195 s = GeoSeries([]) assert repr(s) == "GeoSeries([], dtype: geometry)" df = GeoDataFrame({"a": [], "geometry": s}) assert "Empty GeoDataFrame" in repr(df) # https://github.com/geopandas/geopandas/issues/1184 assert "geometry" in df._repr_html_() def test_indexing(s, df): # accessing scalar from the geometry (colunm) exp = Point(1, 1) assert s[1] == exp assert s.loc[1] == exp assert s.iloc[1] == exp assert df.loc[1, "geometry"] == exp assert df.iloc[1, 0] == exp # multiple values exp = GeoSeries([Point(2, 2), Point(0, 0)], index=[2, 0]) assert_geoseries_equal(s.loc[[2, 0]], exp) assert_geoseries_equal(s.iloc[[2, 0]], exp) assert_geoseries_equal(s.reindex([2, 0]), exp) assert_geoseries_equal(df.loc[[2, 0], "geometry"], exp) # TODO here iloc does not return a GeoSeries assert_series_equal( df.iloc[[2, 0], 0], exp, check_series_type=False, check_names=False ) # boolean indexing exp = GeoSeries([Point(0, 0), Point(2, 2)], index=[0, 2]) mask = np.array([True, False, True]) assert_geoseries_equal(s[mask], exp) assert_geoseries_equal(s.loc[mask], exp) assert_geoseries_equal(df[mask]["geometry"], exp) assert_geoseries_equal(df.loc[mask, "geometry"], exp) # slices s.index = [1, 2, 3] exp = GeoSeries([Point(1, 1), Point(2, 2)], index=[2, 3]) assert_series_equal(s[1:], exp) assert_series_equal(s.iloc[1:], exp) assert_series_equal(s.loc[2:], exp) def test_reindex(s, df): # GeoSeries reindex res = s.reindex([1, 2, 3]) exp = GeoSeries([Point(1, 1), Point(2, 2), None], index=[1, 2, 3]) assert_geoseries_equal(res, exp) # GeoDataFrame reindex index res = df.reindex(index=[1, 2, 3]) assert_geoseries_equal(res.geometry, exp) # GeoDataFrame reindex columns res = df.reindex(columns=["value1", "geometry"]) assert isinstance(res, GeoDataFrame) assert isinstance(res.geometry, GeoSeries) assert_frame_equal(res, df[["value1", "geometry"]]) # TODO df.reindex(columns=['value1', 'value2']) still returns GeoDataFrame, # should it return DataFrame instead ? def test_take(s, df): inds = np.array([0, 2]) # GeoSeries take result = s.take(inds) expected = s.iloc[[0, 2]] assert isinstance(result, GeoSeries) assert_geoseries_equal(result, expected) # GeoDataFrame take axis 0 result = df.take(inds, axis=0) expected = df.iloc[[0, 2], :] assert isinstance(result, GeoDataFrame) assert_geodataframe_equal(result, expected) # GeoDataFrame take axis 1 df = df.reindex(columns=["value1", "value2", "geometry"]) # ensure consistent order result = df.take(inds, axis=1) expected = df[["value1", "geometry"]] assert isinstance(result, GeoDataFrame) assert_geodataframe_equal(result, expected) result = df.take(np.array([0, 1]), axis=1) expected = df[["value1", "value2"]] assert isinstance(result, pd.DataFrame) assert_frame_equal(result, expected) def test_take_empty(s, df): # ensure that index type is preserved in an empty take # https://github.com/geopandas/geopandas/issues/1190 inds = np.array([], dtype="int64") # use non-default index df.index = pd.date_range("2012-01-01", periods=len(df)) result = df.take(inds, axis=0) assert isinstance(result, GeoDataFrame) assert result.shape == (0, 3) assert isinstance(result.index, pd.DatetimeIndex) # the original bug report was an empty boolean mask for result in [df.loc[df["value1"] > 100], df[df["value1"] > 100]]: assert isinstance(result, GeoDataFrame) assert result.shape == (0, 3) assert isinstance(result.index, pd.DatetimeIndex) def test_assignment(s, df): exp = GeoSeries([Point(10, 10), Point(1, 1), Point(2, 2)]) s2 = s.copy() s2[0] = Point(10, 10) assert_geoseries_equal(s2, exp) s2 = s.copy() s2.loc[0] = Point(10, 10) assert_geoseries_equal(s2, exp) s2 = s.copy() s2.iloc[0] = Point(10, 10) assert_geoseries_equal(s2, exp) df2 = df.copy() df2.loc[0, "geometry"] = Point(10, 10) assert_geoseries_equal(df2["geometry"], exp) df2 = df.copy() df2.iloc[0, 0] = Point(10, 10) assert_geoseries_equal(df2["geometry"], exp) def test_assign(df): res = df.assign(new=1) exp = df.copy() exp["new"] = 1 assert isinstance(res, GeoDataFrame) assert_frame_equal(res, exp) def test_astype(s, df): # check geoseries functionality with pytest.raises(TypeError): s.astype(int) assert s.astype(str)[0] == "POINT (0 0)" res = s.astype(object) assert isinstance(res, pd.Series) and not isinstance(res, GeoSeries) assert res.dtype == object df = df.rename_geometry("geom_list") # check whether returned object is a geodataframe res = df.astype({"value1": float}) assert isinstance(res, GeoDataFrame) # check whether returned object is a datafrane res = df.astype(str) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) res = df.astype({"geom_list": str}) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) res = df.astype(object) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) assert res["geom_list"].dtype == object def test_astype_invalid_geodataframe(): # https://github.com/geopandas/geopandas/issues/1144 # a GeoDataFrame without geometry column should not error in astype df = GeoDataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) res = df.astype(object) assert isinstance(res, pd.DataFrame) and not isinstance(res, GeoDataFrame) assert res["a"].dtype == object @pytest.mark.xfail( not compat.PANDAS_GE_10, reason="Convert dtypes new in pandas 1.0", raises=NotImplementedError, ) def test_convert_dtypes(df): # https://github.com/geopandas/geopandas/issues/1870 # Test geometry col is first col, first, geom_col_name=geometry # (order is important in concat, used internally) res1 = df.convert_dtypes() # note res1 done first for pandas < 1 xfail check expected1 = GeoDataFrame( pd.DataFrame(df).convert_dtypes(), crs=df.crs, geometry=df.geometry.name ) # Checking type and metadata are right assert_geodataframe_equal(expected1, res1) # Test geom last, geom_col_name=geometry res2 = df[["value1", "value2", "geometry"]].convert_dtypes() assert_geodataframe_equal(expected1[["value1", "value2", "geometry"]], res2) # Test again with crs set and custom geom col name df2 = df.set_crs(epsg=4326).rename_geometry("points") expected2 = GeoDataFrame( pd.DataFrame(df2).convert_dtypes(), crs=df2.crs, geometry=df2.geometry.name ) res3 = df2.convert_dtypes() assert_geodataframe_equal(expected2, res3) # Test geom last, geom_col=geometry res4 = df2[["value1", "value2", "points"]].convert_dtypes() assert_geodataframe_equal(expected2[["value1", "value2", "points"]], res4) def test_to_csv(df): exp = ( "geometry,value1,value2\nPOINT (0 0),0,1\nPOINT (1 1),1,2\nPOINT (2 2),2,1\n" ).replace("\n", os.linesep) assert df.to_csv(index=False) == exp def test_numerical_operations(s, df): # df methods ignore the geometry column exp = pd.Series([3, 4], index=["value1", "value2"]) assert_series_equal(df.sum(), exp) # series methods raise error (not supported for geometry) with pytest.raises(TypeError): s.sum() with pytest.raises(TypeError): s.max() with pytest.raises((TypeError, ValueError)): # TODO: remove ValueError after pandas-dev/pandas#32749 s.idxmax() # numerical ops raise an error with pytest.raises(TypeError): df + 1 with pytest.raises((TypeError, AssertionError)): # TODO(pandas 0.23) remove AssertionError -> raised in 0.23 s + 1 # boolean comparisons work res = df == 100 exp = pd.DataFrame(False, index=df.index, columns=df.columns) assert_frame_equal(res, exp) def test_where(s): res = s.where(np.array([True, False, True])) exp = GeoSeries([Point(0, 0), None, Point(2, 2)]) assert_series_equal(res, exp) def test_select_dtypes(df): res = df.select_dtypes(include=[np.number]) exp = df[["value1", "value2"]] assert_frame_equal(res, exp) def test_equals(s, df): # https://github.com/geopandas/geopandas/issues/1420 s2 = s.copy() assert s.equals(s2) is True s2.iloc[0] = None assert s.equals(s2) is False df2 = df.copy() assert df.equals(df2) is True df2.loc[0, "geometry"] = Point(10, 10) assert df.equals(df2) is False df2 = df.copy() df2.loc[0, "value1"] = 10 assert df.equals(df2) is False # Missing values def test_fillna(s, df): s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) res = s2.fillna(Point(1, 1)) assert_geoseries_equal(res, s) # allow np.nan although this does not change anything # https://github.com/geopandas/geopandas/issues/1149 res = s2.fillna(np.nan) assert_geoseries_equal(res, s2) # raise exception if trying to fill missing geometry w/ non-geometry df2 = df.copy() df2["geometry"] = s2 res = df2.fillna(Point(1, 1)) assert_geodataframe_equal(res, df) with pytest.raises(NotImplementedError): df2.fillna(0) # allow non-geometry fill value if there are no missing values # https://github.com/geopandas/geopandas/issues/1149 df3 = df.copy() df3.loc[0, "value1"] = np.nan res = df3.fillna(0) assert_geodataframe_equal(res.astype({"value1": "int64"}), df) def test_dropna(): s2 = GeoSeries([Point(0, 0), None, Point(2, 2)]) res = s2.dropna() exp = s2.loc[[0, 2]] assert_geoseries_equal(res, exp) @pytest.mark.parametrize("NA", [None, np.nan]) def test_isna(NA): s2 = GeoSeries([Point(0, 0), NA, Point(2, 2)], index=[2, 4, 5], name="tt") exp = pd.Series([False, True, False], index=[2, 4, 5], name="tt") res = s2.isnull() assert type(res) == pd.Series assert_series_equal(res, exp) res = s2.isna() assert_series_equal(res, exp) res = s2.notnull() assert_series_equal(res, ~exp) res = s2.notna() assert_series_equal(res, ~exp) # Any / all def test_any_all(): empty = GeometryCollection([]) s = GeoSeries([empty, Point(1, 1)]) assert not s.all() assert s.any() s = GeoSeries([Point(1, 1), Point(1, 1)]) assert s.all() assert s.any() s = GeoSeries([empty, empty]) assert not s.all() assert not s.any() # Groupby / algos def test_unique(): s = GeoSeries([Point(0, 0), Point(0, 0), Point(2, 2)]) exp = from_shapely([Point(0, 0), Point(2, 2)]) # TODO should have specialized GeometryArray assert method assert_array_equal(s.unique(), exp) def test_value_counts(): # each object is considered unique s = GeoSeries([Point(0, 0), Point(1, 1), Point(0, 0)]) res = s.value_counts() with compat.ignore_shapely2_warnings(): exp = pd.Series([2, 1], index=[Point(0, 0), Point(1, 1)]) assert_series_equal(res, exp) # Check crs doesn't make a difference - note it is not kept in output index anyway s2 = GeoSeries([Point(0, 0), Point(1, 1), Point(0, 0)], crs="EPSG:4326") res2 = s2.value_counts() assert_series_equal(res2, exp) # check mixed geometry s3 = GeoSeries([Point(0, 0), LineString([[1, 1], [2, 2]]), Point(0, 0)]) res3 = s3.value_counts() with compat.ignore_shapely2_warnings(): exp3 = pd.Series([2, 1], index=[Point(0, 0), LineString([[1, 1], [2, 2]])]) assert_series_equal(res3, exp3) # check None is handled s4 = GeoSeries([Point(0, 0), None, Point(0, 0)]) res4 = s4.value_counts(dropna=True) with compat.ignore_shapely2_warnings(): exp4_dropna = pd.Series([2], index=[Point(0, 0)]) assert_series_equal(res4, exp4_dropna) with compat.ignore_shapely2_warnings(): exp4_keepna = pd.Series([2, 1], index=[Point(0, 0), None]) res4_keepna = s4.value_counts(dropna=False) assert_series_equal(res4_keepna, exp4_keepna) @pytest.mark.xfail(strict=False) def test_drop_duplicates_series(): # duplicated does not yet use EA machinery # (https://github.com/pandas-dev/pandas/issues/27264) # but relies on unstable hashing of unhashable objects in numpy array # giving flaky test (https://github.com/pandas-dev/pandas/issues/27035) dups = GeoSeries([Point(0, 0), Point(0, 0)]) dropped = dups.drop_duplicates() assert len(dropped) == 1 @pytest.mark.xfail(strict=False) def test_drop_duplicates_frame(): # duplicated does not yet use EA machinery, see above gdf_len = 3 dup_gdf = GeoDataFrame( {"geometry": [Point(0, 0) for _ in range(gdf_len)], "value1": range(gdf_len)} ) dropped_geometry = dup_gdf.drop_duplicates(subset="geometry") assert len(dropped_geometry) == 1 dropped_all = dup_gdf.drop_duplicates() assert len(dropped_all) == gdf_len def test_groupby(df): # counts work fine res = df.groupby("value2").count() exp = pd.DataFrame( {"geometry": [2, 1], "value1": [2, 1], "value2": [1, 2]} ).set_index("value2") assert_frame_equal(res, exp) # reductions ignore geometry column res = df.groupby("value2").sum() exp = pd.DataFrame({"value1": [2, 1], "value2": [1, 2]}, dtype="int64").set_index( "value2" ) assert_frame_equal(res, exp) # applying on the geometry column res = df.groupby("value2")["geometry"].apply(lambda x: x.unary_union) if compat.PANDAS_GE_11: exp = GeoSeries( [shapely.geometry.MultiPoint([(0, 0), (2, 2)]), Point(1, 1)], index=pd.Index([1, 2], name="value2"), name="geometry", ) else: exp = pd.Series( [shapely.geometry.MultiPoint([(0, 0), (2, 2)]), Point(1, 1)], index=pd.Index([1, 2], name="value2"), name="geometry", ) assert_series_equal(res, exp) # apply on geometry column not resulting in new geometry res = df.groupby("value2")["geometry"].apply(lambda x: x.unary_union.area) exp = pd.Series([0.0, 0.0], index=pd.Index([1, 2], name="value2"), name="geometry") assert_series_equal(res, exp) def test_groupby_groups(df): g = df.groupby("value2") res = g.get_group(1) assert isinstance(res, GeoDataFrame) exp = df.loc[[0, 2]] assert_frame_equal(res, exp) def test_apply(s): # function that returns geometry preserves GeoSeries class def geom_func(geom): assert isinstance(geom, Point) return geom result = s.apply(geom_func) assert isinstance(result, GeoSeries) assert_geoseries_equal(result, s) # function that returns non-geometry results in Series def numeric_func(geom): assert isinstance(geom, Point) return geom.x result = s.apply(numeric_func) assert not isinstance(result, GeoSeries) assert_series_equal(result, pd.Series([0.0, 1.0, 2.0])) def test_apply_loc_len1(df): # subset of len 1 with loc -> bug in pandas with inconsistent Block ndim # resulting in bug in apply # https://github.com/geopandas/geopandas/issues/1078 subset = df.loc[[0], "geometry"] result = subset.apply(lambda geom: geom.is_empty) expected = subset.is_empty np.testing.assert_allclose(result, expected) def test_apply_convert_dtypes_keyword(s): # ensure the convert_dtypes keyword is accepted res = s.apply(lambda x: x, convert_dtype=True, args=()) assert_geoseries_equal(res, s) @pytest.mark.parametrize("crs", [None, "EPSG:4326"]) def test_apply_no_geometry_result(df, crs): if crs: df = df.set_crs(crs) result = df.apply(lambda col: col.astype(str), axis=0) # TODO this should actually not return a GeoDataFrame assert isinstance(result, GeoDataFrame) expected = df.astype(str) assert_frame_equal(result, expected) result = df.apply(lambda col: col.astype(str), axis=1) assert isinstance(result, GeoDataFrame) assert_frame_equal(result, expected) def test_apply_preserves_geom_col_name(df): df = df.rename_geometry("geom") result = df.apply(lambda col: col, axis=0) assert result.geometry.name == "geom" @pytest.mark.skipif(not compat.PANDAS_GE_10, reason="attrs introduced in pandas 1.0") def test_preserve_attrs(df): # https://github.com/geopandas/geopandas/issues/1654 df.attrs["name"] = "my_name" attrs = {"name": "my_name"} assert df.attrs == attrs # preserve attrs in indexing operations for subset in [df[:2], df[df["value1"] > 2], df[["value2", "geometry"]]]: assert df.attrs == attrs # preserve attrs in methods df2 = df.reset_index() assert df2.attrs == attrs # https://github.com/geopandas/geopandas/issues/1875 df3 = df2.explode(index_parts=True) assert df3.attrs == attrs @pytest.mark.skipif(not compat.PANDAS_GE_12, reason="attrs introduced in pandas 1.0") def test_preserve_flags(df): # https://github.com/geopandas/geopandas/issues/1654 df = df.set_flags(allows_duplicate_labels=False) assert df.flags.allows_duplicate_labels is False # preserve flags in indexing operations for subset in [df[:2], df[df["value1"] > 2], df[["value2", "geometry"]]]: assert df.flags.allows_duplicate_labels is False # preserve attrs in methods df2 = df.reset_index() assert df2.flags.allows_duplicate_labels is False # it is honored for operations that introduce duplicate labels with pytest.raises(ValueError): df.reindex([0, 0, 1]) with pytest.raises(ValueError): df[["value1", "value1", "geometry"]] with pytest.raises(ValueError): pd.concat([df, df]) geopandas-0.10.2/geopandas/tests/test_plotting.py000066400000000000000000002107271413247402600221230ustar00rootroot00000000000000from distutils.version import LooseVersion import itertools import warnings import numpy as np import pandas as pd from shapely import wkt from shapely.affinity import rotate from shapely.geometry import ( MultiPolygon, Polygon, LineString, LinearRing, Point, MultiPoint, MultiLineString, GeometryCollection, ) from geopandas import GeoDataFrame, GeoSeries, read_file from geopandas.datasets import get_path import geopandas._compat as compat from geopandas.plotting import GeoplotAccessor import pytest matplotlib = pytest.importorskip("matplotlib") matplotlib.use("Agg") import matplotlib.pyplot as plt # noqa try: # skipif and importorskip do not work for decorators from matplotlib.testing.decorators import check_figures_equal if matplotlib.__version__ >= LooseVersion("3.3.0"): MPL_DECORATORS = True else: MPL_DECORATORS = False except ImportError: MPL_DECORATORS = False @pytest.fixture(autouse=True) def close_figures(request): yield plt.close("all") try: cycle = matplotlib.rcParams["axes.prop_cycle"].by_key() MPL_DFT_COLOR = cycle["color"][0] except KeyError: MPL_DFT_COLOR = matplotlib.rcParams["axes.color_cycle"][0] plt.rcParams.update({"figure.max_open_warning": 0}) class TestPointPlotting: def setup_method(self): self.N = 10 self.points = GeoSeries(Point(i, i) for i in range(self.N)) values = np.arange(self.N) self.df = GeoDataFrame({"geometry": self.points, "values": values}) self.df["exp"] = (values * 10) ** 3 multipoint1 = MultiPoint(self.points) multipoint2 = rotate(multipoint1, 90) self.df2 = GeoDataFrame( {"geometry": [multipoint1, multipoint2], "values": [0, 1]} ) def test_figsize(self): ax = self.points.plot(figsize=(1, 1)) np.testing.assert_array_equal(ax.figure.get_size_inches(), (1, 1)) ax = self.df.plot(figsize=(1, 1)) np.testing.assert_array_equal(ax.figure.get_size_inches(), (1, 1)) def test_default_colors(self): # # without specifying values -> uniform color # GeoSeries ax = self.points.plot() _check_colors( self.N, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * self.N ) # GeoDataFrame ax = self.df.plot() _check_colors( self.N, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * self.N ) # # with specifying values -> different colors for all 10 values ax = self.df.plot(column="values") cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, ax.collections[0].get_facecolors(), expected_colors) def test_series_color_no_index(self): # Color order with ordered index colors_ord = pd.Series(["a", "b", "c", "a", "b", "c", "a", "b", "c", "a"]) # Plot using Series as color ax1 = self.df.plot(colors_ord) # Correct answer: Add as column to df and plot self.df["colors_ord"] = colors_ord ax2 = self.df.plot("colors_ord") # Confirm out-of-order index re-sorted point_colors1 = ax1.collections[0].get_facecolors() point_colors2 = ax2.collections[0].get_facecolors() np.testing.assert_array_equal(point_colors1[1], point_colors2[1]) def test_series_color_index(self): # Color order with out-of-order index colors_ord = pd.Series( ["a", "a", "a", "a", "b", "b", "b", "c", "c", "c"], index=[0, 3, 6, 9, 1, 4, 7, 2, 5, 8], ) # Plot using Series as color ax1 = self.df.plot(colors_ord) # Correct answer: Add as column to df and plot self.df["colors_ord"] = colors_ord ax2 = self.df.plot("colors_ord") # Confirm out-of-order index re-sorted point_colors1 = ax1.collections[0].get_facecolors() point_colors2 = ax2.collections[0].get_facecolors() np.testing.assert_array_equal(point_colors1[1], point_colors2[1]) def test_colormap(self): # without specifying values but cmap specified -> no uniform color # but different colors for all points # GeoSeries ax = self.points.plot(cmap="RdYlGn") cmap = plt.get_cmap("RdYlGn") exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) ax = self.df.plot(cmap="RdYlGn") _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) # # with specifying values -> different colors for all 10 values ax = self.df.plot(column="values", cmap="RdYlGn") cmap = plt.get_cmap("RdYlGn") _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) # when using a cmap with specified lut -> limited number of different # colors ax = self.points.plot(cmap=plt.get_cmap("Set1", lut=5)) cmap = plt.get_cmap("Set1", lut=5) exp_colors = cmap(list(range(5)) * 2) _check_colors(self.N, ax.collections[0].get_facecolors(), exp_colors) def test_single_color(self): ax = self.points.plot(color="green") _check_colors(self.N, ax.collections[0].get_facecolors(), ["green"] * self.N) ax = self.df.plot(color="green") _check_colors(self.N, ax.collections[0].get_facecolors(), ["green"] * self.N) # check rgba tuple GH1178 ax = self.df.plot(color=(0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5)] * self.N ) ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5, 0.5)] * self.N ) with pytest.raises((ValueError, TypeError)): self.df.plot(color="not color") with warnings.catch_warnings(record=True) as _: # don't print warning # 'color' overrides 'column' ax = self.df.plot(column="values", color="green") _check_colors( self.N, ax.collections[0].get_facecolors(), ["green"] * self.N ) def test_markersize(self): ax = self.points.plot(markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(column="values", markersize=10) assert ax.collections[0].get_sizes() == [10] ax = self.df.plot(markersize="values") assert (ax.collections[0].get_sizes() == self.df["values"]).all() ax = self.df.plot(column="values", markersize="values") assert (ax.collections[0].get_sizes() == self.df["values"]).all() def test_markerstyle(self): ax = self.df2.plot(marker="+") expected = _style_to_vertices("+") np.testing.assert_array_equal( expected, ax.collections[0].get_paths()[0].vertices ) def test_style_kwargs(self): ax = self.points.plot(edgecolors="k") assert (ax.collections[0].get_edgecolor() == [0, 0, 0, 1]).all() def test_style_kwargs_alpha(self): ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df.plot(alpha=np.linspace(0, 0.0, 1.0, self.N)) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( np.linspace(0, 0.0, 1.0, self.N), ax.collections[0].get_alpha() ) def test_legend(self): with warnings.catch_warnings(record=True) as _: # don't print warning # legend ignored if color is given. ax = self.df.plot(column="values", color="green", legend=True) assert len(ax.get_figure().axes) == 1 # no separate legend axis # legend ignored if no column is given. ax = self.df.plot(legend=True) assert len(ax.get_figure().axes) == 1 # no separate legend axis # # Continuous legend # the colorbar matches the Point colors ax = self.df.plot(column="values", cmap="RdYlGn", legend=True) point_colors = ax.collections[0].get_facecolors() cbar_colors = _get_colorbar_ax(ax.get_figure()).collections[-1].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) # # Categorical legend # the colorbar matches the Point colors ax = self.df.plot(column="values", categorical=True, legend=True) point_colors = ax.collections[0].get_facecolors() cbar_colors = ax.get_legend().axes.collections[-1].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) # # Normalized legend # the colorbar matches the Point colors norm = matplotlib.colors.LogNorm( vmin=self.df[1:].exp.min(), vmax=self.df[1:].exp.max() ) ax = self.df[1:].plot(column="exp", cmap="RdYlGn", legend=True, norm=norm) point_colors = ax.collections[0].get_facecolors() cbar_colors = _get_colorbar_ax(ax.get_figure()).collections[-1].get_facecolors() # first point == bottom of colorbar np.testing.assert_array_equal(point_colors[0], cbar_colors[0]) # last point == top of colorbar np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1]) # colorbar generated proper long transition assert cbar_colors.shape == (256, 4) def test_subplots_norm(self): # colors of subplots are the same as for plot (norm is applied) cmap = matplotlib.cm.viridis_r norm = matplotlib.colors.Normalize(vmin=0, vmax=20) ax = self.df.plot(column="values", cmap=cmap, norm=norm) actual_colors_orig = ax.collections[0].get_facecolors() exp_colors = cmap(np.arange(10) / (20)) np.testing.assert_array_equal(exp_colors, actual_colors_orig) fig, ax = plt.subplots() self.df[1:].plot(column="values", ax=ax, norm=norm, cmap=cmap) actual_colors_sub = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors_orig[1], actual_colors_sub[0]) def test_empty_plot(self): s = GeoSeries([Polygon()]) with pytest.warns(UserWarning): ax = s.plot() assert len(ax.collections) == 0 s = GeoSeries([]) with pytest.warns(UserWarning): ax = s.plot() assert len(ax.collections) == 0 df = GeoDataFrame([]) with pytest.warns(UserWarning): ax = df.plot() assert len(ax.collections) == 0 def test_empty_geometry(self): if compat.USE_PYGEOS: s = GeoSeries([wkt.loads("POLYGON EMPTY")]) s = GeoSeries( [Polygon([(0, 0), (1, 0), (1, 1)]), wkt.loads("POLYGON EMPTY")] ) ax = s.plot() assert len(ax.collections) == 1 if not compat.USE_PYGEOS: s = GeoSeries([Polygon([(0, 0), (1, 0), (1, 1)]), Polygon()]) ax = s.plot() assert len(ax.collections) == 1 # more complex case with GEOMETRYCOLLECTION EMPTY, POINT EMPTY and NONE poly = Polygon([(-1, -1), (-1, 2), (2, 2), (2, -1), (-1, -1)]) point = Point(0, 1) point_ = Point(10, 10) empty_point = Point() gdf = GeoDataFrame(geometry=[point, empty_point, point_]) gdf["geometry"] = gdf.intersection(poly) gdf.loc[3] = [None] ax = gdf.plot() assert len(ax.collections) == 1 def test_multipoints(self): # MultiPoints ax = self.df2.plot() _check_colors(4, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * 4) ax = self.df2.plot(column="values") cmap = plt.get_cmap(lut=2) expected_colors = [cmap(0)] * self.N + [cmap(1)] * self.N _check_colors(20, ax.collections[0].get_facecolors(), expected_colors) ax = self.df2.plot(color=["r", "b"]) # colors are repeated for all components within a MultiPolygon _check_colors(20, ax.collections[0].get_facecolors(), ["r"] * 10 + ["b"] * 10) def test_multipoints_alpha(self): ax = self.df2.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df2.plot(alpha=[0.7, 0.2]) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( [0.7] * 10 + [0.2] * 10, ax.collections[0].get_alpha() ) def test_categories(self): self.df["cats_object"] = ["cat1", "cat2"] * 5 self.df["nums"] = [1, 2] * 5 self.df["singlecat_object"] = ["cat2"] * 10 self.df["cats"] = pd.Categorical(["cat1", "cat2"] * 5) self.df["singlecat"] = pd.Categorical( ["cat2"] * 10, categories=["cat1", "cat2"] ) self.df["cats_ordered"] = pd.Categorical( ["cat2", "cat1"] * 5, categories=["cat2", "cat1"] ) ax1 = self.df.plot("cats_object", legend=True) ax2 = self.df.plot("cats", legend=True) ax3 = self.df.plot("singlecat_object", categories=["cat1", "cat2"], legend=True) ax4 = self.df.plot("singlecat", legend=True) ax5 = self.df.plot("cats_ordered", legend=True) ax6 = self.df.plot("nums", categories=[1, 2], legend=True) point_colors1 = ax1.collections[0].get_facecolors() for ax in [ax2, ax3, ax4, ax5, ax6]: point_colors2 = ax.collections[0].get_facecolors() np.testing.assert_array_equal(point_colors1[1], point_colors2[1]) legend1 = [x.get_markerfacecolor() for x in ax1.get_legend().get_lines()] for ax in [ax2, ax3, ax4, ax5, ax6]: legend2 = [x.get_markerfacecolor() for x in ax.get_legend().get_lines()] np.testing.assert_array_equal(legend1, legend2) with pytest.raises(TypeError): self.df.plot(column="cats_object", categories="non_list") with pytest.raises( ValueError, match="Column contains values not listed in categories." ): self.df.plot(column="cats_object", categories=["cat1"]) with pytest.raises( ValueError, match="Cannot specify 'categories' when column has" ): self.df.plot(column="cats", categories=["cat1"]) def test_misssing(self): self.df.loc[0, "values"] = np.nan ax = self.df.plot("values") cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N - 1) / (self.N - 2)) _check_colors(self.N - 1, ax.collections[0].get_facecolors(), expected_colors) ax = self.df.plot("values", missing_kwds={"color": "r"}) cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N - 1) / (self.N - 2)) _check_colors(1, ax.collections[1].get_facecolors(), ["r"]) _check_colors(self.N - 1, ax.collections[0].get_facecolors(), expected_colors) ax = self.df.plot( "values", missing_kwds={"color": "r"}, categorical=True, legend=True ) _check_colors(1, ax.collections[1].get_facecolors(), ["r"]) point_colors = ax.collections[0].get_facecolors() nan_color = ax.collections[1].get_facecolors() leg_colors = ax.get_legend().axes.collections[0].get_facecolors() leg_colors1 = ax.get_legend().axes.collections[1].get_facecolors() np.testing.assert_array_equal(point_colors[0], leg_colors[0]) np.testing.assert_array_equal(nan_color[0], leg_colors1[0]) class TestPointZPlotting: def setup_method(self): self.N = 10 self.points = GeoSeries(Point(i, i, i) for i in range(self.N)) values = np.arange(self.N) self.df = GeoDataFrame({"geometry": self.points, "values": values}) def test_plot(self): # basic test that points with z coords don't break plotting self.df.plot() class TestLineStringPlotting: def setup_method(self): self.N = 10 values = np.arange(self.N) self.lines = GeoSeries( [LineString([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)], index=list("ABCDEFGHIJ"), ) self.df = GeoDataFrame({"geometry": self.lines, "values": values}) multiline1 = MultiLineString(self.lines.loc["A":"B"].values) multiline2 = MultiLineString(self.lines.loc["C":"D"].values) self.df2 = GeoDataFrame( {"geometry": [multiline1, multiline2], "values": [0, 1]} ) self.linearrings = GeoSeries( [LinearRing([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)], index=list("ABCDEFGHIJ"), ) self.df3 = GeoDataFrame({"geometry": self.linearrings, "values": values}) def test_single_color(self): ax = self.lines.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) ax = self.df.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) ax = self.linearrings.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) ax = self.df3.plot(color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) # check rgba tuple GH1178 ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_colors(), [(0.5, 0.5, 0.5, 0.5)] * self.N ) ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors( self.N, ax.collections[0].get_colors(), [(0.5, 0.5, 0.5, 0.5)] * self.N ) with pytest.raises((TypeError, ValueError)): self.df.plot(color="not color") with warnings.catch_warnings(record=True) as _: # don't print warning # 'color' overrides 'column' ax = self.df.plot(column="values", color="green") _check_colors(self.N, ax.collections[0].get_colors(), ["green"] * self.N) def test_style_kwargs_linestyle(self): # single for ax in [ self.lines.plot(linestyle=":", linewidth=1), self.df.plot(linestyle=":", linewidth=1), self.df.plot(column="values", linestyle=":", linewidth=1), ]: assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.lines.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() # multiple ls = [("dashed", "dotted", "dashdot", "solid")[k % 4] for k in range(self.N)] exp_ls = [_style_to_linestring_onoffseq(st, 1) for st in ls] for ax in [ self.lines.plot(linestyle=ls, linewidth=1), self.lines.plot(linestyles=ls, linewidth=1), self.df.plot(linestyle=ls, linewidth=1), self.df.plot(column="values", linestyle=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_style_kwargs_linewidth(self): # single for ax in [ self.lines.plot(linewidth=2), self.df.plot(linewidth=2), self.df.plot(column="values", linewidth=2), ]: np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) # multiple lw = [(0, 1, 2, 5.5, 10)[k % 5] for k in range(self.N)] for ax in [ self.lines.plot(linewidth=lw), self.lines.plot(linewidths=lw), self.df.plot(linewidth=lw), self.df.plot(column="values", linewidth=lw), ]: np.testing.assert_array_equal(lw, ax.collections[0].get_linewidths()) def test_style_kwargs_alpha(self): ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df.plot(alpha=np.linspace(0, 0.0, 1.0, self.N)) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( np.linspace(0, 0.0, 1.0, self.N), ax.collections[0].get_alpha() ) def test_style_kwargs_path_effects(self): from matplotlib.patheffects import withStroke effects = [withStroke(linewidth=8, foreground="b")] ax = self.df.plot(color="orange", path_effects=effects) assert ax.collections[0].get_path_effects()[0].__dict__["_gc"] == { "linewidth": 8, "foreground": "b", } def test_subplots_norm(self): # colors of subplots are the same as for plot (norm is applied) cmap = matplotlib.cm.viridis_r norm = matplotlib.colors.Normalize(vmin=0, vmax=20) ax = self.df.plot(column="values", cmap=cmap, norm=norm) actual_colors_orig = ax.collections[0].get_edgecolors() exp_colors = cmap(np.arange(10) / (20)) np.testing.assert_array_equal(exp_colors, actual_colors_orig) fig, ax = plt.subplots() self.df[1:].plot(column="values", ax=ax, norm=norm, cmap=cmap) actual_colors_sub = ax.collections[0].get_edgecolors() np.testing.assert_array_equal(actual_colors_orig[1], actual_colors_sub[0]) def test_multilinestrings(self): # MultiLineStrings ax = self.df2.plot() assert len(ax.collections[0].get_paths()) == 4 _check_colors(4, ax.collections[0].get_edgecolors(), [MPL_DFT_COLOR] * 4) ax = self.df2.plot("values") cmap = plt.get_cmap(lut=2) # colors are repeated for all components within a MultiLineString expected_colors = [cmap(0), cmap(0), cmap(1), cmap(1)] _check_colors(4, ax.collections[0].get_edgecolors(), expected_colors) ax = self.df2.plot(color=["r", "b"]) # colors are repeated for all components within a MultiLineString _check_colors(4, ax.collections[0].get_edgecolors(), ["r", "r", "b", "b"]) class TestPolygonPlotting: def setup_method(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(1, 0), (2, 0), (2, 1)]) self.polys = GeoSeries([t1, t2], index=list("AB")) self.df = GeoDataFrame({"geometry": self.polys, "values": [0, 1]}) multipoly1 = MultiPolygon([t1, t2]) multipoly2 = rotate(multipoly1, 180) self.df2 = GeoDataFrame( {"geometry": [multipoly1, multipoly2], "values": [0, 1]} ) t3 = Polygon([(2, 0), (3, 0), (3, 1)]) df_nan = GeoDataFrame({"geometry": t3, "values": [np.nan]}) self.df3 = self.df.append(df_nan) def test_single_color(self): ax = self.polys.plot(color="green") _check_colors(2, ax.collections[0].get_facecolors(), ["green"] * 2) # color only sets facecolor assert len(ax.collections[0].get_edgecolors()) == 0 ax = self.df.plot(color="green") _check_colors(2, ax.collections[0].get_facecolors(), ["green"] * 2) assert len(ax.collections[0].get_edgecolors()) == 0 # check rgba tuple GH1178 ax = self.df.plot(color=(0.5, 0.5, 0.5)) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5)] * 2) ax = self.df.plot(color=(0.5, 0.5, 0.5, 0.5)) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5, 0.5)] * 2) with pytest.raises((TypeError, ValueError)): self.df.plot(color="not color") with warnings.catch_warnings(record=True) as _: # don't print warning # 'color' overrides 'values' ax = self.df.plot(column="values", color="green") _check_colors(2, ax.collections[0].get_facecolors(), ["green"] * 2) def test_vmin_vmax(self): # when vmin == vmax, all polygons should be the same color # non-categorical ax = self.df.plot(column="values", categorical=False, vmin=0, vmax=0) actual_colors = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors[0], actual_colors[1]) # categorical ax = self.df.plot(column="values", categorical=True, vmin=0, vmax=0) actual_colors = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors[0], actual_colors[1]) # vmin vmax set correctly for array with NaN (GitHub issue 877) ax = self.df3.plot(column="values") actual_colors = ax.collections[0].get_facecolors() assert np.any(np.not_equal(actual_colors[0], actual_colors[1])) def test_style_kwargs_color(self): # facecolor overrides default cmap when color is not set ax = self.polys.plot(facecolor="k") _check_colors(2, ax.collections[0].get_facecolors(), ["k"] * 2) # facecolor overrides more general-purpose color when both are set ax = self.polys.plot(color="red", facecolor="k") # TODO with new implementation, color overrides facecolor # _check_colors(2, ax.collections[0], ['k']*2, alpha=0.5) # edgecolor ax = self.polys.plot(edgecolor="red") np.testing.assert_array_equal( [(1, 0, 0, 1)], ax.collections[0].get_edgecolors() ) ax = self.df.plot("values", edgecolor="red") np.testing.assert_array_equal( [(1, 0, 0, 1)], ax.collections[0].get_edgecolors() ) # alpha sets both edge and face ax = self.polys.plot(facecolor="g", edgecolor="r", alpha=0.4) _check_colors(2, ax.collections[0].get_facecolors(), ["g"] * 2, alpha=0.4) _check_colors(2, ax.collections[0].get_edgecolors(), ["r"] * 2, alpha=0.4) # check rgba tuple GH1178 for face and edge ax = self.df.plot(facecolor=(0.5, 0.5, 0.5), edgecolor=(0.4, 0.5, 0.6)) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5)] * 2) _check_colors(2, ax.collections[0].get_edgecolors(), [(0.4, 0.5, 0.6)] * 2) ax = self.df.plot( facecolor=(0.5, 0.5, 0.5, 0.5), edgecolor=(0.4, 0.5, 0.6, 0.5) ) _check_colors(2, ax.collections[0].get_facecolors(), [(0.5, 0.5, 0.5, 0.5)] * 2) _check_colors(2, ax.collections[0].get_edgecolors(), [(0.4, 0.5, 0.6, 0.5)] * 2) def test_style_kwargs_linestyle(self): # single ax = self.df.plot(linestyle=":", linewidth=1) assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.df.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() # multiple ls = ["dashed", "dotted"] exp_ls = [_style_to_linestring_onoffseq(st, 1) for st in ls] for ax in [ self.df.plot(linestyle=ls, linewidth=1), self.df.plot(linestyles=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_style_kwargs_linewidth(self): # single ax = self.df.plot(linewidth=2) np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) # multiple for ax in [self.df.plot(linewidth=[2, 4]), self.df.plot(linewidths=[2, 4])]: np.testing.assert_array_equal([2, 4], ax.collections[0].get_linewidths()) # alpha ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df.plot(alpha=[0.7, 0.2]) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal([0.7, 0.2], ax.collections[0].get_alpha()) def test_legend_kwargs(self): ax = self.df.plot( column="values", categorical=True, legend=True, legend_kwds={"frameon": False}, ) assert ax.get_legend().get_frame_on() is False def test_colorbar_kwargs(self): # Test if kwargs are passed to colorbar label_txt = "colorbar test" ax = self.df.plot( column="values", categorical=False, legend=True, legend_kwds={"label": label_txt}, ) cax = _get_colorbar_ax(ax.get_figure()) assert cax.get_ylabel() == label_txt ax = self.df.plot( column="values", categorical=False, legend=True, legend_kwds={"label": label_txt, "orientation": "horizontal"}, ) cax = _get_colorbar_ax(ax.get_figure()) assert cax.get_xlabel() == label_txt def test_fmt_ignore(self): # test if fmt is removed if scheme is not passed (it would raise Error) # GH #1253 self.df.plot( column="values", categorical=True, legend=True, legend_kwds={"fmt": "{:.0f}"}, ) self.df.plot(column="values", legend=True, legend_kwds={"fmt": "{:.0f}"}) def test_multipolygons_color(self): # MultiPolygons ax = self.df2.plot() assert len(ax.collections[0].get_paths()) == 4 _check_colors(4, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * 4) ax = self.df2.plot("values") cmap = plt.get_cmap(lut=2) # colors are repeated for all components within a MultiPolygon expected_colors = [cmap(0), cmap(0), cmap(1), cmap(1)] _check_colors(4, ax.collections[0].get_facecolors(), expected_colors) ax = self.df2.plot(color=["r", "b"]) # colors are repeated for all components within a MultiPolygon _check_colors(4, ax.collections[0].get_facecolors(), ["r", "r", "b", "b"]) def test_multipolygons_linestyle(self): # single ax = self.df2.plot(linestyle=":", linewidth=1) assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.df2.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() # multiple ls = ["dashed", "dotted"] exp_ls = [_style_to_linestring_onoffseq(st, 1) for st in ls for i in range(2)] for ax in [ self.df2.plot(linestyle=ls, linewidth=1), self.df2.plot(linestyles=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_multipolygons_linewidth(self): # single ax = self.df2.plot(linewidth=2) np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) # multiple for ax in [self.df2.plot(linewidth=[2, 4]), self.df2.plot(linewidths=[2, 4])]: np.testing.assert_array_equal( [2, 2, 4, 4], ax.collections[0].get_linewidths() ) def test_multipolygons_alpha(self): ax = self.df2.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) try: ax = self.df2.plot(alpha=[0.7, 0.2]) except TypeError: # no list allowed for alpha up to matplotlib 3.3 pass else: np.testing.assert_array_equal( [0.7, 0.7, 0.2, 0.2], ax.collections[0].get_alpha() ) def test_subplots_norm(self): # colors of subplots are the same as for plot (norm is applied) cmap = matplotlib.cm.viridis_r norm = matplotlib.colors.Normalize(vmin=0, vmax=10) ax = self.df.plot(column="values", cmap=cmap, norm=norm) actual_colors_orig = ax.collections[0].get_facecolors() exp_colors = cmap(np.arange(2) / (10)) np.testing.assert_array_equal(exp_colors, actual_colors_orig) fig, ax = plt.subplots() self.df[1:].plot(column="values", ax=ax, norm=norm, cmap=cmap) actual_colors_sub = ax.collections[0].get_facecolors() np.testing.assert_array_equal(actual_colors_orig[1], actual_colors_sub[0]) class TestPolygonZPlotting: def setup_method(self): t1 = Polygon([(0, 0, 0), (1, 0, 0), (1, 1, 1)]) t2 = Polygon([(1, 0, 0), (2, 0, 0), (2, 1, 1)]) self.polys = GeoSeries([t1, t2], index=list("AB")) self.df = GeoDataFrame({"geometry": self.polys, "values": [0, 1]}) multipoly1 = MultiPolygon([t1, t2]) multipoly2 = rotate(multipoly1, 180) self.df2 = GeoDataFrame( {"geometry": [multipoly1, multipoly2], "values": [0, 1]} ) def test_plot(self): # basic test that points with z coords don't break plotting self.df.plot() class TestGeometryCollectionPlotting: def setup_method(self): coll1 = GeometryCollection( [ Polygon([(1, 0), (2, 0), (2, 1)]), MultiLineString([((0.5, 0.5), (1, 1)), ((1, 0.5), (1.5, 1))]), ] ) coll2 = GeometryCollection( [Point(0.75, 0.25), Polygon([(2, 2), (3, 2), (2, 3)])] ) self.series = GeoSeries([coll1, coll2]) self.df = GeoDataFrame({"geometry": self.series, "values": [1, 2]}) def test_colors(self): # default uniform color ax = self.series.plot() _check_colors( 2, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR] * 2 ) # poly _check_colors( 2, ax.collections[1].get_edgecolors(), [MPL_DFT_COLOR] * 2 ) # line _check_colors(1, ax.collections[2].get_facecolors(), [MPL_DFT_COLOR]) # point def test_values(self): ax = self.df.plot("values") cmap = plt.get_cmap() exp_colors = cmap([0.0, 1.0]) _check_colors(2, ax.collections[0].get_facecolors(), exp_colors) # poly _check_colors( 2, ax.collections[1].get_edgecolors(), [exp_colors[0]] * 2 ) # line _check_colors(1, ax.collections[2].get_facecolors(), [exp_colors[1]]) # point class TestNonuniformGeometryPlotting: def setup_method(self): pytest.importorskip("matplotlib", "1.5.0") poly = Polygon([(1, 0), (2, 0), (2, 1)]) line = LineString([(0.5, 0.5), (1, 1), (1, 0.5), (1.5, 1)]) point = Point(0.75, 0.25) self.series = GeoSeries([poly, line, point]) self.df = GeoDataFrame({"geometry": self.series, "values": [1, 2, 3]}) def test_colors(self): # default uniform color ax = self.series.plot() _check_colors(1, ax.collections[0].get_facecolors(), [MPL_DFT_COLOR]) _check_colors(1, ax.collections[1].get_edgecolors(), [MPL_DFT_COLOR]) _check_colors(1, ax.collections[2].get_facecolors(), [MPL_DFT_COLOR]) # colormap: different colors ax = self.series.plot(cmap="RdYlGn") cmap = plt.get_cmap("RdYlGn") exp_colors = cmap(np.arange(3) / (3 - 1)) _check_colors(1, ax.collections[0].get_facecolors(), [exp_colors[0]]) _check_colors(1, ax.collections[1].get_edgecolors(), [exp_colors[1]]) _check_colors(1, ax.collections[2].get_facecolors(), [exp_colors[2]]) def test_style_kwargs(self): ax = self.series.plot(markersize=10) assert ax.collections[2].get_sizes() == [10] ax = self.df.plot(markersize=10) assert ax.collections[2].get_sizes() == [10] def test_style_kwargs_linestyle(self): # single for ax in [ self.series.plot(linestyle=":", linewidth=1), self.df.plot(linestyle=":", linewidth=1), ]: assert [(0.0, [1.0, 1.65])] == ax.collections[0].get_linestyle() # tuple ax = self.series.plot(linestyle=(0, (3, 10, 1, 15)), linewidth=1) assert [(0, [3, 10, 1, 15])] == ax.collections[0].get_linestyle() @pytest.mark.skip( reason="array-like style_kwds not supported for mixed geometry types (#1379)" ) def test_style_kwargs_linestyle_listlike(self): # multiple ls = ["solid", "dotted", "dashdot"] exp_ls = [_style_to_linestring_onoffseq(style, 1) for style in ls] for ax in [ self.series.plot(linestyle=ls, linewidth=1), self.series.plot(linestyles=ls, linewidth=1), self.df.plot(linestyles=ls, linewidth=1), ]: assert exp_ls == ax.collections[0].get_linestyle() def test_style_kwargs_linewidth(self): # single ax = self.df.plot(linewidth=2) np.testing.assert_array_equal([2], ax.collections[0].get_linewidths()) @pytest.mark.skip( reason="array-like style_kwds not supported for mixed geometry types (#1379)" ) def test_style_kwargs_linewidth_listlike(self): # multiple for ax in [ self.series.plot(linewidths=[2, 4, 5.5]), self.series.plot(linewidths=[2, 4, 5.5]), self.df.plot(linewidths=[2, 4, 5.5]), ]: np.testing.assert_array_equal( [2, 4, 5.5], ax.collections[0].get_linewidths() ) def test_style_kwargs_alpha(self): ax = self.df.plot(alpha=0.7) np.testing.assert_array_equal([0.7], ax.collections[0].get_alpha()) # TODO splitting array-like arguments for the different plot types # is not yet supported - https://github.com/geopandas/geopandas/issues/1379 # try: # ax = self.df.plot(alpha=[0.7, 0.2, 0.9]) # except TypeError: # # no list allowed for alpha up to matplotlib 3.3 # pass # else: # np.testing.assert_array_equal( # [0.7, 0.2, 0.9], ax.collections[0].get_alpha() # ) class TestGeographicAspect: def setup_class(self): pth = get_path("naturalearth_lowres") df = read_file(pth) self.north = df.loc[df.continent == "North America"] self.north_proj = self.north.to_crs("ESRI:102008") bounds = self.north.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) self.exp = 1 / np.cos(y_coord * np.pi / 180) def test_auto(self): ax = self.north.geometry.plot() assert ax.get_aspect() == self.exp ax2 = self.north_proj.geometry.plot() assert ax2.get_aspect() in ["equal", 1.0] ax = self.north.plot() assert ax.get_aspect() == self.exp ax2 = self.north_proj.plot() assert ax2.get_aspect() in ["equal", 1.0] ax3 = self.north.plot("pop_est") assert ax3.get_aspect() == self.exp ax4 = self.north_proj.plot("pop_est") assert ax4.get_aspect() in ["equal", 1.0] def test_manual(self): ax = self.north.geometry.plot(aspect="equal") assert ax.get_aspect() in ["equal", 1.0] self.north.geometry.plot(ax=ax, aspect=None) assert ax.get_aspect() in ["equal", 1.0] ax2 = self.north.geometry.plot(aspect=0.5) assert ax2.get_aspect() == 0.5 self.north.geometry.plot(ax=ax2, aspect=None) assert ax2.get_aspect() == 0.5 ax3 = self.north_proj.geometry.plot(aspect=0.5) assert ax3.get_aspect() == 0.5 self.north_proj.geometry.plot(ax=ax3, aspect=None) assert ax3.get_aspect() == 0.5 ax = self.north.plot(aspect="equal") assert ax.get_aspect() in ["equal", 1.0] self.north.plot(ax=ax, aspect=None) assert ax.get_aspect() in ["equal", 1.0] ax2 = self.north.plot(aspect=0.5) assert ax2.get_aspect() == 0.5 self.north.plot(ax=ax2, aspect=None) assert ax2.get_aspect() == 0.5 ax3 = self.north_proj.plot(aspect=0.5) assert ax3.get_aspect() == 0.5 self.north_proj.plot(ax=ax3, aspect=None) assert ax3.get_aspect() == 0.5 ax = self.north.plot("pop_est", aspect="equal") assert ax.get_aspect() in ["equal", 1.0] self.north.plot("pop_est", ax=ax, aspect=None) assert ax.get_aspect() in ["equal", 1.0] ax2 = self.north.plot("pop_est", aspect=0.5) assert ax2.get_aspect() == 0.5 self.north.plot("pop_est", ax=ax2, aspect=None) assert ax2.get_aspect() == 0.5 ax3 = self.north_proj.plot("pop_est", aspect=0.5) assert ax3.get_aspect() == 0.5 self.north_proj.plot("pop_est", ax=ax3, aspect=None) assert ax3.get_aspect() == 0.5 class TestMapclassifyPlotting: @classmethod def setup_class(cls): try: import mapclassify # noqa except ImportError: pytest.importorskip("mapclassify") cls.classifiers = list(mapclassify.classifiers.CLASSIFIERS) cls.classifiers.remove("UserDefined") pth = get_path("naturalearth_lowres") cls.df = read_file(pth) cls.df["NEGATIVES"] = np.linspace(-10, 10, len(cls.df.index)) cls.df["low_vals"] = np.linspace(0, 0.3, cls.df.shape[0]) cls.df["mid_vals"] = np.linspace(0.3, 0.7, cls.df.shape[0]) cls.df["high_vals"] = np.linspace(0.7, 1.0, cls.df.shape[0]) cls.df.loc[cls.df.index[:20:2], "high_vals"] = np.nan cls.nybb = read_file(get_path("nybb")) cls.nybb["vals"] = [0.001, 0.002, 0.003, 0.004, 0.005] def test_legend(self): with warnings.catch_warnings(record=True) as _: # don't print warning # warning coming from scipy.stats ax = self.df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [ u" 140.00, 5217064.00", u" 5217064.00, 19532732.33", u" 19532732.33, 1379302771.00", ] assert labels == expected def test_bin_labels(self): ax = self.df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True, legend_kwds={"labels": ["foo", "bar", "baz"]}, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = ["foo", "bar", "baz"] assert labels == expected def test_invalid_labels_length(self): with pytest.raises(ValueError): self.df.plot( column="pop_est", scheme="QUANTILES", k=3, cmap="OrRd", legend=True, legend_kwds={"labels": ["foo", "bar"]}, ) def test_negative_legend(self): ax = self.df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [u"-10.00, -3.41", u" -3.41, 3.30", u" 3.30, 10.00"] assert labels == expected def test_fmt(self): ax = self.df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True, legend_kwds={"fmt": "{:.0f}"}, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [u"-10, -3", u" -3, 3", u" 3, 10"] assert labels == expected def test_interval(self): ax = self.df.plot( column="NEGATIVES", scheme="FISHER_JENKS", k=3, cmap="OrRd", legend=True, legend_kwds={"interval": True}, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [u"[-10.00, -3.41]", u"( -3.41, 3.30]", u"( 3.30, 10.00]"] assert labels == expected @pytest.mark.parametrize("scheme", ["FISHER_JENKS", "FISHERJENKS"]) def test_scheme_name_compat(self, scheme): ax = self.df.plot(column="NEGATIVES", scheme=scheme, k=3, legend=True) assert len(ax.get_legend().get_texts()) == 3 def test_schemes(self): # test if all available classifiers pass for scheme in self.classifiers: self.df.plot(column="pop_est", scheme=scheme, legend=True) def test_classification_kwds(self): ax = self.df.plot( column="pop_est", scheme="percentiles", k=3, classification_kwds={"pct": [50, 100]}, cmap="OrRd", legend=True, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [" 140.00, 9961396.00", " 9961396.00, 1379302771.00"] assert labels == expected def test_invalid_scheme(self): with pytest.raises(ValueError): scheme = "invalid_scheme_*#&)(*#" self.df.plot( column="gdp_md_est", scheme=scheme, k=3, cmap="OrRd", legend=True ) def test_cax_legend_passing(self): """Pass a 'cax' argument to 'df.plot(.)', that is valid only if 'ax' is passed as well (if not, a new figure is created ad hoc, and 'cax' is ignored) """ ax = plt.axes() from mpl_toolkits.axes_grid1 import make_axes_locatable divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.1) with pytest.raises(ValueError): ax = self.df.plot(column="pop_est", cmap="OrRd", legend=True, cax=cax) def test_cax_legend_height(self): """Pass a cax argument to 'df.plot(.)', the legend location must be aligned with those of main plot """ # base case with warnings.catch_warnings(record=True) as _: # don't print warning ax = self.df.plot(column="pop_est", cmap="OrRd", legend=True) plot_height = _get_ax(ax.get_figure(), "").get_position().height legend_height = _get_ax(ax.get_figure(), "").get_position().height assert abs(plot_height - legend_height) >= 1e-6 # fix heights with cax argument fig, ax2 = plt.subplots() from mpl_toolkits.axes_grid1 import make_axes_locatable divider = make_axes_locatable(ax2) cax = divider.append_axes("right", size="5%", pad=0.1, label="fixed_colorbar") with warnings.catch_warnings(record=True) as _: ax2 = self.df.plot( column="pop_est", cmap="OrRd", legend=True, cax=cax, ax=ax2 ) plot_height = _get_ax(fig, "").get_position().height legend_height = _get_ax(fig, "fixed_colorbar").get_position().height assert abs(plot_height - legend_height) < 1e-6 def test_empty_bins(self): bins = np.arange(1, 11) / 10 ax = self.df.plot( "low_vals", scheme="UserDefined", classification_kwds={"bins": bins}, legend=True, ) expected = np.array( [ [0.281412, 0.155834, 0.469201, 1.0], [0.267004, 0.004874, 0.329415, 1.0], [0.244972, 0.287675, 0.53726, 1.0], ] ) assert all( [ (z == expected).all(axis=1).any() for z in ax.collections[0].get_facecolors() ] ) labels = [ "0.00, 0.10", "0.10, 0.20", "0.20, 0.30", "0.30, 0.40", "0.40, 0.50", "0.50, 0.60", "0.60, 0.70", "0.70, 0.80", "0.80, 0.90", "0.90, 1.00", ] legend = [t.get_text() for t in ax.get_legend().get_texts()] assert labels == legend legend_colors_exp = [ (0.267004, 0.004874, 0.329415, 1.0), (0.281412, 0.155834, 0.469201, 1.0), (0.244972, 0.287675, 0.53726, 1.0), (0.190631, 0.407061, 0.556089, 1.0), (0.147607, 0.511733, 0.557049, 1.0), (0.119699, 0.61849, 0.536347, 1.0), (0.20803, 0.718701, 0.472873, 1.0), (0.430983, 0.808473, 0.346476, 1.0), (0.709898, 0.868751, 0.169257, 1.0), (0.993248, 0.906157, 0.143936, 1.0), ] assert [ line.get_markerfacecolor() for line in ax.get_legend().get_lines() ] == legend_colors_exp ax2 = self.df.plot( "mid_vals", scheme="UserDefined", classification_kwds={"bins": bins}, legend=True, ) expected = np.array( [ [0.244972, 0.287675, 0.53726, 1.0], [0.190631, 0.407061, 0.556089, 1.0], [0.147607, 0.511733, 0.557049, 1.0], [0.119699, 0.61849, 0.536347, 1.0], [0.20803, 0.718701, 0.472873, 1.0], ] ) assert all( [ (z == expected).all(axis=1).any() for z in ax2.collections[0].get_facecolors() ] ) labels = [ "-inf, 0.10", "0.10, 0.20", "0.20, 0.30", "0.30, 0.40", "0.40, 0.50", "0.50, 0.60", "0.60, 0.70", "0.70, 0.80", "0.80, 0.90", "0.90, 1.00", ] legend = [t.get_text() for t in ax2.get_legend().get_texts()] assert labels == legend assert [ line.get_markerfacecolor() for line in ax2.get_legend().get_lines() ] == legend_colors_exp ax3 = self.df.plot( "high_vals", scheme="UserDefined", classification_kwds={"bins": bins}, legend=True, ) expected = np.array( [ [0.709898, 0.868751, 0.169257, 1.0], [0.993248, 0.906157, 0.143936, 1.0], [0.430983, 0.808473, 0.346476, 1.0], ] ) assert all( [ (z == expected).all(axis=1).any() for z in ax3.collections[0].get_facecolors() ] ) legend = [t.get_text() for t in ax3.get_legend().get_texts()] assert labels == legend assert [ line.get_markerfacecolor() for line in ax3.get_legend().get_lines() ] == legend_colors_exp def test_equally_formatted_bins(self): ax = self.nybb.plot( "vals", scheme="quantiles", legend=True, ) labels = [t.get_text() for t in ax.get_legend().get_texts()] expected = [ "0.00, 0.00", "0.00, 0.00", "0.00, 0.00", "0.00, 0.00", "0.00, 0.01", ] assert labels == expected ax2 = self.nybb.plot( "vals", scheme="quantiles", legend=True, legend_kwds=dict(fmt="{:.3f}") ) labels = [t.get_text() for t in ax2.get_legend().get_texts()] expected = [ "0.001, 0.002", "0.002, 0.003", "0.003, 0.003", "0.003, 0.004", "0.004, 0.005", ] assert labels == expected class TestPlotCollections: def setup_method(self): self.N = 3 self.values = np.arange(self.N) self.points = GeoSeries(Point(i, i) for i in range(self.N)) self.lines = GeoSeries( [LineString([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)] ) self.polygons = GeoSeries( [Polygon([(0, i), (4, i + 0.5), (9, i)]) for i in range(self.N)] ) def test_points(self): # failing with matplotlib 1.4.3 (edge stays black even when specified) pytest.importorskip("matplotlib", "1.5.0") from geopandas.plotting import _plot_point_collection, plot_point_collection from matplotlib.collections import PathCollection fig, ax = plt.subplots() coll = _plot_point_collection(ax, self.points) assert isinstance(coll, PathCollection) ax.cla() # default: single default matplotlib color coll = _plot_point_collection(ax, self.points) _check_colors(self.N, coll.get_facecolors(), [MPL_DFT_COLOR] * self.N) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolors(), [MPL_DFT_COLOR]*self.N) ax.cla() # specify single other color coll = _plot_point_collection(ax, self.points, color="g") _check_colors(self.N, coll.get_facecolors(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolors(), ["g"] * self.N) ax.cla() # specify edgecolor/facecolor coll = _plot_point_collection(ax, self.points, facecolor="g", edgecolor="r") _check_colors(self.N, coll.get_facecolors(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolors(), ["r"] * self.N) ax.cla() # list of colors coll = _plot_point_collection(ax, self.points, color=["r", "g", "b"]) _check_colors(self.N, coll.get_facecolors(), ["r", "g", "b"]) _check_colors(self.N, coll.get_edgecolors(), ["r", "g", "b"]) ax.cla() coll = _plot_point_collection( ax, self.points, color=[(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_facecolors(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_edgecolors(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) ax.cla() # not a color with pytest.raises((TypeError, ValueError)): _plot_point_collection(ax, self.points, color="not color") # check DeprecationWarning with pytest.warns(DeprecationWarning): plot_point_collection(ax, self.points) def test_points_values(self): from geopandas.plotting import _plot_point_collection # default colormap fig, ax = plt.subplots() coll = _plot_point_collection(ax, self.points, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolors(), expected_colors) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolors(), expected_colors) def test_linestrings(self): from geopandas.plotting import ( _plot_linestring_collection, plot_linestring_collection, ) from matplotlib.collections import LineCollection fig, ax = plt.subplots() coll = _plot_linestring_collection(ax, self.lines) assert isinstance(coll, LineCollection) ax.cla() # default: single default matplotlib color coll = _plot_linestring_collection(ax, self.lines) _check_colors(self.N, coll.get_color(), [MPL_DFT_COLOR] * self.N) ax.cla() # specify single other color coll = _plot_linestring_collection(ax, self.lines, color="g") _check_colors(self.N, coll.get_colors(), ["g"] * self.N) ax.cla() # specify edgecolor / facecolor coll = _plot_linestring_collection(ax, self.lines, facecolor="g", edgecolor="r") _check_colors(self.N, coll.get_facecolors(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolors(), ["r"] * self.N) ax.cla() # list of colors coll = _plot_linestring_collection(ax, self.lines, color=["r", "g", "b"]) _check_colors(self.N, coll.get_colors(), ["r", "g", "b"]) ax.cla() coll = _plot_linestring_collection( ax, self.lines, color=[(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_colors(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) ax.cla() # pass through of kwargs coll = _plot_linestring_collection(ax, self.lines, linestyle="--", linewidth=1) exp_ls = _style_to_linestring_onoffseq("dashed", 1) res_ls = coll.get_linestyle()[0] assert res_ls[0] == exp_ls[0] assert res_ls[1] == exp_ls[1] ax.cla() # not a color with pytest.raises((TypeError, ValueError)): _plot_linestring_collection(ax, self.lines, color="not color") # check DeprecationWarning with pytest.warns(DeprecationWarning): plot_linestring_collection(ax, self.lines) def test_linestrings_values(self): from geopandas.plotting import _plot_linestring_collection fig, ax = plt.subplots() # default colormap coll = _plot_linestring_collection(ax, self.lines, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() # specify colormap coll = _plot_linestring_collection(ax, self.lines, self.values, cmap="RdBu") fig.canvas.draw_idle() cmap = plt.get_cmap("RdBu") expected_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_color(), expected_colors) ax.cla() # specify vmin/vmax coll = _plot_linestring_collection(ax, self.lines, self.values, vmin=3, vmax=5) fig.canvas.draw_idle() cmap = plt.get_cmap() expected_colors = [cmap(0)] _check_colors(self.N, coll.get_color(), expected_colors * 3) ax.cla() def test_polygons(self): from geopandas.plotting import _plot_polygon_collection, plot_polygon_collection from matplotlib.collections import PatchCollection fig, ax = plt.subplots() coll = _plot_polygon_collection(ax, self.polygons) assert isinstance(coll, PatchCollection) ax.cla() # default: single default matplotlib color coll = _plot_polygon_collection(ax, self.polygons) _check_colors(self.N, coll.get_facecolor(), [MPL_DFT_COLOR] * self.N) assert len(coll.get_edgecolor()) == 0 ax.cla() # default: color sets both facecolor and edgecolor coll = _plot_polygon_collection(ax, self.polygons, color="g") _check_colors(self.N, coll.get_facecolor(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolor(), ["g"] * self.N) ax.cla() # default: color can be passed as a list coll = _plot_polygon_collection(ax, self.polygons, color=["g", "b", "r"]) _check_colors(self.N, coll.get_facecolor(), ["g", "b", "r"]) _check_colors(self.N, coll.get_edgecolor(), ["g", "b", "r"]) ax.cla() coll = _plot_polygon_collection( ax, self.polygons, color=[(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_facecolor(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) _check_colors( self.N, coll.get_edgecolor(), [(0.5, 0.5, 0.5, 0.5), (0.1, 0.2, 0.3, 0.5), (0.4, 0.5, 0.6, 0.5)], ) ax.cla() # only setting facecolor keeps default for edgecolor coll = _plot_polygon_collection(ax, self.polygons, facecolor="g") _check_colors(self.N, coll.get_facecolor(), ["g"] * self.N) assert len(coll.get_edgecolor()) == 0 ax.cla() # custom facecolor and edgecolor coll = _plot_polygon_collection(ax, self.polygons, facecolor="g", edgecolor="r") _check_colors(self.N, coll.get_facecolor(), ["g"] * self.N) _check_colors(self.N, coll.get_edgecolor(), ["r"] * self.N) ax.cla() # not a color with pytest.raises((TypeError, ValueError)): _plot_polygon_collection(ax, self.polygons, color="not color") # check DeprecationWarning with pytest.warns(DeprecationWarning): plot_polygon_collection(ax, self.polygons) def test_polygons_values(self): from geopandas.plotting import _plot_polygon_collection fig, ax = plt.subplots() # default colormap, edge is still black by default coll = _plot_polygon_collection(ax, self.polygons, self.values) fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) # edgecolor depends on matplotlib version # _check_colors(self.N, coll.get_edgecolor(), ['k'] * self.N) ax.cla() # specify colormap coll = _plot_polygon_collection(ax, self.polygons, self.values, cmap="RdBu") fig.canvas.draw_idle() cmap = plt.get_cmap("RdBu") exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) ax.cla() # specify vmin/vmax coll = _plot_polygon_collection(ax, self.polygons, self.values, vmin=3, vmax=5) fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = [cmap(0)] _check_colors(self.N, coll.get_facecolor(), exp_colors * 3) ax.cla() # override edgecolor coll = _plot_polygon_collection(ax, self.polygons, self.values, edgecolor="g") fig.canvas.draw_idle() cmap = plt.get_cmap() exp_colors = cmap(np.arange(self.N) / (self.N - 1)) _check_colors(self.N, coll.get_facecolor(), exp_colors) _check_colors(self.N, coll.get_edgecolor(), ["g"] * self.N) ax.cla() class TestGeoplotAccessor: def setup_method(self): geometries = [Polygon([(0, 0), (1, 0), (1, 1)]), Point(1, 3)] x = [1, 2] y = [10, 20] self.gdf = GeoDataFrame( {"geometry": geometries, "x": x, "y": y}, crs="EPSG:4326" ) self.df = pd.DataFrame({"x": x, "y": y}) def compare_figures(self, kind, fig_test, fig_ref, kwargs): """Compare Figures.""" ax_pandas_1 = fig_test.subplots() self.df.plot(kind=kind, ax=ax_pandas_1, **kwargs) ax_geopandas_1 = fig_ref.subplots() self.gdf.plot(kind=kind, ax=ax_geopandas_1, **kwargs) ax_pandas_2 = fig_test.subplots() getattr(self.df.plot, kind)(ax=ax_pandas_2, **kwargs) ax_geopandas_2 = fig_ref.subplots() getattr(self.gdf.plot, kind)(ax=ax_geopandas_2, **kwargs) _pandas_kinds = [] _pandas_kinds = GeoplotAccessor._pandas_kinds if MPL_DECORATORS: @pytest.mark.parametrize("kind", _pandas_kinds) @check_figures_equal(extensions=["png", "pdf"]) def test_pandas_kind(self, kind, fig_test, fig_ref): """Test Pandas kind.""" import importlib _scipy_dependent_kinds = ["kde", "density"] # Needs scipy _y_kinds = ["pie"] # Needs y _xy_kinds = ["scatter", "hexbin"] # Needs x & y kwargs = {} if kind in _scipy_dependent_kinds: if not importlib.util.find_spec("scipy"): with pytest.raises( ModuleNotFoundError, match="No module named 'scipy'" ): self.gdf.plot(kind=kind) elif kind in _y_kinds: kwargs = {"y": "y"} elif kind in _xy_kinds: kwargs = {"x": "x", "y": "y"} if kind == "hexbin": # increase gridsize to reduce duration kwargs["gridsize"] = 10 self.compare_figures(kind, fig_test, fig_ref, kwargs) plt.close("all") @check_figures_equal(extensions=["png", "pdf"]) def test_geo_kind(self, fig_test, fig_ref): """Test Geo kind.""" ax1 = fig_test.subplots() self.gdf.plot(ax=ax1) ax2 = fig_ref.subplots() getattr(self.gdf.plot, "geo")(ax=ax2) plt.close("all") def test_invalid_kind(self): """Test invalid kinds.""" with pytest.raises(ValueError, match="error is not a valid plot kind"): self.gdf.plot(kind="error") with pytest.raises( AttributeError, match="'GeoplotAccessor' object has no attribute 'error'", ): self.gdf.plot.error() def test_column_values(): """ Check that the dataframe plot method returns same values with an input string (column in df), pd.Series, or np.array """ # Build test data t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(1, 0), (2, 0), (2, 1)]) polys = GeoSeries([t1, t2], index=list("AB")) df = GeoDataFrame({"geometry": polys, "values": [0, 1]}) # Test with continuous values ax = df.plot(column="values") colors = ax.collections[0].get_facecolors() ax = df.plot(column=df["values"]) colors_series = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_series) ax = df.plot(column=df["values"].values) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) # Test with categorical values ax = df.plot(column="values", categorical=True) colors = ax.collections[0].get_facecolors() ax = df.plot(column=df["values"], categorical=True) colors_series = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_series) ax = df.plot(column=df["values"].values, categorical=True) colors_array = ax.collections[0].get_facecolors() np.testing.assert_array_equal(colors, colors_array) # Check raised error: is df rows number equal to column length? with pytest.raises(ValueError, match="different number of rows"): ax = df.plot(column=np.array([1, 2, 3])) def test_polygon_patch(): # test adapted from descartes by Sean Gillies # (BSD license, https://pypi.org/project/descartes). from geopandas.plotting import _PolygonPatch from matplotlib.patches import PathPatch polygon = ( Point(0, 0).buffer(10.0).difference(MultiPoint([(-5, 0), (5, 0)]).buffer(3.0)) ) patch = _PolygonPatch(polygon) assert isinstance(patch, PathPatch) path = patch.get_path() if compat.GEOS_GE_390: assert len(path.vertices) == len(path.codes) == 195 else: assert len(path.vertices) == len(path.codes) == 198 def _check_colors(N, actual_colors, expected_colors, alpha=None): """ Asserts that the members of `collection` match the `expected_colors` (in order) Parameters ---------- N : int The number of geometries believed to be in collection. matplotlib.collection is implemented such that the number of geoms in `collection` doesn't have to match the number of colors assignments in the collection: the colors will cycle to meet the needs of the geoms. `N` helps us resolve this. collection : matplotlib.collections.Collection The colors of this collection's patches are read from `collection.get_facecolors()` expected_colors : sequence of RGBA tuples alpha : float (optional) If set, this alpha transparency will be applied to the `expected_colors`. (Any transparency on the `collection` is assumed to be set in its own facecolor RGBA tuples.) """ import matplotlib.colors as colors conv = colors.colorConverter # Convert 2D numpy array to a list of RGBA tuples. actual_colors = map(tuple, actual_colors) all_actual_colors = list(itertools.islice(itertools.cycle(actual_colors), N)) assert len(all_actual_colors) == len(expected_colors), ( "Different " "lengths of actual and expected colors!" ) for actual, expected in zip(all_actual_colors, expected_colors): assert actual == conv.to_rgba(expected, alpha=alpha), "{} != {}".format( actual, conv.to_rgba(expected, alpha=alpha) ) def _style_to_linestring_onoffseq(linestyle, linewidth): """Converts a linestyle string representation, namely one of: ['dashed', 'dotted', 'dashdot', 'solid'], documented in `Collections.set_linestyle`, to the form `onoffseq`. """ offset, dashes = matplotlib.lines._get_dash_pattern(linestyle) return matplotlib.lines._scale_dashes(offset, dashes, linewidth) def _style_to_vertices(markerstyle): """Converts a markerstyle string to a path.""" # TODO: Vertices values are twice the actual path; unclear, why. path = matplotlib.markers.MarkerStyle(markerstyle).get_path() return path.vertices / 2 def _get_ax(fig, label): """ Helper function to not rely on the order of `fig.axes`. Previously, we did `fig.axes[1]`, but in matplotlib 3.4 the order switched and the colorbar ax was first and subplot ax second. """ if matplotlib.__version__ < LooseVersion("3.0.0"): if label == "": return fig.axes[1] elif label == "": return fig.axes[0] for ax in fig.axes: if ax.get_label() == label: return ax else: raise ValueError("no ax found with label {0}".format(label)) def _get_colorbar_ax(fig): return _get_ax(fig, "") geopandas-0.10.2/geopandas/tests/test_show_versions.py000066400000000000000000000022231413247402600231610ustar00rootroot00000000000000from geopandas.tools._show_versions import ( _get_C_info, _get_deps_info, _get_sys_info, show_versions, ) def test_get_sys_info(): sys_info = _get_sys_info() assert "python" in sys_info assert "executable" in sys_info assert "machine" in sys_info def test_get_c_info(): C_info = _get_C_info() assert "GEOS" in C_info assert "GEOS lib" in C_info assert "GDAL" in C_info assert "GDAL data dir" in C_info assert "PROJ" in C_info assert "PROJ data dir" in C_info def test_get_deps_info(): deps_info = _get_deps_info() assert "geopandas" in deps_info assert "pandas" in deps_info assert "fiona" in deps_info assert "numpy" in deps_info assert "shapely" in deps_info assert "rtree" in deps_info assert "pyproj" in deps_info assert "matplotlib" in deps_info assert "mapclassify" in deps_info assert "geopy" in deps_info assert "psycopg2" in deps_info assert "geoalchemy2" in deps_info def test_show_versions(capsys): show_versions() out, err = capsys.readouterr() assert "python" in out assert "GEOS" in out assert "geopandas" in out geopandas-0.10.2/geopandas/tests/test_sindex.py000066400000000000000000000763721413247402600215630ustar00rootroot00000000000000from math import sqrt from shapely.geometry import ( Point, Polygon, MultiPolygon, box, GeometryCollection, LineString, ) from numpy.testing import assert_array_equal import geopandas from geopandas import _compat as compat from geopandas import GeoDataFrame, GeoSeries, read_file, datasets import pytest import numpy as np if compat.USE_PYGEOS: import pygeos @pytest.mark.skip_no_sindex class TestSeriesSindex: def test_has_sindex(self): """Test the has_sindex method.""" t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) d = GeoDataFrame({"geom": [t1, t2]}, geometry="geom") assert not d.has_sindex d.sindex assert d.has_sindex d.geometry.values._sindex = None assert not d.has_sindex d.sindex assert d.has_sindex s = GeoSeries([t1, t2]) assert not s.has_sindex s.sindex assert s.has_sindex s.values._sindex = None assert not s.has_sindex s.sindex assert s.has_sindex def test_empty_geoseries(self): """Tests creating a spatial index from an empty GeoSeries.""" s = GeoSeries(dtype=object) assert not s.sindex assert len(s.sindex) == 0 def test_point(self): s = GeoSeries([Point(0, 0)]) assert s.sindex.size == 1 hits = s.sindex.intersection((-1, -1, 1, 1)) assert len(list(hits)) == 1 hits = s.sindex.intersection((-2, -2, -1, -1)) assert len(list(hits)) == 0 def test_empty_point(self): """Tests that a single empty Point results in an empty tree.""" s = GeoSeries([Point()]) assert not s.sindex assert len(s.sindex) == 0 def test_polygons(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) s = GeoSeries([t1, t2, sq]) assert s.sindex.size == 3 def test_polygons_append(self): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) sq = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) s = GeoSeries([t1, t2, sq]) t = GeoSeries([t1, t2, sq], [3, 4, 5]) s = s.append(t) assert len(s) == 6 assert s.sindex.size == 6 def test_lazy_build(self): s = GeoSeries([Point(0, 0)]) assert s.values._sindex is None assert s.sindex.size == 1 assert s.values._sindex is not None def test_rebuild_on_item_change(self): s = GeoSeries([Point(0, 0)]) original_index = s.sindex s.iloc[0] = Point(0, 0) assert s.sindex is not original_index def test_rebuild_on_slice(self): s = GeoSeries([Point(0, 0), Point(0, 0)]) original_index = s.sindex # Select a couple of rows sliced = s.iloc[:1] assert sliced.sindex is not original_index # Select all rows sliced = s.iloc[:] assert sliced.sindex is original_index # Select all rows and flip sliced = s.iloc[::-1] assert sliced.sindex is not original_index @pytest.mark.skip_no_sindex class TestFrameSindex: def setup_method(self): data = { "A": range(5), "B": range(-5, 0), "geom": [Point(x, y) for x, y in zip(range(5), range(5))], } self.df = GeoDataFrame(data, geometry="geom") def test_sindex(self): self.df.crs = "epsg:4326" assert self.df.sindex.size == 5 hits = list(self.df.sindex.intersection((2.5, 2.5, 4, 4))) assert len(hits) == 2 assert hits[0] == 3 def test_lazy_build(self): assert self.df.geometry.values._sindex is None assert self.df.sindex.size == 5 assert self.df.geometry.values._sindex is not None def test_sindex_rebuild_on_set_geometry(self): # First build the sindex assert self.df.sindex is not None original_index = self.df.sindex self.df.set_geometry( [Point(x, y) for x, y in zip(range(5, 10), range(5, 10))], inplace=True ) assert self.df.sindex is not original_index def test_rebuild_on_row_slice(self): # Select a subset of rows rebuilds original_index = self.df.sindex sliced = self.df.iloc[:1] assert sliced.sindex is not original_index # Slicing all does not rebuild original_index = self.df.sindex sliced = self.df.iloc[:] assert sliced.sindex is original_index # Re-ordering rebuilds sliced = self.df.iloc[::-1] assert sliced.sindex is not original_index def test_rebuild_on_single_col_selection(self): """Selecting a single column should not rebuild the spatial index.""" # Selecting geometry column preserves the index original_index = self.df.sindex geometry_col = self.df["geom"] assert geometry_col.sindex is original_index geometry_col = self.df.geometry assert geometry_col.sindex is original_index @pytest.mark.skipif( not compat.PANDAS_GE_11, reason="Column selection returns a copy on pd<=1.1.0" ) def test_rebuild_on_multiple_col_selection(self): """Selecting a subset of columns preserves the index.""" original_index = self.df.sindex # Selecting a subset of columns preserves the index subset1 = self.df[["geom", "A"]] assert subset1.sindex is original_index subset2 = self.df[["A", "geom"]] assert subset2.sindex is original_index # Skip to accommodate Shapely geometries being unhashable @pytest.mark.skip class TestJoinSindex: def setup_method(self): nybb_filename = geopandas.datasets.get_path("nybb") self.boros = read_file(nybb_filename) def test_merge_geo(self): # First check that we gets hits from the boros frame. tree = self.boros.sindex hits = tree.intersection((1012821.80, 229228.26)) res = [self.boros.iloc[hit]["BoroName"] for hit in hits] assert res == ["Bronx", "Queens"] # Check that we only get the Bronx from this view. first = self.boros[self.boros["BoroCode"] < 3] tree = first.sindex hits = tree.intersection((1012821.80, 229228.26)) res = [first.iloc[hit]["BoroName"] for hit in hits] assert res == ["Bronx"] # Check that we only get Queens from this view. second = self.boros[self.boros["BoroCode"] >= 3] tree = second.sindex hits = tree.intersection((1012821.80, 229228.26)) res = ([second.iloc[hit]["BoroName"] for hit in hits],) assert res == ["Queens"] # Get both the Bronx and Queens again. merged = first.merge(second, how="outer") assert len(merged) == 5 assert merged.sindex.size == 5 tree = merged.sindex hits = tree.intersection((1012821.80, 229228.26)) res = [merged.iloc[hit]["BoroName"] for hit in hits] assert res == ["Bronx", "Queens"] @pytest.mark.skip_no_sindex class TestPygeosInterface: def setup_method(self): data = { "geom": [Point(x, y) for x, y in zip(range(5), range(5))] + [box(10, 10, 20, 20)] # include a box geometry } self.df = GeoDataFrame(data, geometry="geom") self.expected_size = len(data["geom"]) # --------------------------- `intersection` tests -------------------------- # @pytest.mark.parametrize( "test_geom, expected", ( ((-1, -1, -0.5, -0.5), []), ((-0.5, -0.5, 0.5, 0.5), [0]), ((0, 0, 1, 1), [0, 1]), ((0, 0), [0]), ), ) def test_intersection_bounds_tuple(self, test_geom, expected): """Tests the `intersection` method with valid inputs.""" res = list(self.df.sindex.intersection(test_geom)) assert_array_equal(res, expected) @pytest.mark.parametrize("test_geom", ((-1, -1, -0.5), -0.5, None, Point(0, 0))) def test_intersection_invalid_bounds_tuple(self, test_geom): """Tests the `intersection` method with invalid inputs.""" if compat.USE_PYGEOS: with pytest.raises(TypeError): # we raise a useful TypeError self.df.sindex.intersection(test_geom) else: with pytest.raises((TypeError, Exception)): # catch a general exception # rtree raises an RTreeError which we need to catch self.df.sindex.intersection(test_geom) # ------------------------------ `query` tests ------------------------------ # @pytest.mark.parametrize( "predicate, test_geom, expected", ( (None, box(-1, -1, -0.5, -0.5), []), # bbox does not intersect (None, box(-0.5, -0.5, 0.5, 0.5), [0]), # bbox intersects (None, box(0, 0, 1, 1), [0, 1]), # bbox intersects multiple ( None, LineString([(0, 1), (1, 0)]), [0, 1], ), # bbox intersects but not geometry ("intersects", box(-1, -1, -0.5, -0.5), []), # bbox does not intersect ( "intersects", box(-0.5, -0.5, 0.5, 0.5), [0], ), # bbox and geometry intersect ( "intersects", box(0, 0, 1, 1), [0, 1], ), # bbox and geometry intersect multiple ( "intersects", LineString([(0, 1), (1, 0)]), [], ), # bbox intersects but not geometry ("within", box(0.25, 0.28, 0.75, 0.75), []), # does not intersect ("within", box(0, 0, 10, 10), []), # intersects but is not within ("within", box(11, 11, 12, 12), [5]), # intersects and is within ("within", LineString([(0, 1), (1, 0)]), []), # intersects but not within ("contains", box(0, 0, 1, 1), []), # intersects but does not contain ("contains", box(0, 0, 1.001, 1.001), [1]), # intersects and contains ("contains", box(0.5, 0.5, 1.5, 1.5), [1]), # intersects and contains ("contains", box(-1, -1, 2, 2), [0, 1]), # intersects and contains multiple ( "contains", LineString([(0, 1), (1, 0)]), [], ), # intersects but not contains ("touches", box(-1, -1, 0, 0), [0]), # bbox intersects and touches ( "touches", box(-0.5, -0.5, 1.5, 1.5), [], ), # bbox intersects but geom does not touch ( "contains", box(10, 10, 20, 20), [5], ), # contains but does not contains_properly ( "covers", box(-0.5, -0.5, 1, 1), [0, 1], ), # covers (0, 0) and (1, 1) ( "covers", box(0.001, 0.001, 0.99, 0.99), [], ), # does not cover any ( "covers", box(0, 0, 1, 1), [0, 1], ), # covers but does not contain ( "contains_properly", box(0, 0, 1, 1), [], ), # intersects but does not contain ( "contains_properly", box(0, 0, 1.001, 1.001), [1], ), # intersects 2 and contains 1 ( "contains_properly", box(0.5, 0.5, 1.001, 1.001), [1], ), # intersects 1 and contains 1 ( "contains_properly", box(0.5, 0.5, 1.5, 1.5), [1], ), # intersects and contains ( "contains_properly", box(-1, -1, 2, 2), [0, 1], ), # intersects and contains multiple ( "contains_properly", box(10, 10, 20, 20), [], ), # contains but does not contains_properly ), ) def test_query(self, predicate, test_geom, expected): """Tests the `query` method with valid inputs and valid predicates.""" res = self.df.sindex.query(test_geom, predicate=predicate) assert_array_equal(res, expected) def test_query_invalid_geometry(self): """Tests the `query` method with invalid geometry.""" with pytest.raises(TypeError): self.df.sindex.query("notavalidgeom") @pytest.mark.parametrize( "test_geom, expected_value", [ (None, []), (GeometryCollection(), []), (Point(), []), (MultiPolygon(), []), (Polygon(), []), ], ) def test_query_empty_geometry(self, test_geom, expected_value): """Tests the `query` method with empty geometry.""" res = self.df.sindex.query(test_geom) assert_array_equal(res, expected_value) def test_query_invalid_predicate(self): """Tests the `query` method with invalid predicates.""" test_geom = box(-1, -1, -0.5, -0.5) with pytest.raises(ValueError): self.df.sindex.query(test_geom, predicate="test") @pytest.mark.parametrize( "sort, expected", ( (True, [[0, 0, 0], [0, 1, 2]]), # False could be anything, at least we'll know if it changes (False, [[0, 0, 0], [0, 1, 2]]), ), ) def test_query_sorting(self, sort, expected): """Check that results from `query` don't depend on the order of geometries. """ # these geometries come from a reported issue: # https://github.com/geopandas/geopandas/issues/1337 # there is no theoretical reason they were chosen test_polys = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)])]) tree_polys = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) expected = [0, 1, 2] # pass through GeoSeries to have GeoPandas # determine if it should use shapely or pygeos geometry objects tree_df = geopandas.GeoDataFrame(geometry=tree_polys) test_df = geopandas.GeoDataFrame(geometry=test_polys) test_geo = test_df.geometry.values.data[0] res = tree_df.sindex.query(test_geo, sort=sort) # asserting the same elements assert sorted(res) == sorted(expected) # asserting the exact array can fail if sort=False try: assert_array_equal(res, expected) except AssertionError as e: if sort is False: pytest.xfail( "rtree results are known to be unordered, see " "https://github.com/geopandas/geopandas/issues/1337\n" "Expected:\n {}\n".format(expected) + "Got:\n {}\n".format(res.tolist()) ) raise e # ------------------------- `query_bulk` tests -------------------------- # @pytest.mark.parametrize( "predicate, test_geom, expected", ( (None, [(-1, -1, -0.5, -0.5)], [[], []]), (None, [(-0.5, -0.5, 0.5, 0.5)], [[0], [0]]), (None, [(0, 0, 1, 1)], [[0, 0], [0, 1]]), ("intersects", [(-1, -1, -0.5, -0.5)], [[], []]), ("intersects", [(-0.5, -0.5, 0.5, 0.5)], [[0], [0]]), ("intersects", [(0, 0, 1, 1)], [[0, 0], [0, 1]]), # only second geom intersects ("intersects", [(-1, -1, -0.5, -0.5), (-0.5, -0.5, 0.5, 0.5)], [[1], [0]]), # both geoms intersect ( "intersects", [(-1, -1, 1, 1), (-0.5, -0.5, 0.5, 0.5)], [[0, 0, 1], [0, 1, 0]], ), ("within", [(0.25, 0.28, 0.75, 0.75)], [[], []]), # does not intersect ("within", [(0, 0, 10, 10)], [[], []]), # intersects but is not within ("within", [(11, 11, 12, 12)], [[0], [5]]), # intersects and is within ( "contains", [(0, 0, 1, 1)], [[], []], ), # intersects and covers, but does not contain ( "contains", [(0, 0, 1.001, 1.001)], [[0], [1]], ), # intersects 2 and contains 1 ( "contains", [(0.5, 0.5, 1.001, 1.001)], [[0], [1]], ), # intersects 1 and contains 1 ("contains", [(0.5, 0.5, 1.5, 1.5)], [[0], [1]]), # intersects and contains ( "contains", [(-1, -1, 2, 2)], [[0, 0], [0, 1]], ), # intersects and contains multiple ( "contains", [(10, 10, 20, 20)], [[0], [5]], ), # contains but does not contains_properly ("touches", [(-1, -1, 0, 0)], [[0], [0]]), # bbox intersects and touches ( "touches", [(-0.5, -0.5, 1.5, 1.5)], [[], []], ), # bbox intersects but geom does not touch ( "covers", [(-0.5, -0.5, 1, 1)], [[0, 0], [0, 1]], ), # covers (0, 0) and (1, 1) ( "covers", [(0.001, 0.001, 0.99, 0.99)], [[], []], ), # does not cover any ( "covers", [(0, 0, 1, 1)], [[0, 0], [0, 1]], ), # covers but does not contain ( "contains_properly", [(0, 0, 1, 1)], [[], []], ), # intersects but does not contain ( "contains_properly", [(0, 0, 1.001, 1.001)], [[0], [1]], ), # intersects 2 and contains 1 ( "contains_properly", [(0.5, 0.5, 1.001, 1.001)], [[0], [1]], ), # intersects 1 and contains 1 ( "contains_properly", [(0.5, 0.5, 1.5, 1.5)], [[0], [1]], ), # intersects and contains ( "contains_properly", [(-1, -1, 2, 2)], [[0, 0], [0, 1]], ), # intersects and contains multiple ( "contains_properly", [(10, 10, 20, 20)], [[], []], ), # contains but does not contains_properly ), ) def test_query_bulk(self, predicate, test_geom, expected): """Tests the `query_bulk` method with valid inputs and valid predicates. """ # pass through GeoSeries to have GeoPandas # determine if it should use shapely or pygeos geometry objects test_geom = geopandas.GeoSeries( [box(*geom) for geom in test_geom], index=range(len(test_geom)) ) res = self.df.sindex.query_bulk(test_geom, predicate=predicate) assert_array_equal(res, expected) @pytest.mark.parametrize( "test_geoms, expected_value", [ # single empty geometry ([GeometryCollection()], [[], []]), # None should be skipped ([GeometryCollection(), None], [[], []]), ([None], [[], []]), ([None, box(-0.5, -0.5, 0.5, 0.5), None], [[1], [0]]), ], ) def test_query_bulk_empty_geometry(self, test_geoms, expected_value): """Tests the `query_bulk` method with an empty geometry.""" # pass through GeoSeries to have GeoPandas # determine if it should use shapely or pygeos geometry objects # note: for this test, test_geoms (note plural) is a list already test_geoms = geopandas.GeoSeries(test_geoms, index=range(len(test_geoms))) res = self.df.sindex.query_bulk(test_geoms) assert_array_equal(res, expected_value) def test_query_bulk_empty_input_array(self): """Tests the `query_bulk` method with an empty input array.""" test_array = np.array([], dtype=object) expected_value = [[], []] res = self.df.sindex.query_bulk(test_array) assert_array_equal(res, expected_value) def test_query_bulk_invalid_input_geometry(self): """ Tests the `query_bulk` method with invalid input for the `geometry` parameter. """ test_array = "notanarray" with pytest.raises(TypeError): self.df.sindex.query_bulk(test_array) def test_query_bulk_invalid_predicate(self): """Tests the `query_bulk` method with invalid predicates.""" test_geom_bounds = (-1, -1, -0.5, -0.5) test_predicate = "test" # pass through GeoSeries to have GeoPandas # determine if it should use shapely or pygeos geometry objects test_geom = geopandas.GeoSeries([box(*test_geom_bounds)], index=["0"]) with pytest.raises(ValueError): self.df.sindex.query_bulk(test_geom.geometry, predicate=test_predicate) @pytest.mark.parametrize( "predicate, test_geom, expected", ( (None, (-1, -1, -0.5, -0.5), [[], []]), ("intersects", (-1, -1, -0.5, -0.5), [[], []]), ("contains", (-1, -1, 1, 1), [[0], [0]]), ), ) def test_query_bulk_input_type(self, predicate, test_geom, expected): """Tests that query_bulk can accept a GeoSeries, GeometryArray or numpy array. """ # pass through GeoSeries to have GeoPandas # determine if it should use shapely or pygeos geometry objects test_geom = geopandas.GeoSeries([box(*test_geom)], index=["0"]) # test GeoSeries res = self.df.sindex.query_bulk(test_geom, predicate=predicate) assert_array_equal(res, expected) # test GeometryArray res = self.df.sindex.query_bulk(test_geom.geometry, predicate=predicate) assert_array_equal(res, expected) res = self.df.sindex.query_bulk(test_geom.geometry.values, predicate=predicate) assert_array_equal(res, expected) # test numpy array res = self.df.sindex.query_bulk( test_geom.geometry.values.data, predicate=predicate ) assert_array_equal(res, expected) res = self.df.sindex.query_bulk( test_geom.geometry.values.data, predicate=predicate ) assert_array_equal(res, expected) @pytest.mark.parametrize( "sort, expected", ( (True, [[0, 0, 0], [0, 1, 2]]), # False could be anything, at least we'll know if it changes (False, [[0, 0, 0], [0, 1, 2]]), ), ) def test_query_bulk_sorting(self, sort, expected): """Check that results from `query_bulk` don't depend on the order of geometries. """ # these geometries come from a reported issue: # https://github.com/geopandas/geopandas/issues/1337 # there is no theoretical reason they were chosen test_polys = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)])]) tree_polys = GeoSeries( [ Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]), Polygon([(-1, 1), (1, 1), (1, 3), (-1, 3)]), Polygon([(3, 3), (5, 3), (5, 5), (3, 5)]), ] ) # pass through GeoSeries to have GeoPandas # determine if it should use shapely or pygeos geometry objects tree_df = geopandas.GeoDataFrame(geometry=tree_polys) test_df = geopandas.GeoDataFrame(geometry=test_polys) res = tree_df.sindex.query_bulk(test_df.geometry, sort=sort) # asserting the same elements assert sorted(res[0]) == sorted(expected[0]) assert sorted(res[1]) == sorted(expected[1]) # asserting the exact array can fail if sort=False try: assert_array_equal(res, expected) except AssertionError as e: if sort is False: pytest.xfail( "rtree results are known to be unordered, see " "https://github.com/geopandas/geopandas/issues/1337\n" "Expected:\n {}\n".format(expected) + "Got:\n {}\n".format(res.tolist()) ) raise e # ------------------------- `nearest` tests ------------------------- # @pytest.mark.skipif( compat.USE_PYGEOS, reason=("RTree supports sindex.nearest with different behaviour"), ) def test_rtree_nearest_warns(self): df = geopandas.GeoDataFrame({"geometry": []}) with pytest.warns( FutureWarning, match="sindex.nearest using the rtree backend" ): df.sindex.nearest((0, 0, 1, 1), num_results=2) @pytest.mark.skipif( not (compat.USE_PYGEOS and not compat.PYGEOS_GE_010), reason=("PyGEOS < 0.10 does not support sindex.nearest"), ) def test_pygeos_error(self): df = geopandas.GeoDataFrame({"geometry": []}) with pytest.raises(NotImplementedError, match="requires pygeos >= 0.10"): df.sindex.nearest(None) @pytest.mark.skipif( not (compat.USE_PYGEOS and compat.PYGEOS_GE_010), reason=("PyGEOS >= 0.10 is required to test sindex.nearest"), ) @pytest.mark.parametrize("return_all", [True, False]) @pytest.mark.parametrize( "geometry,expected", [ ([0.25, 0.25], [[0], [0]]), ([0.75, 0.75], [[0], [1]]), ], ) def test_nearest_single(self, geometry, expected, return_all): geoms = pygeos.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) p = Point(geometry) res = df.sindex.nearest(p, return_all=return_all) assert_array_equal(res, expected) p = pygeos.points(geometry) res = df.sindex.nearest(p, return_all=return_all) assert_array_equal(res, expected) @pytest.mark.skipif( not compat.USE_PYGEOS or not compat.PYGEOS_GE_010, reason=("PyGEOS >= 0.10 is required to test sindex.nearest"), ) @pytest.mark.parametrize("return_all", [True, False]) @pytest.mark.parametrize( "geometry,expected", [ ([(1, 1), (0, 0)], [[0, 1], [1, 0]]), ([(1, 1), (0.25, 1)], [[0, 1], [1, 1]]), ], ) def test_nearest_multi(self, geometry, expected, return_all): geoms = pygeos.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) ps = [Point(p) for p in geometry] res = df.sindex.nearest(ps, return_all=return_all) assert_array_equal(res, expected) ps = pygeos.points(geometry) res = df.sindex.nearest(ps, return_all=return_all) assert_array_equal(res, expected) s = geopandas.GeoSeries(ps) res = df.sindex.nearest(s, return_all=return_all) assert_array_equal(res, expected) x, y = zip(*geometry) ga = geopandas.points_from_xy(x, y) res = df.sindex.nearest(ga, return_all=return_all) assert_array_equal(res, expected) @pytest.mark.skipif( not compat.USE_PYGEOS or not compat.PYGEOS_GE_010, reason=("PyGEOS >= 0.10 is required to test sindex.nearest"), ) @pytest.mark.parametrize("return_all", [True, False]) @pytest.mark.parametrize( "geometry,expected", [ (None, [[], []]), ([None], [[], []]), ], ) def test_nearest_none(self, geometry, expected, return_all): geoms = pygeos.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) res = df.sindex.nearest(geometry, return_all=return_all) assert_array_equal(res, expected) @pytest.mark.skipif( not compat.USE_PYGEOS or not compat.PYGEOS_GE_010, reason=("PyGEOS >= 0.10 is required to test sindex.nearest"), ) @pytest.mark.parametrize("return_distance", [True, False]) @pytest.mark.parametrize( "return_all,max_distance,expected", [ (True, None, ([[0, 0, 1], [0, 1, 5]], [sqrt(0.5), sqrt(0.5), sqrt(50)])), (False, None, ([[0, 1], [0, 5]], [sqrt(0.5), sqrt(50)])), (True, 1, ([[0, 0], [0, 1]], [sqrt(0.5), sqrt(0.5)])), (False, 1, ([[0], [0]], [sqrt(0.5)])), ], ) def test_nearest_max_distance( self, expected, max_distance, return_all, return_distance ): geoms = pygeos.points(np.arange(10), np.arange(10)) df = geopandas.GeoDataFrame({"geometry": geoms}) ps = [Point(0.5, 0.5), Point(0, 10)] res = df.sindex.nearest( ps, return_all=return_all, max_distance=max_distance, return_distance=return_distance, ) if return_distance: assert_array_equal(res[0], expected[0]) assert_array_equal(res[1], expected[1]) else: assert_array_equal(res, expected[0]) # --------------------------- misc tests ---------------------------- # def test_empty_tree_geometries(self): """Tests building sindex with interleaved empty geometries.""" geoms = [Point(0, 0), None, Point(), Point(1, 1), Point()] df = geopandas.GeoDataFrame(geometry=geoms) assert df.sindex.query(Point(1, 1))[0] == 3 def test_size(self): """Tests the `size` property.""" assert self.df.sindex.size == self.expected_size def test_len(self): """Tests the `__len__` method of spatial indexes.""" assert len(self.df.sindex) == self.expected_size def test_is_empty(self): """Tests the `is_empty` property.""" # create empty tree empty = geopandas.GeoSeries([], dtype=object) assert empty.sindex.is_empty empty = geopandas.GeoSeries([None]) assert empty.sindex.is_empty empty = geopandas.GeoSeries([Point()]) assert empty.sindex.is_empty # create a non-empty tree non_empty = geopandas.GeoSeries([Point(0, 0)]) assert not non_empty.sindex.is_empty @pytest.mark.parametrize( "predicate, expected_shape", [ (None, (2, 396)), ("intersects", (2, 172)), ("within", (2, 172)), ("contains", (2, 0)), ("overlaps", (2, 0)), ("crosses", (2, 0)), ("touches", (2, 0)), ], ) def test_integration_natural_earth(self, predicate, expected_shape): """Tests output sizes for the naturalearth datasets.""" world = read_file(datasets.get_path("naturalearth_lowres")) capitals = read_file(datasets.get_path("naturalearth_cities")) res = world.sindex.query_bulk(capitals.geometry, predicate) assert res.shape == expected_shape @pytest.mark.skipif(not compat.HAS_RTREE, reason="no rtree installed") def test_old_spatial_index_deprecated(): t1 = Polygon([(0, 0), (1, 0), (1, 1)]) t2 = Polygon([(0, 0), (1, 1), (0, 1)]) stream = ((i, item.bounds, None) for i, item in enumerate([t1, t2])) with pytest.warns(FutureWarning): idx = geopandas.sindex.SpatialIndex(stream) assert list(idx.intersection((0, 0, 1, 1))) == [0, 1] geopandas-0.10.2/geopandas/tests/test_testing.py000066400000000000000000000077671413247402600217500ustar00rootroot00000000000000import numpy as np from shapely.geometry import Point, Polygon from pandas import Series from geopandas import GeoDataFrame, GeoSeries from geopandas.array import from_shapely from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal import pytest s1 = GeoSeries( [ Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s2 = GeoSeries( [ Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s3 = Series( [ Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) a = from_shapely( [ Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) s4 = Series(a) df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}) df2 = GeoDataFrame({"col1": [1, 2], "geometry": s2}) s4 = s1.copy() s4.crs = 4326 s5 = s2.copy() s5.crs = 27700 s6 = GeoSeries( [ Polygon([(0, 3), (0, 0), (2, 0), (2, 2)]), Polygon([(2, 2), (4, 2), (4, 4), (2, 4)]), ] ) df4 = GeoDataFrame( {"col1": [1, 2], "geometry": s1.copy(), "geom2": s4.copy(), "geom3": s5.copy()}, crs=3857, ) df5 = GeoDataFrame( {"col1": [1, 2], "geometry": s1.copy(), "geom3": s5.copy(), "geom2": s4.copy()}, crs=3857, ) @pytest.mark.filterwarnings("ignore::UserWarning") def test_geoseries(): assert_geoseries_equal(s1, s2) assert_geoseries_equal(s1, s3, check_series_type=False, check_dtype=False) assert_geoseries_equal(s3, s2, check_series_type=False, check_dtype=False) assert_geoseries_equal(s1, s4, check_series_type=False) with pytest.raises(AssertionError) as error: assert_geoseries_equal(s1, s2, check_less_precise=True) assert "1 out of 2 geometries are not almost equal" in str(error.value) assert "not almost equal: [0]" in str(error.value) with pytest.raises(AssertionError) as error: assert_geoseries_equal(s2, s6, check_less_precise=False) assert "1 out of 2 geometries are not equal" in str(error.value) assert "not equal: [0]" in str(error.value) def test_geodataframe(): assert_geodataframe_equal(df1, df2) with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df2, check_less_precise=True) with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df2[["geometry", "col1"]]) assert_geodataframe_equal(df1, df2[["geometry", "col1"]], check_like=True) df3 = df2.copy() df3.loc[0, "col1"] = 10 with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df3) assert_geodataframe_equal(df5, df4, check_like=True) df5.geom2.crs = 3857 with pytest.raises(AssertionError): assert_geodataframe_equal(df5, df4, check_like=True) def test_equal_nans(): s = GeoSeries([Point(0, 0), np.nan]) assert_geoseries_equal(s, s.copy()) assert_geoseries_equal(s, s.copy(), check_less_precise=True) def test_no_crs(): df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1}, crs=None) df2 = GeoDataFrame({"col1": [1, 2], "geometry": s1}, crs={}) assert_geodataframe_equal(df1, df2) def test_ignore_crs_mismatch(): df1 = GeoDataFrame({"col1": [1, 2], "geometry": s1.copy()}, crs="EPSG:4326") df2 = GeoDataFrame({"col1": [1, 2], "geometry": s1}, crs="EPSG:31370") with pytest.raises(AssertionError): assert_geodataframe_equal(df1, df2) # assert that with `check_crs=False` the assert passes, and also does not # generate any warning from comparing both geometries with different crs with pytest.warns(None) as record: assert_geodataframe_equal(df1, df2, check_crs=False) assert len(record) == 0 def test_almost_equal_but_not_equal(): s_origin = GeoSeries([Point(0, 0)]) s_almost_origin = GeoSeries([Point(0.0000001, 0)]) assert_geoseries_equal(s_origin, s_almost_origin, check_less_precise=True) with pytest.raises(AssertionError): assert_geoseries_equal(s_origin, s_almost_origin) geopandas-0.10.2/geopandas/tests/test_types.py000066400000000000000000000047101413247402600214200ustar00rootroot00000000000000from pandas import DataFrame, Series from shapely.geometry import Point from geopandas import GeoDataFrame, GeoSeries class TestSeries: def setup_method(self): N = self.N = 10 r = 0.5 self.pts = GeoSeries([Point(x, y) for x, y in zip(range(N), range(N))]) self.polys = self.pts.buffer(r) def test_slice(self): assert type(self.pts[:2]) is GeoSeries assert type(self.pts[::2]) is GeoSeries assert type(self.polys[:2]) is GeoSeries def test_head(self): assert type(self.pts.head()) is GeoSeries def test_tail(self): assert type(self.pts.tail()) is GeoSeries def test_sort_index(self): assert type(self.pts.sort_index()) is GeoSeries def test_loc(self): assert type(self.pts.loc[5:]) is GeoSeries def test_iloc(self): assert type(self.pts.iloc[5:]) is GeoSeries def test_fancy(self): idx = (self.pts.index.to_series() % 2).astype(bool) assert type(self.pts[idx]) is GeoSeries def test_take(self): assert type(self.pts.take(list(range(0, self.N, 2)))) is GeoSeries def test_groupby(self): for f, s in self.pts.groupby(lambda x: x % 2): assert type(s) is GeoSeries class TestDataFrame: def setup_method(self): N = 10 self.df = GeoDataFrame( [ {"geometry": Point(x, y), "value1": x + y, "value2": x * y} for x, y in zip(range(N), range(N)) ] ) def test_geometry(self): assert type(self.df.geometry) is GeoSeries # still GeoSeries if different name df2 = GeoDataFrame( { "coords": [Point(x, y) for x, y in zip(range(5), range(5))], "nums": range(5), }, geometry="coords", ) assert type(df2.geometry) is GeoSeries assert type(df2["coords"]) is GeoSeries def test_nongeometry(self): assert type(self.df["value1"]) is Series def test_geometry_multiple(self): assert type(self.df[["geometry", "value1"]]) is GeoDataFrame def test_nongeometry_multiple(self): assert type(self.df[["value1", "value2"]]) is DataFrame def test_slice(self): assert type(self.df[:2]) is GeoDataFrame assert type(self.df[::2]) is GeoDataFrame def test_fancy(self): idx = (self.df.index.to_series() % 2).astype(bool) assert type(self.df[idx]) is GeoDataFrame geopandas-0.10.2/geopandas/tests/util.py000066400000000000000000000102131413247402600201650ustar00rootroot00000000000000import os.path from pandas import Series from geopandas import GeoDataFrame from geopandas.testing import ( # noqa assert_geoseries_equal, geom_almost_equals, geom_equals, ) HERE = os.path.abspath(os.path.dirname(__file__)) PACKAGE_DIR = os.path.dirname(os.path.dirname(HERE)) # mock not used here, but the import from here is used in other modules try: import unittest.mock as mock # noqa except ImportError: import mock # noqa def validate_boro_df(df, case_sensitive=False): """Tests a GeoDataFrame that has been read in from the nybb dataset.""" assert isinstance(df, GeoDataFrame) # Make sure all the columns are there and the geometries # were properly loaded as MultiPolygons assert len(df) == 5 columns = ("BoroCode", "BoroName", "Shape_Leng", "Shape_Area") if case_sensitive: for col in columns: assert col in df.columns else: for col in columns: assert col.lower() in (dfcol.lower() for dfcol in df.columns) assert Series(df.geometry.type).dropna().eq("MultiPolygon").all() def get_srid(df): """Return srid from `df.crs`.""" if df.crs is not None: return df.crs.to_epsg() or 0 return 0 def create_spatialite(con, df): """ Return a SpatiaLite connection containing the nybb table. Parameters ---------- `con`: ``sqlite3.Connection`` `df`: ``GeoDataFrame`` """ with con: geom_col = df.geometry.name srid = get_srid(df) con.execute( "CREATE TABLE IF NOT EXISTS nybb " "( ogc_fid INTEGER PRIMARY KEY" ", borocode INTEGER" ", boroname TEXT" ", shape_leng REAL" ", shape_area REAL" ")" ) con.execute( "SELECT AddGeometryColumn(?, ?, ?, ?)", ("nybb", geom_col, srid, df.geom_type.dropna().iat[0].upper()), ) con.execute("SELECT CreateSpatialIndex(?, ?)", ("nybb", geom_col)) sql_row = "INSERT INTO nybb VALUES(?, ?, ?, ?, ?, GeomFromText(?, ?))" con.executemany( sql_row, ( ( None, row.BoroCode, row.BoroName, row.Shape_Leng, row.Shape_Area, row.geometry.wkt if row.geometry else None, srid, ) for row in df.itertuples(index=False) ), ) def create_postgis(con, df, srid=None, geom_col="geom"): """ Create a nybb table in the test_geopandas PostGIS database. Returns a boolean indicating whether the database table was successfully created """ # Try to create the database, skip the db tests if something goes # wrong # If you'd like these tests to run, create a database called # 'test_geopandas' and enable postgis in it: # > createdb test_geopandas # > psql -c "CREATE EXTENSION postgis" -d test_geopandas if srid is not None: geom_schema = "geometry(MULTIPOLYGON, {})".format(srid) geom_insert = "ST_SetSRID(ST_GeometryFromText(%s), {})".format(srid) else: geom_schema = "geometry" geom_insert = "ST_GeometryFromText(%s)" try: cursor = con.cursor() cursor.execute("DROP TABLE IF EXISTS nybb;") sql = """CREATE TABLE nybb ( {geom_col} {geom_schema}, borocode integer, boroname varchar(40), shape_leng float, shape_area float );""".format( geom_col=geom_col, geom_schema=geom_schema ) cursor.execute(sql) for i, row in df.iterrows(): sql = """INSERT INTO nybb VALUES ({}, %s, %s, %s, %s );""".format( geom_insert ) cursor.execute( sql, ( row["geometry"].wkt, row["BoroCode"], row["BoroName"], row["Shape_Leng"], row["Shape_Area"], ), ) finally: cursor.close() con.commit() geopandas-0.10.2/geopandas/tools/000077500000000000000000000000001413247402600166375ustar00rootroot00000000000000geopandas-0.10.2/geopandas/tools/__init__.py000066400000000000000000000005551413247402600207550ustar00rootroot00000000000000from .crs import explicit_crs_from_epsg from .geocoding import geocode, reverse_geocode from .overlay import overlay from .sjoin import sjoin, sjoin_nearest from .util import collect from .clip import clip __all__ = [ "collect", "explicit_crs_from_epsg", "geocode", "overlay", "reverse_geocode", "sjoin", "sjoin_nearest", "clip", ] geopandas-0.10.2/geopandas/tools/_show_versions.py000066400000000000000000000063531413247402600222670ustar00rootroot00000000000000import importlib import platform import sys def _get_sys_info(): """System information Returns ------- sys_info : dict system and Python version information """ python = sys.version.replace("\n", " ") blob = [ ("python", python), ("executable", sys.executable), ("machine", platform.platform()), ] return dict(blob) def _get_C_info(): """Information on system PROJ, GDAL, GEOS Returns ------- c_info: dict system PROJ information """ try: import pyproj proj_version = pyproj.proj_version_str except Exception: proj_version = None try: import pyproj proj_dir = pyproj.datadir.get_data_dir() except Exception: proj_dir = None try: import shapely._buildcfg geos_version = "{}.{}.{}".format(*shapely._buildcfg.geos_version) geos_dir = shapely._buildcfg.geos_library_path except Exception: geos_version = None geos_dir = None try: import fiona gdal_version = fiona.env.get_gdal_release_name() except Exception: gdal_version = None try: import fiona gdal_dir = fiona.env.GDALDataFinder().search() except Exception: gdal_dir = None blob = [ ("GEOS", geos_version), ("GEOS lib", geos_dir), ("GDAL", gdal_version), ("GDAL data dir", gdal_dir), ("PROJ", proj_version), ("PROJ data dir", proj_dir), ] return dict(blob) def _get_deps_info(): """Overview of the installed version of main dependencies Returns ------- deps_info: dict version information on relevant Python libraries """ deps = [ "geopandas", "pandas", "fiona", "numpy", "shapely", "rtree", "pyproj", "matplotlib", "mapclassify", "geopy", "psycopg2", "geoalchemy2", "pyarrow", "pygeos", ] def get_version(module): return module.__version__ deps_info = {} for modname in deps: try: if modname in sys.modules: mod = sys.modules[modname] else: mod = importlib.import_module(modname) ver = get_version(mod) deps_info[modname] = ver except Exception: deps_info[modname] = None return deps_info def show_versions(): """ Print system information and installed module versions. Examples -------- :: $ python -c "import geopandas; geopandas.show_versions()" """ sys_info = _get_sys_info() deps_info = _get_deps_info() proj_info = _get_C_info() maxlen = max(len(x) for x in deps_info) tpl = "{{k:<{maxlen}}}: {{stat}}".format(maxlen=maxlen) print("\nSYSTEM INFO") print("-----------") for k, stat in sys_info.items(): print(tpl.format(k=k, stat=stat)) print("\nGEOS, GDAL, PROJ INFO") print("---------------------") for k, stat in proj_info.items(): print(tpl.format(k=k, stat=stat)) print("\nPYTHON DEPENDENCIES") print("-------------------") for k, stat in deps_info.items(): print(tpl.format(k=k, stat=stat)) geopandas-0.10.2/geopandas/tools/clip.py000066400000000000000000000143231413247402600201430ustar00rootroot00000000000000""" geopandas.clip ============== A module to clip vector data using GeoPandas. """ import warnings from shapely.geometry import Polygon, MultiPolygon from geopandas import GeoDataFrame, GeoSeries from geopandas.array import _check_crs, _crs_mismatch_warn def _clip_gdf_with_polygon(gdf, poly): """Clip geometry to the polygon extent. Clip an input GeoDataFrame to the polygon extent of the poly parameter. Parameters ---------- gdf : GeoDataFrame, GeoSeries Dataframe to clip. poly : (Multi)Polygon Reference polygon for clipping. Returns ------- GeoDataFrame The returned GeoDataFrame is a clipped subset of gdf that intersects with poly. """ gdf_sub = gdf.iloc[gdf.sindex.query(poly, predicate="intersects")] # For performance reasons points don't need to be intersected with poly non_point_mask = gdf_sub.geom_type != "Point" if not non_point_mask.any(): # only points, directly return return gdf_sub # Clip the data with the polygon if isinstance(gdf_sub, GeoDataFrame): clipped = gdf_sub.copy() clipped.loc[ non_point_mask, clipped._geometry_column_name ] = gdf_sub.geometry.values[non_point_mask].intersection(poly) else: # GeoSeries clipped = gdf_sub.copy() clipped[non_point_mask] = gdf_sub.values[non_point_mask].intersection(poly) return clipped def clip(gdf, mask, keep_geom_type=False): """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The `gdf` will be clipped to the full extent of the clip object. If there are multiple polygons in mask, data from `gdf` will be clipped to the total boundary of all polygons in mask. Parameters ---------- gdf : GeoDataFrame or GeoSeries Vector layer (point, line, polygon) to be clipped to mask. mask : GeoDataFrame, GeoSeries, (Multi)Polygon Polygon vector layer used to clip `gdf`. The mask's geometry is dissolved into one geometric feature and intersected with `gdf`. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed-types). Returns ------- GeoDataFrame or GeoSeries Vector data (points, lines, polygons) from `gdf` clipped to polygon boundary from mask. See also -------- GeoDataFrame.clip : equivalent GeoDataFrame method GeoSeries.clip : equivalent GeoSeries method Examples -------- Clip points (global cities) with a polygon (the South American continent): >>> world = geopandas.read_file( ... geopandas.datasets.get_path('naturalearth_lowres')) >>> south_america = world[world['continent'] == "South America"] >>> capitals = geopandas.read_file( ... geopandas.datasets.get_path('naturalearth_cities')) >>> capitals.shape (202, 2) >>> sa_capitals = geopandas.clip(capitals, south_america) >>> sa_capitals.shape (12, 2) """ if not isinstance(gdf, (GeoDataFrame, GeoSeries)): raise TypeError( "'gdf' should be GeoDataFrame or GeoSeries, got {}".format(type(gdf)) ) if not isinstance(mask, (GeoDataFrame, GeoSeries, Polygon, MultiPolygon)): raise TypeError( "'mask' should be GeoDataFrame, GeoSeries or" "(Multi)Polygon, got {}".format(type(mask)) ) if isinstance(mask, (GeoDataFrame, GeoSeries)): if not _check_crs(gdf, mask): _crs_mismatch_warn(gdf, mask, stacklevel=3) if isinstance(mask, (GeoDataFrame, GeoSeries)): box_mask = mask.total_bounds else: box_mask = mask.bounds box_gdf = gdf.total_bounds if not ( ((box_mask[0] <= box_gdf[2]) and (box_gdf[0] <= box_mask[2])) and ((box_mask[1] <= box_gdf[3]) and (box_gdf[1] <= box_mask[3])) ): return gdf.iloc[:0] if isinstance(mask, (GeoDataFrame, GeoSeries)): poly = mask.geometry.unary_union else: poly = mask clipped = _clip_gdf_with_polygon(gdf, poly) if keep_geom_type: geomcoll_concat = (clipped.geom_type == "GeometryCollection").any() geomcoll_orig = (gdf.geom_type == "GeometryCollection").any() new_collection = geomcoll_concat and not geomcoll_orig if geomcoll_orig: warnings.warn( "keep_geom_type can not be called on a " "GeoDataFrame with GeometryCollection." ) else: polys = ["Polygon", "MultiPolygon"] lines = ["LineString", "MultiLineString", "LinearRing"] points = ["Point", "MultiPoint"] # Check that the gdf for multiple geom types (points, lines and/or polys) orig_types_total = sum( [ gdf.geom_type.isin(polys).any(), gdf.geom_type.isin(lines).any(), gdf.geom_type.isin(points).any(), ] ) # Check how many geometry types are in the clipped GeoDataFrame clip_types_total = sum( [ clipped.geom_type.isin(polys).any(), clipped.geom_type.isin(lines).any(), clipped.geom_type.isin(points).any(), ] ) # Check there aren't any new geom types in the clipped GeoDataFrame more_types = orig_types_total < clip_types_total if orig_types_total > 1: warnings.warn( "keep_geom_type can not be called on a mixed type GeoDataFrame." ) elif new_collection or more_types: orig_type = gdf.geom_type.iloc[0] if new_collection: clipped = clipped.explode(index_parts=False) if orig_type in polys: clipped = clipped.loc[clipped.geom_type.isin(polys)] elif orig_type in lines: clipped = clipped.loc[clipped.geom_type.isin(lines)] return clipped geopandas-0.10.2/geopandas/tools/crs.py000066400000000000000000000027211413247402600200020ustar00rootroot00000000000000import warnings from pyproj import CRS def explicit_crs_from_epsg(crs=None, epsg=None): """ Gets full/explicit CRS from EPSG code provided. Parameters ---------- crs : dict or string, default None An existing crs dict or Proj string with the 'init' key specifying an EPSG code epsg : string or int, default None The EPSG code to lookup """ warnings.warn( "explicit_crs_from_epsg is deprecated. " "You can set the epsg on the GeoDataFrame (gdf) using gdf.crs=epsg", FutureWarning, stacklevel=2, ) if crs is not None: return CRS.from_user_input(crs) elif epsg is not None: return CRS.from_epsg(epsg) raise ValueError("Must pass either crs or epsg.") def epsg_from_crs(crs): """ Returns an epsg code from a crs dict or Proj string. Parameters ---------- crs : dict or string, default None A crs dict or Proj string """ warnings.warn( "epsg_from_crs is deprecated. " "You can get the epsg code from GeoDataFrame (gdf) " "using gdf.crs.to_epsg()", FutureWarning, stacklevel=2, ) crs = CRS.from_user_input(crs) if "init=epsg" in crs.to_string().lower(): epsg_code = crs.to_epsg(0) else: epsg_code = crs.to_epsg() return epsg_code def get_epsg_file_contents(): warnings.warn("get_epsg_file_contents is deprecated.", FutureWarning, stacklevel=2) return "" geopandas-0.10.2/geopandas/tools/geocoding.py000066400000000000000000000130201413247402600211430ustar00rootroot00000000000000from collections import defaultdict import time import pandas as pd from shapely.geometry import Point import geopandas def _get_throttle_time(provider): """ Amount of time to wait between requests to a geocoding API, for providers that specify rate limits in their terms of service. """ import geopy.geocoders # https://operations.osmfoundation.org/policies/nominatim/ if provider == geopy.geocoders.Nominatim: return 1 else: return 0 def geocode(strings, provider=None, **kwargs): """ Geocode a set of strings and get a GeoDataFrame of the resulting points. Parameters ---------- strings : list or Series of addresses to geocode provider : str or geopy.geocoder Specifies geocoding service to use. If none is provided, will use 'photon' (see the Photon's terms of service at: https://photon.komoot.io). Either the string name used by geopy (as specified in geopy.geocoders.SERVICE_TO_GEOCODER) or a geopy Geocoder instance (e.g., geopy.geocoders.Photon) may be used. Some providers require additional arguments such as access keys See each geocoder's specific parameters in geopy.geocoders Notes ----- Ensure proper use of the results by consulting the Terms of Service for your provider. Geocoding requires geopy. Install it using 'pip install geopy'. See also https://github.com/geopy/geopy Examples -------- >>> df = geopandas.tools.geocode( # doctest: +SKIP ... ["boston, ma", "1600 pennsylvania ave. washington, dc"] ... ) >>> df # doctest: +SKIP geometry address 0 POINT (-71.05863 42.35899) Boston, MA, United States 1 POINT (-77.03651 38.89766) 1600 Pennsylvania Ave NW, Washington, DC 20006... """ if provider is None: provider = "photon" throttle_time = _get_throttle_time(provider) return _query(strings, True, provider, throttle_time, **kwargs) def reverse_geocode(points, provider=None, **kwargs): """ Reverse geocode a set of points and get a GeoDataFrame of the resulting addresses. The points Parameters ---------- points : list or Series of Shapely Point objects. x coordinate is longitude y coordinate is latitude provider : str or geopy.geocoder (opt) Specifies geocoding service to use. If none is provided, will use 'photon' (see the Photon's terms of service at: https://photon.komoot.io). Either the string name used by geopy (as specified in geopy.geocoders.SERVICE_TO_GEOCODER) or a geopy Geocoder instance (e.g., geopy.geocoders.Photon) may be used. Some providers require additional arguments such as access keys See each geocoder's specific parameters in geopy.geocoders Notes ----- Ensure proper use of the results by consulting the Terms of Service for your provider. Reverse geocoding requires geopy. Install it using 'pip install geopy'. See also https://github.com/geopy/geopy Examples -------- >>> from shapely.geometry import Point >>> df = geopandas.tools.reverse_geocode( # doctest: +SKIP ... [Point(-71.0594869, 42.3584697), Point(-77.0365305, 38.8977332)] ... ) >>> df # doctest: +SKIP geometry address 0 POINT (-71.05941 42.35837) 29 Court Sq, Boston, MA 02108, United States 1 POINT (-77.03641 38.89766) 1600 Pennsylvania Ave NW, Washington, DC 20006... """ if provider is None: provider = "photon" throttle_time = _get_throttle_time(provider) return _query(points, False, provider, throttle_time, **kwargs) def _query(data, forward, provider, throttle_time, **kwargs): # generic wrapper for calls over lists to geopy Geocoders from geopy.geocoders.base import GeocoderQueryError from geopy.geocoders import get_geocoder_for_service if forward: if not isinstance(data, pd.Series): data = pd.Series(data) else: if not isinstance(data, geopandas.GeoSeries): data = geopandas.GeoSeries(data) if isinstance(provider, str): provider = get_geocoder_for_service(provider) coder = provider(**kwargs) results = {} for i, s in data.items(): try: if forward: results[i] = coder.geocode(s) else: results[i] = coder.reverse((s.y, s.x), exactly_one=True) except (GeocoderQueryError, ValueError): results[i] = (None, None) time.sleep(throttle_time) df = _prepare_geocode_result(results) return df def _prepare_geocode_result(results): """ Helper function for the geocode function Takes a dict where keys are index entries, values are tuples containing: (address, (lat, lon)) """ # Prepare the data for the DataFrame as a dict of lists d = defaultdict(list) index = [] for i, s in results.items(): if s is None: p = Point() address = None else: address, loc = s # loc is lat, lon and we want lon, lat if loc is None: p = Point() else: p = Point(loc[1], loc[0]) d["geometry"].append(p) d["address"].append(address) index.append(i) df = geopandas.GeoDataFrame(d, index=index, crs="EPSG:4326") return df geopandas-0.10.2/geopandas/tools/overlay.py000066400000000000000000000374741413247402600207110ustar00rootroot00000000000000import warnings from functools import reduce import numpy as np import pandas as pd from geopandas import GeoDataFrame, GeoSeries from geopandas.array import _check_crs, _crs_mismatch_warn def _ensure_geometry_column(df): """ Helper function to ensure the geometry column is called 'geometry'. If another column with that name exists, it will be dropped. """ if not df._geometry_column_name == "geometry": if "geometry" in df.columns: df.drop("geometry", axis=1, inplace=True) df.rename( columns={df._geometry_column_name: "geometry"}, copy=False, inplace=True ) df.set_geometry("geometry", inplace=True) def _overlay_intersection(df1, df2): """ Overlay Intersection operation used in overlay function """ # Spatial Index to create intersections idx1, idx2 = df2.sindex.query_bulk(df1.geometry, predicate="intersects", sort=True) # Create pairs of geometries in both dataframes to be intersected if idx1.size > 0 and idx2.size > 0: left = df1.geometry.take(idx1) left.reset_index(drop=True, inplace=True) right = df2.geometry.take(idx2) right.reset_index(drop=True, inplace=True) intersections = left.intersection(right) poly_ix = intersections.type.isin(["Polygon", "MultiPolygon"]) intersections.loc[poly_ix] = intersections[poly_ix].buffer(0) # only keep actual intersecting geometries pairs_intersect = pd.DataFrame({"__idx1": idx1, "__idx2": idx2}) geom_intersect = intersections # merge data for intersecting geometries df1 = df1.reset_index(drop=True) df2 = df2.reset_index(drop=True) dfinter = pairs_intersect.merge( df1.drop(df1._geometry_column_name, axis=1), left_on="__idx1", right_index=True, ) dfinter = dfinter.merge( df2.drop(df2._geometry_column_name, axis=1), left_on="__idx2", right_index=True, suffixes=("_1", "_2"), ) return GeoDataFrame(dfinter, geometry=geom_intersect, crs=df1.crs) else: result = df1.iloc[:0].merge( df2.iloc[:0].drop(df2.geometry.name, axis=1), left_index=True, right_index=True, suffixes=("_1", "_2"), ) result["__idx1"] = None result["__idx2"] = None return result[ result.columns.drop(df1.geometry.name).tolist() + [df1.geometry.name] ] def _overlay_difference(df1, df2): """ Overlay Difference operation used in overlay function """ # spatial index query to find intersections idx1, idx2 = df2.sindex.query_bulk(df1.geometry, predicate="intersects", sort=True) idx1_unique, idx1_unique_indices = np.unique(idx1, return_index=True) idx2_split = np.split(idx2, idx1_unique_indices[1:]) sidx = [ idx2_split.pop(0) if idx in idx1_unique else [] for idx in range(df1.geometry.size) ] # Create differences new_g = [] for geom, neighbours in zip(df1.geometry, sidx): new = reduce( lambda x, y: x.difference(y), [geom] + list(df2.geometry.iloc[neighbours]) ) new_g.append(new) differences = GeoSeries(new_g, index=df1.index, crs=df1.crs) poly_ix = differences.type.isin(["Polygon", "MultiPolygon"]) differences.loc[poly_ix] = differences[poly_ix].buffer(0) geom_diff = differences[~differences.is_empty].copy() dfdiff = df1[~differences.is_empty].copy() dfdiff[dfdiff._geometry_column_name] = geom_diff return dfdiff def _overlay_symmetric_diff(df1, df2): """ Overlay Symmetric Difference operation used in overlay function """ dfdiff1 = _overlay_difference(df1, df2) dfdiff2 = _overlay_difference(df2, df1) dfdiff1["__idx1"] = range(len(dfdiff1)) dfdiff2["__idx2"] = range(len(dfdiff2)) dfdiff1["__idx2"] = np.nan dfdiff2["__idx1"] = np.nan # ensure geometry name (otherwise merge goes wrong) _ensure_geometry_column(dfdiff1) _ensure_geometry_column(dfdiff2) # combine both 'difference' dataframes dfsym = dfdiff1.merge( dfdiff2, on=["__idx1", "__idx2"], how="outer", suffixes=("_1", "_2") ) geometry = dfsym.geometry_1.copy() geometry.name = "geometry" # https://github.com/pandas-dev/pandas/issues/26468 use loc for now geometry.loc[dfsym.geometry_1.isnull()] = dfsym.loc[ dfsym.geometry_1.isnull(), "geometry_2" ] dfsym.drop(["geometry_1", "geometry_2"], axis=1, inplace=True) dfsym.reset_index(drop=True, inplace=True) dfsym = GeoDataFrame(dfsym, geometry=geometry, crs=df1.crs) return dfsym def _overlay_union(df1, df2): """ Overlay Union operation used in overlay function """ dfinter = _overlay_intersection(df1, df2) dfsym = _overlay_symmetric_diff(df1, df2) dfunion = pd.concat([dfinter, dfsym], ignore_index=True, sort=False) # keep geometry column last columns = list(dfunion.columns) columns.remove("geometry") columns = columns + ["geometry"] return dfunion.reindex(columns=columns) def overlay(df1, df2, how="intersection", keep_geom_type=None, make_valid=True): """Perform spatial overlay between two GeoDataFrames. Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union. See the User Guide page :doc:`../../user_guide/set_operations` for details. Parameters ---------- df1 : GeoDataFrame df2 : GeoDataFrame how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. keep_geom_type : bool If True, return only geometries of the same geometry type as df1 has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries. make_valid : bool, default True If True, any invalid input geometries are corrected with a call to `buffer(0)`, if False, a `ValueError` is raised if any input geometries are invalid. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay Examples -------- >>> from shapely.geometry import Polygon >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), ... Polygon([(2,2), (4,2), (4,4), (2,4)])]) >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), ... Polygon([(3,3), (5,3), (5,5), (3,5)])]) >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]}) >>> geopandas.overlay(df1, df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... 3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 4 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... 5 NaN 1.0 MULTIPOLYGON (((2.00000 2.00000, 3.00000 2.000... 6 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5.... >>> geopandas.overlay(df1, df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2 1 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2 2 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... >>> geopandas.overlay(df1, df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 1 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... 2 NaN 1.0 MULTIPOLYGON (((2.00000 2.00000, 3.00000 2.000... 3 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5.... >>> geopandas.overlay(df1, df2, how='difference') geometry df1_data 0 POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 1 1 MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... 2 >>> geopandas.overlay(df1, df2, how='identity') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... 3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 4 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000... See also -------- sjoin : spatial join GeoDataFrame.overlay : equivalent method Notes ------ Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ # Allowed operations allowed_hows = [ "intersection", "union", "identity", "symmetric_difference", "difference", # aka erase ] # Error Messages if how not in allowed_hows: raise ValueError( "`how` was '{0}' but is expected to be in {1}".format(how, allowed_hows) ) if isinstance(df1, GeoSeries) or isinstance(df2, GeoSeries): raise NotImplementedError( "overlay currently only implemented for " "GeoDataFrames" ) if not _check_crs(df1, df2): _crs_mismatch_warn(df1, df2, stacklevel=3) if keep_geom_type is None: keep_geom_type = True keep_geom_type_warning = True else: keep_geom_type_warning = False polys = ["Polygon", "MultiPolygon"] lines = ["LineString", "MultiLineString", "LinearRing"] points = ["Point", "MultiPoint"] for i, df in enumerate([df1, df2]): poly_check = df.geom_type.isin(polys).any() lines_check = df.geom_type.isin(lines).any() points_check = df.geom_type.isin(points).any() if sum([poly_check, lines_check, points_check]) > 1: raise NotImplementedError( "df{} contains mixed geometry types.".format(i + 1) ) if how == "intersection": box_gdf1 = df1.total_bounds box_gdf2 = df2.total_bounds if not ( ((box_gdf1[0] <= box_gdf2[2]) and (box_gdf2[0] <= box_gdf1[2])) and ((box_gdf1[1] <= box_gdf2[3]) and (box_gdf2[1] <= box_gdf1[3])) ): result = df1.iloc[:0].merge( df2.iloc[:0].drop(df2.geometry.name, axis=1), left_index=True, right_index=True, suffixes=("_1", "_2"), ) return result[ result.columns.drop(df1.geometry.name).tolist() + [df1.geometry.name] ] # Computations def _make_valid(df): df = df.copy() if df.geom_type.isin(polys).all(): mask = ~df.geometry.is_valid col = df._geometry_column_name if make_valid: df.loc[mask, col] = df.loc[mask, col].buffer(0) elif mask.any(): raise ValueError( "You have passed make_valid=False along with " f"{mask.sum()} invalid input geometries. " "Use make_valid=True or make sure that all geometries " "are valid before using overlay." ) return df df1 = _make_valid(df1) df2 = _make_valid(df2) with warnings.catch_warnings(): # CRS checked above, suppress array-level warning warnings.filterwarnings("ignore", message="CRS mismatch between the CRS") if how == "difference": result = _overlay_difference(df1, df2) elif how == "intersection": result = _overlay_intersection(df1, df2) elif how == "symmetric_difference": result = _overlay_symmetric_diff(df1, df2) elif how == "union": result = _overlay_union(df1, df2) elif how == "identity": dfunion = _overlay_union(df1, df2) result = dfunion[dfunion["__idx1"].notnull()].copy() if how in ["intersection", "symmetric_difference", "union", "identity"]: result.drop(["__idx1", "__idx2"], axis=1, inplace=True) if keep_geom_type: geom_type = df1.geom_type.iloc[0] # First we filter the geometry types inside GeometryCollections objects # (e.g. GeometryCollection([polygon, point]) -> polygon) # we do this separately on only the relevant rows, as this is an expensive # operation (an expensive no-op for geometry types other than collections) is_collection = result.geom_type == "GeometryCollection" if is_collection.any(): geom_col = result._geometry_column_name collections = result[[geom_col]][is_collection] exploded = collections.reset_index(drop=True).explode(index_parts=True) exploded = exploded.reset_index(level=0) orig_num_geoms_exploded = exploded.shape[0] if geom_type in polys: exploded.loc[~exploded.geom_type.isin(polys), geom_col] = None elif geom_type in lines: exploded.loc[~exploded.geom_type.isin(lines), geom_col] = None elif geom_type in points: exploded.loc[~exploded.geom_type.isin(points), geom_col] = None else: raise TypeError( "`keep_geom_type` does not support {}.".format(geom_type) ) num_dropped_collection = ( orig_num_geoms_exploded - exploded.geometry.isna().sum() ) # level_0 created with above reset_index operation # and represents the original geometry collections # TODO avoiding dissolve to call unary_union in this case could further # improve performance (we only need to collect geometries in their # respective Multi version) dissolved = exploded.dissolve(by="level_0") result.loc[is_collection, geom_col] = dissolved[geom_col].values else: num_dropped_collection = 0 # Now we filter all geometries (in theory we don't need to do this # again for the rows handled above for GeometryCollections, but filtering # them out is probably more expensive as simply including them when this # is typically about only a few rows) orig_num_geoms = result.shape[0] if geom_type in polys: result = result.loc[result.geom_type.isin(polys)] elif geom_type in lines: result = result.loc[result.geom_type.isin(lines)] elif geom_type in points: result = result.loc[result.geom_type.isin(points)] else: raise TypeError("`keep_geom_type` does not support {}.".format(geom_type)) num_dropped = orig_num_geoms - result.shape[0] if (num_dropped > 0 or num_dropped_collection > 0) and keep_geom_type_warning: warnings.warn( "`keep_geom_type=True` in overlay resulted in {} dropped " "geometries of different geometry types than df1 has. " "Set `keep_geom_type=False` to retain all " "geometries".format(num_dropped + num_dropped_collection), UserWarning, stacklevel=2, ) result.reset_index(drop=True, inplace=True) return result geopandas-0.10.2/geopandas/tools/sjoin.py000066400000000000000000000460451413247402600203440ustar00rootroot00000000000000from typing import Optional import warnings import numpy as np import pandas as pd from geopandas import GeoDataFrame from geopandas import _compat as compat from geopandas.array import _check_crs, _crs_mismatch_warn def sjoin( left_df, right_df, how="inner", predicate="intersects", lsuffix="left", rsuffix="right", **kwargs, ): """Spatial join of two GeoDataFrames. See the User Guide page :doc:`../../user_guide/mergingdata` for details. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column predicate : string, default 'intersects' Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as ``left_df.sindex.valid_query_predicates`` or ``right_df.sindex.valid_query_predicates`` Replaces deprecated ``op`` parameter. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). Examples -------- >>> countries = geopandas.read_file(geopandas.datasets.get_\ path("naturalearth_lowres")) >>> cities = geopandas.read_file(geopandas.datasets.get_path("naturalearth_cities")) >>> countries.head() # doctest: +SKIP pop_est continent name \ iso_a3 gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 MULTIPOLY\ GON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 POLYGON (\ (33.90371 -0.95000, 34.07262 -1.05982... 2 603253 Africa W. Sahara ESH 906.5 POLYGON (\ (-8.66559 27.65643, -8.66512 27.58948... 3 35623680 North America Canada CAN 1674000.0 MULTIPOLY\ GON (((-122.84000 49.00000, -122.9742... 4 326625791 North America United States of America USA 18560000.0 MULTIPOLY\ GON (((-122.84000 49.00000, -120.0000... >>> cities.head() name geometry 0 Vatican City POINT (12.45339 41.90328) 1 San Marino POINT (12.44177 43.93610) 2 Vaduz POINT (9.51667 47.13372) 3 Luxembourg POINT (6.13000 49.61166) 4 Palikir POINT (158.14997 6.91664) >>> cities_w_country_data = geopandas.sjoin(cities, countries) >>> cities_w_country_data.head() # doctest: +SKIP name_left geometry index_right pop_est continent name_\ right iso_a3 gdp_md_est 0 Vatican City POINT (12.45339 41.90328) 141 62137802 Europe \ Italy ITA 2221000.0 1 San Marino POINT (12.44177 43.93610) 141 62137802 Europe \ Italy ITA 2221000.0 192 Rome POINT (12.48131 41.89790) 141 62137802 Europe \ Italy ITA 2221000.0 2 Vaduz POINT (9.51667 47.13372) 114 8754413 Europe Au\ stria AUT 416600.0 184 Vienna POINT (16.36469 48.20196) 114 8754413 Europe Au\ stria AUT 416600.0 See also -------- overlay : overlay operation resulting in a new geometry GeoDataFrame.sjoin : equivalent method Notes ------ Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ if "op" in kwargs: op = kwargs.pop("op") deprecation_message = ( "The `op` parameter is deprecated and will be removed" " in a future release. Please use the `predicate` parameter" " instead." ) if predicate != "intersects" and op != predicate: override_message = ( "A non-default value for `predicate` was passed" f' (got `predicate="{predicate}"`' f' in combination with `op="{op}"`).' " The value of `predicate` will be overriden by the value of `op`," " , which may result in unexpected behavior." f"\n{deprecation_message}" ) warnings.warn(override_message, UserWarning, stacklevel=4) else: warnings.warn(deprecation_message, FutureWarning, stacklevel=4) predicate = op if kwargs: first = next(iter(kwargs.keys())) raise TypeError(f"sjoin() got an unexpected keyword argument '{first}'") _basic_checks(left_df, right_df, how, lsuffix, rsuffix) indices = _geom_predicate_query(left_df, right_df, predicate) joined = _frame_join(indices, left_df, right_df, how, lsuffix, rsuffix) return joined def _basic_checks(left_df, right_df, how, lsuffix, rsuffix): """Checks the validity of join input parameters. `how` must be one of the valid options. `'index_'` concatenated with `lsuffix` or `rsuffix` must not already exist as columns in the left or right data frames. Parameters ------------ left_df : GeoDataFrame right_df : GeoData Frame how : str, one of 'left', 'right', 'inner' join type lsuffix : str left index suffix rsuffix : str right index suffix """ if not isinstance(left_df, GeoDataFrame): raise ValueError( "'left_df' should be GeoDataFrame, got {}".format(type(left_df)) ) if not isinstance(right_df, GeoDataFrame): raise ValueError( "'right_df' should be GeoDataFrame, got {}".format(type(right_df)) ) allowed_hows = ["left", "right", "inner"] if how not in allowed_hows: raise ValueError( '`how` was "{}" but is expected to be in {}'.format(how, allowed_hows) ) if not _check_crs(left_df, right_df): _crs_mismatch_warn(left_df, right_df, stacklevel=4) index_left = "index_{}".format(lsuffix) index_right = "index_{}".format(rsuffix) # due to GH 352 if any(left_df.columns.isin([index_left, index_right])) or any( right_df.columns.isin([index_left, index_right]) ): raise ValueError( "'{0}' and '{1}' cannot be names in the frames being" " joined".format(index_left, index_right) ) def _geom_predicate_query(left_df, right_df, predicate): """Compute geometric comparisons and get matching indices. Parameters ---------- left_df : GeoDataFrame right_df : GeoDataFrame predicate : string Binary predicate to query. Returns ------- DataFrame DataFrame with matching indices in columns named `_key_left` and `_key_right`. """ with warnings.catch_warnings(): # We don't need to show our own warning here # TODO remove this once the deprecation has been enforced warnings.filterwarnings( "ignore", "Generated spatial index is empty", FutureWarning ) original_predicate = predicate if predicate == "within": # within is implemented as the inverse of contains # contains is a faster predicate # see discussion at https://github.com/geopandas/geopandas/pull/1421 predicate = "contains" sindex = left_df.sindex input_geoms = right_df.geometry else: # all other predicates are symmetric # keep them the same sindex = right_df.sindex input_geoms = left_df.geometry if sindex: l_idx, r_idx = sindex.query_bulk(input_geoms, predicate=predicate, sort=False) indices = pd.DataFrame({"_key_left": l_idx, "_key_right": r_idx}) else: # when sindex is empty / has no valid geometries indices = pd.DataFrame(columns=["_key_left", "_key_right"], dtype=float) if original_predicate == "within": # within is implemented as the inverse of contains # flip back the results indices = indices.rename( columns={"_key_left": "_key_right", "_key_right": "_key_left"} ) return indices def _frame_join(join_df, left_df, right_df, how, lsuffix, rsuffix): """Join the GeoDataFrames at the DataFrame level. Parameters ---------- join_df : DataFrame Indices and join data returned by the geometric join. Must have columns `_key_left` and `_key_right` with integer indices representing the matches from `left_df` and `right_df` respectively. Additional columns may be included and will be copied to the resultant GeoDataFrame. left_df : GeoDataFrame right_df : GeoDataFrame lsuffix : string Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string Suffix to apply to overlapping column names (right GeoDataFrame). how : string The type of join to use on the DataFrame level. Returns ------- GeoDataFrame Joined GeoDataFrame. """ # the spatial index only allows limited (numeric) index types, but an # index in geopandas may be any arbitrary dtype. so reset both indices now # and store references to the original indices, to be reaffixed later. # GH 352 index_left = "index_{}".format(lsuffix) left_df = left_df.copy(deep=True) try: left_index_name = left_df.index.name left_df.index = left_df.index.rename(index_left) except TypeError: index_left = [ "index_{}".format(lsuffix + str(pos)) for pos, ix in enumerate(left_df.index.names) ] left_index_name = left_df.index.names left_df.index = left_df.index.rename(index_left) left_df = left_df.reset_index() index_right = "index_{}".format(rsuffix) right_df = right_df.copy(deep=True) try: right_index_name = right_df.index.name right_df.index = right_df.index.rename(index_right) except TypeError: index_right = [ "index_{}".format(rsuffix + str(pos)) for pos, ix in enumerate(right_df.index.names) ] right_index_name = right_df.index.names right_df.index = right_df.index.rename(index_right) right_df = right_df.reset_index() # perform join on the dataframes if how == "inner": join_df = join_df.set_index("_key_left") joined = ( left_df.merge(join_df, left_index=True, right_index=True) .merge( right_df.drop(right_df.geometry.name, axis=1), left_on="_key_right", right_index=True, suffixes=("_{}".format(lsuffix), "_{}".format(rsuffix)), ) .set_index(index_left) .drop(["_key_right"], axis=1) ) if isinstance(index_left, list): joined.index.names = left_index_name else: joined.index.name = left_index_name elif how == "left": join_df = join_df.set_index("_key_left") joined = ( left_df.merge(join_df, left_index=True, right_index=True, how="left") .merge( right_df.drop(right_df.geometry.name, axis=1), how="left", left_on="_key_right", right_index=True, suffixes=("_{}".format(lsuffix), "_{}".format(rsuffix)), ) .set_index(index_left) .drop(["_key_right"], axis=1) ) if isinstance(index_left, list): joined.index.names = left_index_name else: joined.index.name = left_index_name else: # how == 'right': joined = ( left_df.drop(left_df.geometry.name, axis=1) .merge( join_df.merge( right_df, left_on="_key_right", right_index=True, how="right" ), left_index=True, right_on="_key_left", how="right", suffixes=("_{}".format(lsuffix), "_{}".format(rsuffix)), ) .set_index(index_right) .drop(["_key_left", "_key_right"], axis=1) ) if isinstance(index_right, list): joined.index.names = right_index_name else: joined.index.name = right_index_name return joined def _nearest_query( left_df: GeoDataFrame, right_df: GeoDataFrame, max_distance: float, how: str, return_distance: bool, ): if not (compat.PYGEOS_GE_010 and compat.USE_PYGEOS): raise NotImplementedError( "Currently, only PyGEOS >= 0.10.0 supports `nearest_all`. " + compat.INSTALL_PYGEOS_ERROR ) # use the opposite of the join direction for the index use_left_as_sindex = how == "right" if use_left_as_sindex: sindex = left_df.sindex query = right_df.geometry else: sindex = right_df.sindex query = left_df.geometry if sindex: res = sindex.nearest( query, return_all=True, max_distance=max_distance, return_distance=return_distance, ) if return_distance: (input_idx, tree_idx), distances = res else: (input_idx, tree_idx) = res distances = None if use_left_as_sindex: l_idx, r_idx = tree_idx, input_idx sort_order = np.argsort(l_idx, kind="stable") l_idx, r_idx = l_idx[sort_order], r_idx[sort_order] if distances is not None: distances = distances[sort_order] else: l_idx, r_idx = input_idx, tree_idx join_df = pd.DataFrame( {"_key_left": l_idx, "_key_right": r_idx, "distances": distances} ) else: # when sindex is empty / has no valid geometries join_df = pd.DataFrame( columns=["_key_left", "_key_right", "distances"], dtype=float ) return join_df def sjoin_nearest( left_df: GeoDataFrame, right_df: GeoDataFrame, how: str = "inner", max_distance: Optional[float] = None, lsuffix: str = "left", rsuffix: str = "right", distance_col: Optional[str] = None, ) -> GeoDataFrame: """Spatial join of two GeoDataFrames based on the distance between their geometries. Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors. See the User Guide page https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html for more details. Parameters ---------- left_df, right_df : GeoDataFrames how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column max_distance : float, default None Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance_col : string, default None If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame. Examples -------- >>> countries = geopandas.read_file(geopandas.datasets.get_\ path("naturalearth_lowres")) >>> cities = geopandas.read_file(geopandas.datasets.get_path("naturalearth_cities")) >>> countries.head(2).name # doctest: +SKIP pop_est continent name \ iso_a3 gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 MULTIPOLY\ GON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 POLYGON (\ (33.90371 -0.95000, 34.07262 -1.05982... >>> cities.head(2).name # doctest: +SKIP name geometry 0 Vatican City POINT (12.45339 41.90328) 1 San Marino POINT (12.44177 43.93610) >>> cities_w_country_data = geopandas.sjoin_nearest(cities, countries) >>> cities_w_country_data[['name_left', 'name_right']].head(2) # doctest: +SKIP name_left geometry index_right pop_est continent name_\ right iso_a3 gdp_md_est 0 Vatican City POINT (12.45339 41.90328) 141 62137802 Europe \ Italy ITA 2221000.0 1 San Marino POINT (12.44177 43.93610) 141 62137802 Europe \ Italy ITA 2221000.0 To include the distances: >>> cities_w_country_data = geopandas.sjoin_nearest\ (cities, countries, distance_col="distances") >>> cities_w_country_data[["name_left", "name_right", \ "distances"]].head(2) # doctest: +SKIP name_left name_right distances 0 Vatican City Italy 0.0 1 San Marino Italy 0.0 In the following example, we get multiple cities for Italy because all results are equidistant (in this case zero because they intersect). In fact, we get 3 results in total: >>> countries_w_city_data = geopandas.sjoin_nearest\ (cities, countries, distance_col="distances", how="right") >>> italy_results = \ countries_w_city_data[countries_w_city_data["name_left"] == "Italy"] >>> italy_results # doctest: +SKIP name_x name_y 141 Vatican City Italy 141 San Marino Italy 141 Rome Italy See also -------- sjoin : binary predicate joins GeoDataFrame.sjoin_nearest : equivalent method Notes ----- Since this join relies on distances, results will be innaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ _basic_checks(left_df, right_df, how, lsuffix, rsuffix) left_df.geometry.values.check_geographic_crs(stacklevel=1) right_df.geometry.values.check_geographic_crs(stacklevel=1) return_distance = distance_col is not None join_df = _nearest_query(left_df, right_df, max_distance, how, return_distance) if return_distance: join_df = join_df.rename(columns={"distances": distance_col}) else: join_df.pop("distances") joined = _frame_join(join_df, left_df, right_df, how, lsuffix, rsuffix) if return_distance: columns = [c for c in joined.columns if c != distance_col] + [distance_col] joined = joined[columns] return joined geopandas-0.10.2/geopandas/tools/tests/000077500000000000000000000000001413247402600200015ustar00rootroot00000000000000geopandas-0.10.2/geopandas/tools/tests/__init__.py000066400000000000000000000000001413247402600221000ustar00rootroot00000000000000geopandas-0.10.2/geopandas/tools/tests/test_clip.py000066400000000000000000000355421413247402600223520ustar00rootroot00000000000000"""Tests for the clip module.""" import warnings from distutils.version import LooseVersion import numpy as np import pandas as pd import shapely from shapely.geometry import ( Polygon, Point, LineString, LinearRing, GeometryCollection, MultiPoint, ) import geopandas from geopandas import GeoDataFrame, GeoSeries, clip from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal import pytest pytestmark = pytest.mark.skip_no_sindex pandas_133 = pd.__version__ == LooseVersion("1.3.3") @pytest.fixture def point_gdf(): """Create a point GeoDataFrame.""" pts = np.array([[2, 2], [3, 4], [9, 8], [-12, -15]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def pointsoutside_nooverlap_gdf(): """Create a point GeoDataFrame. Its points are all outside the single rectangle, and its bounds are outside the single rectangle's.""" pts = np.array([[5, 15], [15, 15], [15, 20]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def pointsoutside_overlap_gdf(): """Create a point GeoDataFrame. Its points are all outside the single rectangle, and its bounds are overlapping the single rectangle's.""" pts = np.array([[5, 15], [15, 15], [15, 5]]) gdf = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") return gdf @pytest.fixture def single_rectangle_gdf(): """Create a single rectangle for clipping.""" poly_inters = Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)]) gdf = GeoDataFrame([1], geometry=[poly_inters], crs="EPSG:3857") gdf["attr2"] = "site-boundary" return gdf @pytest.fixture def larger_single_rectangle_gdf(): """Create a slightly larger rectangle for clipping. The smaller single rectangle is used to test the edge case where slivers are returned when you clip polygons. This fixture is larger which eliminates the slivers in the clip return. """ poly_inters = Polygon([(-5, -5), (-5, 15), (15, 15), (15, -5), (-5, -5)]) gdf = GeoDataFrame([1], geometry=[poly_inters], crs="EPSG:3857") gdf["attr2"] = ["study area"] return gdf @pytest.fixture def buffered_locations(point_gdf): """Buffer points to create a multi-polygon.""" buffered_locs = point_gdf buffered_locs["geometry"] = buffered_locs.buffer(4) buffered_locs["type"] = "plot" return buffered_locs @pytest.fixture def donut_geometry(buffered_locations, single_rectangle_gdf): """Make a geometry with a hole in the middle (a donut).""" donut = geopandas.overlay( buffered_locations, single_rectangle_gdf, how="symmetric_difference" ) return donut @pytest.fixture def two_line_gdf(): """Create Line Objects For Testing""" linea = LineString([(1, 1), (2, 2), (3, 2), (5, 3)]) lineb = LineString([(3, 4), (5, 7), (12, 2), (10, 5), (9, 7.5)]) gdf = GeoDataFrame([1, 2], geometry=[linea, lineb], crs="EPSG:3857") return gdf @pytest.fixture def multi_poly_gdf(donut_geometry): """Create a multi-polygon GeoDataFrame.""" multi_poly = donut_geometry.unary_union out_df = GeoDataFrame(geometry=GeoSeries(multi_poly), crs="EPSG:3857") out_df["attr"] = ["pool"] return out_df @pytest.fixture def multi_line(two_line_gdf): """Create a multi-line GeoDataFrame. This GDF has one multiline and one regular line.""" # Create a single and multi line object multiline_feat = two_line_gdf.unary_union linec = LineString([(2, 1), (3, 1), (4, 1), (5, 2)]) out_df = GeoDataFrame(geometry=GeoSeries([multiline_feat, linec]), crs="EPSG:3857") out_df["attr"] = ["road", "stream"] return out_df @pytest.fixture def multi_point(point_gdf): """Create a multi-point GeoDataFrame.""" multi_point = point_gdf.unary_union out_df = GeoDataFrame( geometry=GeoSeries( [multi_point, Point(2, 5), Point(-11, -14), Point(-10, -12)] ), crs="EPSG:3857", ) out_df["attr"] = ["tree", "another tree", "shrub", "berries"] return out_df @pytest.fixture def mixed_gdf(): """Create a Mixed Polygon and LineString For Testing""" point = Point([(2, 3), (11, 4), (7, 2), (8, 9), (1, 13)]) line = LineString([(1, 1), (2, 2), (3, 2), (5, 3), (12, 1)]) poly = Polygon([(3, 4), (5, 2), (12, 2), (10, 5), (9, 7.5)]) ring = LinearRing([(1, 1), (2, 2), (3, 2), (5, 3), (12, 1)]) gdf = GeoDataFrame( [1, 2, 3, 4], geometry=[point, poly, line, ring], crs="EPSG:3857" ) return gdf @pytest.fixture def geomcol_gdf(): """Create a Mixed Polygon and LineString For Testing""" point = Point([(2, 3), (11, 4), (7, 2), (8, 9), (1, 13)]) poly = Polygon([(3, 4), (5, 2), (12, 2), (10, 5), (9, 7.5)]) coll = GeometryCollection([point, poly]) gdf = GeoDataFrame([1], geometry=[coll], crs="EPSG:3857") return gdf @pytest.fixture def sliver_line(): """Create a line that will create a point when clipped.""" linea = LineString([(10, 5), (13, 5), (15, 5)]) lineb = LineString([(1, 1), (2, 2), (3, 2), (5, 3), (12, 1)]) gdf = GeoDataFrame([1, 2], geometry=[linea, lineb], crs="EPSG:3857") return gdf def test_not_gdf(single_rectangle_gdf): """Non-GeoDataFrame inputs raise attribute errors.""" with pytest.raises(TypeError): clip((2, 3), single_rectangle_gdf) with pytest.raises(TypeError): clip(single_rectangle_gdf, (2, 3)) def test_returns_gdf(point_gdf, single_rectangle_gdf): """Test that function returns a GeoDataFrame (or GDF-like) object.""" out = clip(point_gdf, single_rectangle_gdf) assert isinstance(out, GeoDataFrame) def test_returns_series(point_gdf, single_rectangle_gdf): """Test that function returns a GeoSeries if GeoSeries is passed.""" out = clip(point_gdf.geometry, single_rectangle_gdf) assert isinstance(out, GeoSeries) def test_non_overlapping_geoms(): """Test that a bounding box returns empty if the extents don't overlap""" unit_box = Polygon([(0, 0), (0, 1), (1, 1), (1, 0), (0, 0)]) unit_gdf = GeoDataFrame([1], geometry=[unit_box], crs="EPSG:3857") non_overlapping_gdf = unit_gdf.copy() non_overlapping_gdf = non_overlapping_gdf.geometry.apply( lambda x: shapely.affinity.translate(x, xoff=20) ) out = clip(unit_gdf, non_overlapping_gdf) assert_geodataframe_equal(out, unit_gdf.iloc[:0]) out2 = clip(unit_gdf.geometry, non_overlapping_gdf) assert_geoseries_equal(out2, GeoSeries(crs=unit_gdf.crs)) def test_clip_points(point_gdf, single_rectangle_gdf): """Test clipping a points GDF with a generic polygon geometry.""" clip_pts = clip(point_gdf, single_rectangle_gdf) pts = np.array([[2, 2], [3, 4], [9, 8]]) exp = GeoDataFrame([Point(xy) for xy in pts], columns=["geometry"], crs="EPSG:3857") assert_geodataframe_equal(clip_pts, exp) def test_clip_points_geom_col_rename(point_gdf, single_rectangle_gdf): """Test clipping a points GDF with a generic polygon geometry.""" point_gdf_geom_col_rename = point_gdf.rename_geometry("geometry2") clip_pts = clip(point_gdf_geom_col_rename, single_rectangle_gdf) pts = np.array([[2, 2], [3, 4], [9, 8]]) exp = GeoDataFrame( [Point(xy) for xy in pts], columns=["geometry2"], crs="EPSG:3857", geometry="geometry2", ) assert_geodataframe_equal(clip_pts, exp) def test_clip_poly(buffered_locations, single_rectangle_gdf): """Test clipping a polygon GDF with a generic polygon geometry.""" clipped_poly = clip(buffered_locations, single_rectangle_gdf) assert len(clipped_poly.geometry) == 3 assert all(clipped_poly.geom_type == "Polygon") def test_clip_poly_geom_col_rename(buffered_locations, single_rectangle_gdf): """Test clipping a polygon GDF with a generic polygon geometry.""" poly_gdf_geom_col_rename = buffered_locations.rename_geometry("geometry2") clipped_poly = clip(poly_gdf_geom_col_rename, single_rectangle_gdf) assert len(clipped_poly.geometry) == 3 assert "geometry" not in clipped_poly.keys() assert "geometry2" in clipped_poly.keys() def test_clip_poly_series(buffered_locations, single_rectangle_gdf): """Test clipping a polygon GDF with a generic polygon geometry.""" clipped_poly = clip(buffered_locations.geometry, single_rectangle_gdf) assert len(clipped_poly) == 3 assert all(clipped_poly.geom_type == "Polygon") @pytest.mark.xfail(pandas_133, reason="Regression in pandas 1.3.3 (GH #2101)") def test_clip_multipoly_keep_slivers(multi_poly_gdf, single_rectangle_gdf): """Test a multi poly object where the return includes a sliver. Also the bounds of the object should == the bounds of the clip object if they fully overlap (as they do in these fixtures).""" clipped = clip(multi_poly_gdf, single_rectangle_gdf) assert np.array_equal(clipped.total_bounds, single_rectangle_gdf.total_bounds) # Assert returned data is a geometry collection given sliver geoms assert "GeometryCollection" in clipped.geom_type[0] @pytest.mark.xfail(pandas_133, reason="Regression in pandas 1.3.3 (GH #2101)") def test_clip_multipoly_keep_geom_type(multi_poly_gdf, single_rectangle_gdf): """Test a multi poly object where the return includes a sliver. Also the bounds of the object should == the bounds of the clip object if they fully overlap (as they do in these fixtures).""" clipped = clip(multi_poly_gdf, single_rectangle_gdf, keep_geom_type=True) assert np.array_equal(clipped.total_bounds, single_rectangle_gdf.total_bounds) # Assert returned data is a not geometry collection assert (clipped.geom_type == "Polygon").any() def test_clip_single_multipoly_no_extra_geoms( buffered_locations, larger_single_rectangle_gdf ): """When clipping a multi-polygon feature, no additional geom types should be returned.""" multi = buffered_locations.dissolve(by="type").reset_index() clipped = clip(multi, larger_single_rectangle_gdf) assert clipped.geom_type[0] == "Polygon" def test_clip_multiline(multi_line, single_rectangle_gdf): """Test that clipping a multiline feature with a poly returns expected output.""" clipped = clip(multi_line, single_rectangle_gdf) assert clipped.geom_type[0] == "MultiLineString" def test_clip_multipoint(single_rectangle_gdf, multi_point): """Clipping a multipoint feature with a polygon works as expected. should return a geodataframe with a single multi point feature""" clipped = clip(multi_point, single_rectangle_gdf) assert clipped.geom_type[0] == "MultiPoint" assert hasattr(clipped, "attr") # All points should intersect the clip geom assert len(clipped) == 2 clipped_mutltipoint = MultiPoint( [ Point(2, 2), Point(3, 4), Point(9, 8), ] ) assert clipped.iloc[0].geometry.wkt == clipped_mutltipoint.wkt assert all(clipped.intersects(single_rectangle_gdf.unary_union)) def test_clip_lines(two_line_gdf, single_rectangle_gdf): """Test what happens when you give the clip_extent a line GDF.""" clip_line = clip(two_line_gdf, single_rectangle_gdf) assert len(clip_line.geometry) == 2 def test_clip_with_multipolygon(buffered_locations, single_rectangle_gdf): """Test clipping a polygon with a multipolygon.""" multi = buffered_locations.dissolve(by="type").reset_index() clipped = clip(single_rectangle_gdf, multi) assert clipped.geom_type[0] == "Polygon" def test_mixed_geom(mixed_gdf, single_rectangle_gdf): """Test clipping a mixed GeoDataFrame""" clipped = clip(mixed_gdf, single_rectangle_gdf) assert ( clipped.geom_type[0] == "Point" and clipped.geom_type[1] == "Polygon" and clipped.geom_type[2] == "LineString" ) def test_mixed_series(mixed_gdf, single_rectangle_gdf): """Test clipping a mixed GeoSeries""" clipped = clip(mixed_gdf.geometry, single_rectangle_gdf) assert ( clipped.geom_type[0] == "Point" and clipped.geom_type[1] == "Polygon" and clipped.geom_type[2] == "LineString" ) def test_clip_warning_no_extra_geoms(buffered_locations, single_rectangle_gdf): """Test a user warning is provided if no new geometry types are found.""" with pytest.warns(UserWarning): clip(buffered_locations, single_rectangle_gdf, True) warnings.warn( "keep_geom_type was called when no extra geometry types existed.", UserWarning, ) def test_clip_with_polygon(single_rectangle_gdf): """Test clip when using a shapely object""" polygon = Polygon([(0, 0), (5, 12), (10, 0), (0, 0)]) clipped = clip(single_rectangle_gdf, polygon) exp_poly = polygon.intersection( Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)]) ) exp = GeoDataFrame([1], geometry=[exp_poly], crs="EPSG:3857") exp["attr2"] = "site-boundary" assert_geodataframe_equal(clipped, exp) def test_clip_with_line_extra_geom(single_rectangle_gdf, sliver_line): """When the output of a clipped line returns a geom collection, and keep_geom_type is True, no geometry collections should be returned.""" clipped = clip(sliver_line, single_rectangle_gdf, keep_geom_type=True) assert len(clipped.geometry) == 1 # Assert returned data is a not geometry collection assert not (clipped.geom_type == "GeometryCollection").any() def test_clip_line_keep_slivers(single_rectangle_gdf, sliver_line): """Test the correct output if a point is returned from a line only geometry type.""" clipped = clip(sliver_line, single_rectangle_gdf) # Assert returned data is a geometry collection given sliver geoms assert "Point" == clipped.geom_type[0] assert "LineString" == clipped.geom_type[1] def test_clip_no_box_overlap(pointsoutside_nooverlap_gdf, single_rectangle_gdf): """Test clip when intersection is empty and boxes do not overlap.""" clipped = clip(pointsoutside_nooverlap_gdf, single_rectangle_gdf) assert len(clipped) == 0 def test_clip_box_overlap(pointsoutside_overlap_gdf, single_rectangle_gdf): """Test clip when intersection is empty and boxes do overlap.""" clipped = clip(pointsoutside_overlap_gdf, single_rectangle_gdf) assert len(clipped) == 0 def test_warning_extra_geoms_mixed(single_rectangle_gdf, mixed_gdf): """Test the correct warnings are raised if keep_geom_type is called on a mixed GDF""" with pytest.warns(UserWarning): clip(mixed_gdf, single_rectangle_gdf, keep_geom_type=True) def test_warning_geomcoll(single_rectangle_gdf, geomcol_gdf): """Test the correct warnings are raised if keep_geom_type is called on a GDF with GeometryCollection""" with pytest.warns(UserWarning): clip(geomcol_gdf, single_rectangle_gdf, keep_geom_type=True) def test_warning_crs_mismatch(point_gdf, single_rectangle_gdf): with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): clip(point_gdf, single_rectangle_gdf.to_crs(4326)) geopandas-0.10.2/geopandas/tools/tests/test_sjoin.py000066400000000000000000001053671413247402600225500ustar00rootroot00000000000000from distutils.version import LooseVersion import math from typing import Sequence from geopandas.testing import assert_geodataframe_equal import numpy as np import pandas as pd from shapely.geometry import Point, Polygon, GeometryCollection import geopandas import geopandas._compat as compat from geopandas import GeoDataFrame, GeoSeries, read_file, sjoin, sjoin_nearest from geopandas.testing import assert_geoseries_equal from pandas.testing import assert_frame_equal import pytest TEST_NEAREST = compat.PYGEOS_GE_010 and compat.USE_PYGEOS pytestmark = pytest.mark.skip_no_sindex @pytest.fixture() def dfs(request): polys1 = GeoSeries( [ Polygon([(0, 0), (5, 0), (5, 5), (0, 5)]), Polygon([(5, 5), (6, 5), (6, 6), (5, 6)]), Polygon([(6, 0), (9, 0), (9, 3), (6, 3)]), ] ) polys2 = GeoSeries( [ Polygon([(1, 1), (4, 1), (4, 4), (1, 4)]), Polygon([(4, 4), (7, 4), (7, 7), (4, 7)]), Polygon([(7, 7), (10, 7), (10, 10), (7, 10)]), ] ) df1 = GeoDataFrame({"geometry": polys1, "df1": [0, 1, 2]}) df2 = GeoDataFrame({"geometry": polys2, "df2": [3, 4, 5]}) if request.param == "string-index": df1.index = ["a", "b", "c"] df2.index = ["d", "e", "f"] if request.param == "named-index": df1.index.name = "df1_ix" df2.index.name = "df2_ix" if request.param == "multi-index": i1 = ["a", "b", "c"] i2 = ["d", "e", "f"] df1 = df1.set_index([i1, i2]) df2 = df2.set_index([i2, i1]) if request.param == "named-multi-index": i1 = ["a", "b", "c"] i2 = ["d", "e", "f"] df1 = df1.set_index([i1, i2]) df2 = df2.set_index([i2, i1]) df1.index.names = ["df1_ix1", "df1_ix2"] df2.index.names = ["df2_ix1", "df2_ix2"] # construction expected frames expected = {} part1 = df1.copy().reset_index().rename(columns={"index": "index_left"}) part2 = ( df2.copy() .iloc[[0, 1, 1, 2]] .reset_index() .rename(columns={"index": "index_right"}) ) part1["_merge"] = [0, 1, 2] part2["_merge"] = [0, 0, 1, 3] exp = pd.merge(part1, part2, on="_merge", how="outer") expected["intersects"] = exp.drop("_merge", axis=1).copy() part1 = df1.copy().reset_index().rename(columns={"index": "index_left"}) part2 = df2.copy().reset_index().rename(columns={"index": "index_right"}) part1["_merge"] = [0, 1, 2] part2["_merge"] = [0, 3, 3] exp = pd.merge(part1, part2, on="_merge", how="outer") expected["contains"] = exp.drop("_merge", axis=1).copy() part1["_merge"] = [0, 1, 2] part2["_merge"] = [3, 1, 3] exp = pd.merge(part1, part2, on="_merge", how="outer") expected["within"] = exp.drop("_merge", axis=1).copy() return [request.param, df1, df2, expected] class TestSpatialJoin: @pytest.mark.parametrize( "how, lsuffix, rsuffix, expected_cols", [ ("left", "left", "right", {"col_left", "col_right", "index_right"}), ("inner", "left", "right", {"col_left", "col_right", "index_right"}), ("right", "left", "right", {"col_left", "col_right", "index_left"}), ("left", "lft", "rgt", {"col_lft", "col_rgt", "index_rgt"}), ("inner", "lft", "rgt", {"col_lft", "col_rgt", "index_rgt"}), ("right", "lft", "rgt", {"col_lft", "col_rgt", "index_lft"}), ], ) def test_suffixes(self, how: str, lsuffix: str, rsuffix: str, expected_cols): left = GeoDataFrame({"col": [1], "geometry": [Point(0, 0)]}) right = GeoDataFrame({"col": [1], "geometry": [Point(0, 0)]}) joined = sjoin(left, right, how=how, lsuffix=lsuffix, rsuffix=rsuffix) assert set(joined.columns) == expected_cols | set(("geometry",)) @pytest.mark.parametrize("dfs", ["default-index", "string-index"], indirect=True) def test_crs_mismatch(self, dfs): index, df1, df2, expected = dfs df1.crs = "epsg:4326" with pytest.warns(UserWarning, match="CRS mismatch between the CRS"): sjoin(df1, df2) @pytest.mark.parametrize("dfs", ["default-index"], indirect=True) @pytest.mark.parametrize("op", ["intersects", "contains", "within"]) def test_deprecated_op_param(self, dfs, op): _, df1, df2, _ = dfs with pytest.warns(FutureWarning, match="`op` parameter is deprecated"): sjoin(df1, df2, op=op) @pytest.mark.parametrize("dfs", ["default-index"], indirect=True) @pytest.mark.parametrize("op", ["intersects", "contains", "within"]) @pytest.mark.parametrize("predicate", ["contains", "within"]) def test_deprecated_op_param_nondefault_predicate(self, dfs, op, predicate): _, df1, df2, _ = dfs match = "use the `predicate` parameter instead" if op != predicate: warntype = UserWarning match = ( "`predicate` will be overriden by the value of `op`" + r"(.|\s)*" + match ) else: warntype = FutureWarning with pytest.warns(warntype, match=match): sjoin(df1, df2, predicate=predicate, op=op) @pytest.mark.parametrize("dfs", ["default-index"], indirect=True) def test_unknown_kwargs(self, dfs): _, df1, df2, _ = dfs with pytest.raises( TypeError, match=r"sjoin\(\) got an unexpected keyword argument 'extra_param'", ): sjoin(df1, df2, extra_param="test") @pytest.mark.filterwarnings("ignore:The `op` parameter:FutureWarning") @pytest.mark.parametrize( "dfs", [ "default-index", "string-index", "named-index", "multi-index", "named-multi-index", ], indirect=True, ) @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) @pytest.mark.parametrize("predicate_kw", ["predicate", "op"]) def test_inner(self, predicate, predicate_kw, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how="inner", **{predicate_kw: predicate}) exp = expected[predicate].dropna().copy() exp = exp.drop("geometry_y", axis=1).rename(columns={"geometry_x": "geometry"}) exp[["df1", "df2"]] = exp[["df1", "df2"]].astype("int64") if index == "default-index": exp[["index_left", "index_right"]] = exp[ ["index_left", "index_right"] ].astype("int64") if index == "named-index": exp[["df1_ix", "df2_ix"]] = exp[["df1_ix", "df2_ix"]].astype("int64") exp = exp.set_index("df1_ix").rename(columns={"df2_ix": "index_right"}) if index in ["default-index", "string-index"]: exp = exp.set_index("index_left") exp.index.name = None if index == "multi-index": exp = exp.set_index(["level_0_x", "level_1_x"]).rename( columns={"level_0_y": "index_right0", "level_1_y": "index_right1"} ) exp.index.names = df1.index.names if index == "named-multi-index": exp = exp.set_index(["df1_ix1", "df1_ix2"]).rename( columns={"df2_ix1": "index_right0", "df2_ix2": "index_right1"} ) exp.index.names = df1.index.names assert_frame_equal(res, exp) @pytest.mark.parametrize( "dfs", [ "default-index", "string-index", "named-index", "multi-index", "named-multi-index", ], indirect=True, ) @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) def test_left(self, predicate, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how="left", predicate=predicate) if index in ["default-index", "string-index"]: exp = expected[predicate].dropna(subset=["index_left"]).copy() elif index == "named-index": exp = expected[predicate].dropna(subset=["df1_ix"]).copy() elif index == "multi-index": exp = expected[predicate].dropna(subset=["level_0_x"]).copy() elif index == "named-multi-index": exp = expected[predicate].dropna(subset=["df1_ix1"]).copy() exp = exp.drop("geometry_y", axis=1).rename(columns={"geometry_x": "geometry"}) exp["df1"] = exp["df1"].astype("int64") if index == "default-index": exp["index_left"] = exp["index_left"].astype("int64") # TODO: in result the dtype is object res["index_right"] = res["index_right"].astype(float) elif index == "named-index": exp[["df1_ix"]] = exp[["df1_ix"]].astype("int64") exp = exp.set_index("df1_ix").rename(columns={"df2_ix": "index_right"}) if index in ["default-index", "string-index"]: exp = exp.set_index("index_left") exp.index.name = None if index == "multi-index": exp = exp.set_index(["level_0_x", "level_1_x"]).rename( columns={"level_0_y": "index_right0", "level_1_y": "index_right1"} ) exp.index.names = df1.index.names if index == "named-multi-index": exp = exp.set_index(["df1_ix1", "df1_ix2"]).rename( columns={"df2_ix1": "index_right0", "df2_ix2": "index_right1"} ) exp.index.names = df1.index.names assert_frame_equal(res, exp) def test_empty_join(self): # Check joins resulting in empty gdfs. polygons = geopandas.GeoDataFrame( { "col2": [1, 2], "geometry": [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), ], } ) not_in = geopandas.GeoDataFrame({"col1": [1], "geometry": [Point(-0.5, 0.5)]}) empty = sjoin(not_in, polygons, how="left", predicate="intersects") assert empty.index_right.isnull().all() empty = sjoin(not_in, polygons, how="right", predicate="intersects") assert empty.index_left.isnull().all() empty = sjoin(not_in, polygons, how="inner", predicate="intersects") assert empty.empty @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) @pytest.mark.parametrize( "empty", [ GeoDataFrame(geometry=[GeometryCollection(), GeometryCollection()]), GeoDataFrame(geometry=GeoSeries()), ], ) def test_join_with_empty(self, predicate, empty): # Check joins with empty geometry columns/dataframes. polygons = geopandas.GeoDataFrame( { "col2": [1, 2], "geometry": [ Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]), Polygon([(1, 0), (2, 0), (2, 1), (1, 1)]), ], } ) result = sjoin(empty, polygons, how="left", predicate=predicate) assert result.index_right.isnull().all() result = sjoin(empty, polygons, how="right", predicate=predicate) assert result.index_left.isnull().all() result = sjoin(empty, polygons, how="inner", predicate=predicate) assert result.empty @pytest.mark.parametrize("dfs", ["default-index", "string-index"], indirect=True) def test_sjoin_invalid_args(self, dfs): index, df1, df2, expected = dfs with pytest.raises(ValueError, match="'left_df' should be GeoDataFrame"): sjoin(df1.geometry, df2) with pytest.raises(ValueError, match="'right_df' should be GeoDataFrame"): sjoin(df1, df2.geometry) @pytest.mark.parametrize( "dfs", [ "default-index", "string-index", "named-index", "multi-index", "named-multi-index", ], indirect=True, ) @pytest.mark.parametrize("predicate", ["intersects", "contains", "within"]) def test_right(self, predicate, dfs): index, df1, df2, expected = dfs res = sjoin(df1, df2, how="right", predicate=predicate) if index in ["default-index", "string-index"]: exp = expected[predicate].dropna(subset=["index_right"]).copy() elif index == "named-index": exp = expected[predicate].dropna(subset=["df2_ix"]).copy() elif index == "multi-index": exp = expected[predicate].dropna(subset=["level_0_y"]).copy() elif index == "named-multi-index": exp = expected[predicate].dropna(subset=["df2_ix1"]).copy() exp = exp.drop("geometry_x", axis=1).rename(columns={"geometry_y": "geometry"}) exp["df2"] = exp["df2"].astype("int64") if index == "default-index": exp["index_right"] = exp["index_right"].astype("int64") res["index_left"] = res["index_left"].astype(float) elif index == "named-index": exp[["df2_ix"]] = exp[["df2_ix"]].astype("int64") exp = exp.set_index("df2_ix").rename(columns={"df1_ix": "index_left"}) if index in ["default-index", "string-index"]: exp = exp.set_index("index_right") exp = exp.reindex(columns=res.columns) exp.index.name = None if index == "multi-index": exp = exp.set_index(["level_0_y", "level_1_y"]).rename( columns={"level_0_x": "index_left0", "level_1_x": "index_left1"} ) exp.index.names = df2.index.names if index == "named-multi-index": exp = exp.set_index(["df2_ix1", "df2_ix2"]).rename( columns={"df1_ix1": "index_left0", "df1_ix2": "index_left1"} ) exp.index.names = df2.index.names # GH 1364 fix of behaviour was done in pandas 1.1.0 if predicate == "within" and str(pd.__version__) >= LooseVersion("1.1.0"): exp = exp.sort_index() assert_frame_equal(res, exp, check_index_type=False) class TestSpatialJoinNYBB: def setup_method(self): nybb_filename = geopandas.datasets.get_path("nybb") self.polydf = read_file(nybb_filename) self.crs = self.polydf.crs N = 20 b = [int(x) for x in self.polydf.total_bounds] self.pointdf = GeoDataFrame( [ {"geometry": Point(x, y), "pointattr1": x + y, "pointattr2": x - y} for x, y in zip( range(b[0], b[2], int((b[2] - b[0]) / N)), range(b[1], b[3], int((b[3] - b[1]) / N)), ) ], crs=self.crs, ) def test_geometry_name(self): # test sjoin is working with other geometry name polydf_original_geom_name = self.polydf.geometry.name self.polydf = self.polydf.rename(columns={"geometry": "new_geom"}).set_geometry( "new_geom" ) assert polydf_original_geom_name != self.polydf.geometry.name res = sjoin(self.polydf, self.pointdf, how="left") assert self.polydf.geometry.name == res.geometry.name def test_sjoin_left(self): df = sjoin(self.pointdf, self.polydf, how="left") assert df.shape == (21, 8) for i, row in df.iterrows(): assert row.geometry.type == "Point" assert "pointattr1" in df.columns assert "BoroCode" in df.columns def test_sjoin_right(self): # the inverse of left df = sjoin(self.pointdf, self.polydf, how="right") df2 = sjoin(self.polydf, self.pointdf, how="left") assert df.shape == (12, 8) assert df.shape == df2.shape for i, row in df.iterrows(): assert row.geometry.type == "MultiPolygon" for i, row in df2.iterrows(): assert row.geometry.type == "MultiPolygon" def test_sjoin_inner(self): df = sjoin(self.pointdf, self.polydf, how="inner") assert df.shape == (11, 8) def test_sjoin_predicate(self): # points within polygons df = sjoin(self.pointdf, self.polydf, how="left", predicate="within") assert df.shape == (21, 8) assert df.loc[1]["BoroName"] == "Staten Island" # points contain polygons? never happens so we should have nulls df = sjoin(self.pointdf, self.polydf, how="left", predicate="contains") assert df.shape == (21, 8) assert np.isnan(df.loc[1]["Shape_Area"]) def test_sjoin_bad_predicate(self): # AttributeError: 'Point' object has no attribute 'spandex' with pytest.raises(ValueError): sjoin(self.pointdf, self.polydf, how="left", predicate="spandex") def test_sjoin_duplicate_column_name(self): pointdf2 = self.pointdf.rename(columns={"pointattr1": "Shape_Area"}) df = sjoin(pointdf2, self.polydf, how="left") assert "Shape_Area_left" in df.columns assert "Shape_Area_right" in df.columns @pytest.mark.parametrize("how", ["left", "right", "inner"]) def test_sjoin_named_index(self, how): # original index names should be unchanged pointdf2 = self.pointdf.copy() pointdf2.index.name = "pointid" polydf = self.polydf.copy() polydf.index.name = "polyid" res = sjoin(pointdf2, polydf, how=how) assert pointdf2.index.name == "pointid" assert polydf.index.name == "polyid" # original index name should pass through to result if how == "right": assert res.index.name == "polyid" else: # how == "left", how == "inner" assert res.index.name == "pointid" def test_sjoin_values(self): # GH190 self.polydf.index = [1, 3, 4, 5, 6] df = sjoin(self.pointdf, self.polydf, how="left") assert df.shape == (21, 8) df = sjoin(self.polydf, self.pointdf, how="left") assert df.shape == (12, 8) @pytest.mark.xfail def test_no_overlapping_geometry(self): # Note: these tests are for correctly returning GeoDataFrame # when result of the join is empty df_inner = sjoin(self.pointdf.iloc[17:], self.polydf, how="inner") df_left = sjoin(self.pointdf.iloc[17:], self.polydf, how="left") df_right = sjoin(self.pointdf.iloc[17:], self.polydf, how="right") expected_inner_df = pd.concat( [ self.pointdf.iloc[:0], pd.Series(name="index_right", dtype="int64"), self.polydf.drop("geometry", axis=1).iloc[:0], ], axis=1, ) expected_inner = GeoDataFrame(expected_inner_df) expected_right_df = pd.concat( [ self.pointdf.drop("geometry", axis=1).iloc[:0], pd.concat( [ pd.Series(name="index_left", dtype="int64"), pd.Series(name="index_right", dtype="int64"), ], axis=1, ), self.polydf, ], axis=1, ) expected_right = GeoDataFrame(expected_right_df).set_index("index_right") expected_left_df = pd.concat( [ self.pointdf.iloc[17:], pd.Series(name="index_right", dtype="int64"), self.polydf.iloc[:0].drop("geometry", axis=1), ], axis=1, ) expected_left = GeoDataFrame(expected_left_df) assert expected_inner.equals(df_inner) assert expected_right.equals(df_right) assert expected_left.equals(df_left) @pytest.mark.skip("Not implemented") def test_sjoin_outer(self): df = sjoin(self.pointdf, self.polydf, how="outer") assert df.shape == (21, 8) def test_sjoin_empty_geometries(self): # https://github.com/geopandas/geopandas/issues/944 empty = GeoDataFrame(geometry=[GeometryCollection()] * 3) df = sjoin(self.pointdf.append(empty), self.polydf, how="left") assert df.shape == (24, 8) df2 = sjoin(self.pointdf, self.polydf.append(empty), how="left") assert df2.shape == (21, 8) @pytest.mark.parametrize("predicate", ["intersects", "within", "contains"]) def test_sjoin_no_valid_geoms(self, predicate): """Tests a completely empty GeoDataFrame.""" empty = GeoDataFrame(geometry=[], crs=self.pointdf.crs) assert sjoin(self.pointdf, empty, how="inner", predicate=predicate).empty assert sjoin(self.pointdf, empty, how="right", predicate=predicate).empty assert sjoin(empty, self.pointdf, how="inner", predicate=predicate).empty assert sjoin(empty, self.pointdf, how="left", predicate=predicate).empty def test_empty_sjoin_return_duplicated_columns(self): nybb = geopandas.read_file(geopandas.datasets.get_path("nybb")) nybb2 = nybb.copy() nybb2.geometry = nybb2.translate(200000) # to get non-overlapping result = geopandas.sjoin(nybb, nybb2) assert "BoroCode_right" in result.columns assert "BoroCode_left" in result.columns class TestSpatialJoinNaturalEarth: def setup_method(self): world_path = geopandas.datasets.get_path("naturalearth_lowres") cities_path = geopandas.datasets.get_path("naturalearth_cities") self.world = read_file(world_path) self.cities = read_file(cities_path) def test_sjoin_inner(self): # GH637 countries = self.world[["geometry", "name"]] countries = countries.rename(columns={"name": "country"}) cities_with_country = sjoin( self.cities, countries, how="inner", predicate="intersects" ) assert cities_with_country.shape == (172, 4) @pytest.mark.skipif( TEST_NEAREST, reason=("This test can only be run _without_ PyGEOS >= 0.10 installed"), ) def test_no_nearest_all(): df1 = geopandas.GeoDataFrame({"geometry": []}) df2 = geopandas.GeoDataFrame({"geometry": []}) with pytest.raises( NotImplementedError, match="Currently, only PyGEOS >= 0.10.0 supports `nearest_all`", ): sjoin_nearest(df1, df2) @pytest.mark.skipif( not TEST_NEAREST, reason=( "PyGEOS >= 0.10.0" " must be installed and activated via the geopandas.compat module to" " test sjoin_nearest" ), ) class TestNearest: @pytest.mark.parametrize( "how_kwargs", ({}, {"how": "inner"}, {"how": "left"}, {"how": "right"}) ) def test_allowed_hows(self, how_kwargs): left = geopandas.GeoDataFrame({"geometry": []}) right = geopandas.GeoDataFrame({"geometry": []}) sjoin_nearest(left, right, **how_kwargs) # no error @pytest.mark.parametrize("how", ("outer", "abcde")) def test_invalid_hows(self, how: str): left = geopandas.GeoDataFrame({"geometry": []}) right = geopandas.GeoDataFrame({"geometry": []}) with pytest.raises(ValueError, match="`how` was"): sjoin_nearest(left, right, how=how) @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_right_df_how_left(self, distance_col: str): # all records from left and no results from right left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest( left, right, how="left", distance_col=distance_col, ) assert_geoseries_equal(joined["geometry"], left["geometry"]) assert joined["index_right"].isna().all() if distance_col is not None: assert joined[distance_col].isna().all() @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_right_df_how_right(self, distance_col: str): # no records in joined left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest( left, right, how="right", distance_col=distance_col, ) assert joined.empty if distance_col is not None: assert distance_col in joined @pytest.mark.parametrize("how", ["inner", "left"]) @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_left_df(self, how, distance_col: str): right = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) left = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest(left, right, how=how, distance_col=distance_col) assert joined.empty if distance_col is not None: assert distance_col in joined @pytest.mark.parametrize("distance_col", (None, "distance")) def test_empty_left_df_how_right(self, distance_col: str): right = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) left = geopandas.GeoDataFrame({"geometry": []}) joined = sjoin_nearest( left, right, how="right", distance_col=distance_col, ) assert_geoseries_equal(joined["geometry"], right["geometry"]) assert joined["index_left"].isna().all() if distance_col is not None: assert joined[distance_col].isna().all() @pytest.mark.parametrize("how", ["inner", "left"]) def test_empty_join_due_to_max_distance(self, how): # after applying max_distance the join comes back empty # (as in NaN in the joined columns) left = geopandas.GeoDataFrame({"geometry": [Point(0, 0)]}) right = geopandas.GeoDataFrame({"geometry": [Point(1, 1), Point(2, 2)]}) joined = sjoin_nearest( left, right, how=how, max_distance=1, distance_col="distances", ) expected = left.copy() expected["index_right"] = [np.nan] expected["distances"] = [np.nan] if how == "inner": expected = expected.dropna() expected["index_right"] = expected["index_right"].astype("int64") assert_geodataframe_equal(joined, expected) def test_empty_join_due_to_max_distance_how_right(self): # after applying max_distance the join comes back empty # (as in NaN in the joined columns) left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": [Point(2, 2)]}) joined = sjoin_nearest( left, right, how="right", max_distance=1, distance_col="distances", ) expected = right.copy() expected["index_left"] = [np.nan] expected["distances"] = [np.nan] expected = expected[["index_left", "geometry", "distances"]] assert_geodataframe_equal(joined, expected) @pytest.mark.parametrize("how", ["inner", "left"]) def test_max_distance(self, how): left = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) right = geopandas.GeoDataFrame({"geometry": [Point(1, 1), Point(2, 2)]}) joined = sjoin_nearest( left, right, how=how, max_distance=1, distance_col="distances", ) expected = left.copy() expected["index_right"] = [np.nan, 0] expected["distances"] = [np.nan, 0] if how == "inner": expected = expected.dropna() expected["index_right"] = expected["index_right"].astype("int64") assert_geodataframe_equal(joined, expected) def test_max_distance_how_right(self): left = geopandas.GeoDataFrame({"geometry": [Point(1, 1), Point(2, 2)]}) right = geopandas.GeoDataFrame({"geometry": [Point(0, 0), Point(1, 1)]}) joined = sjoin_nearest( left, right, how="right", max_distance=1, distance_col="distances", ) expected = right.copy() expected["index_left"] = [np.nan, 0] expected["distances"] = [np.nan, 0] expected = expected[["index_left", "geometry", "distances"]] assert_geodataframe_equal(joined, expected) @pytest.mark.parametrize("how", ["inner", "left"]) @pytest.mark.parametrize( "geo_left, geo_right, expected_left, expected_right, distances", [ ( [Point(0, 0), Point(1, 1)], [Point(1, 1)], [0, 1], [0, 0], [math.sqrt(2), 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0)], [0, 1], [1, 0], [0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(0, 0)], [0, 0, 1], [1, 2, 0], [0, 0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(2, 2)], [0, 1], [1, 0], [0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0.25, 1)], [0, 1], [1, 0], [math.sqrt(0.25 ** 2 + 1), 0], ), ( [Point(0, 0), Point(1, 1)], [Point(-10, -10), Point(100, 100)], [0, 1], [0, 0], [math.sqrt(10 ** 2 + 10 ** 2), math.sqrt(11 ** 2 + 11 ** 2)], ), ( [Point(0, 0), Point(1, 1)], [Point(x, y) for x, y in zip(np.arange(10), np.arange(10))], [0, 1], [0, 1], [0, 0], ), ( [Point(0, 0), Point(1, 1), Point(0, 0)], [Point(1.1, 1.1), Point(0, 0)], [0, 1, 2], [1, 0, 1], [0, np.sqrt(0.1 ** 2 + 0.1 ** 2), 0], ), ], ) def test_sjoin_nearest_left( self, geo_left, geo_right, expected_left: Sequence[int], expected_right: Sequence[int], distances: Sequence[float], how, ): left = geopandas.GeoDataFrame({"geometry": geo_left}) right = geopandas.GeoDataFrame({"geometry": geo_right}) expected_gdf = left.iloc[expected_left].copy() expected_gdf["index_right"] = expected_right # without distance col joined = sjoin_nearest(left, right, how=how) # inner / left join give a different row order check_like = how == "inner" assert_geodataframe_equal(expected_gdf, joined, check_like=check_like) # with distance col expected_gdf["distance_col"] = np.array(distances, dtype=float) joined = sjoin_nearest(left, right, how=how, distance_col="distance_col") assert_geodataframe_equal(expected_gdf, joined, check_like=check_like) @pytest.mark.parametrize( "geo_left, geo_right, expected_left, expected_right, distances", [ ([Point(0, 0), Point(1, 1)], [Point(1, 1)], [1], [0], [0]), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0)], [1, 0], [0, 1], [0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(0, 0)], [1, 0, 0], [0, 1, 2], [0, 0, 0], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0, 0), Point(2, 2)], [1, 0, 1], [0, 1, 2], [0, 0, math.sqrt(2)], ), ( [Point(0, 0), Point(1, 1)], [Point(1, 1), Point(0.25, 1)], [1, 1], [0, 1], [0, 0.75], ), ( [Point(0, 0), Point(1, 1)], [Point(-10, -10), Point(100, 100)], [0, 1], [0, 1], [math.sqrt(10 ** 2 + 10 ** 2), math.sqrt(99 ** 2 + 99 ** 2)], ), ( [Point(0, 0), Point(1, 1)], [Point(x, y) for x, y in zip(np.arange(10), np.arange(10))], [0, 1] + [1] * 8, list(range(10)), [0, 0] + [np.sqrt(x ** 2 + x ** 2) for x in np.arange(1, 9)], ), ( [Point(0, 0), Point(1, 1), Point(0, 0)], [Point(1.1, 1.1), Point(0, 0)], [1, 0, 2], [0, 1, 1], [np.sqrt(0.1 ** 2 + 0.1 ** 2), 0, 0], ), ], ) def test_sjoin_nearest_right( self, geo_left, geo_right, expected_left: Sequence[int], expected_right: Sequence[int], distances: Sequence[float], ): left = geopandas.GeoDataFrame({"geometry": geo_left}) right = geopandas.GeoDataFrame({"geometry": geo_right}) expected_gdf = right.iloc[expected_right].copy() expected_gdf["index_left"] = expected_left expected_gdf = expected_gdf[["index_left", "geometry"]] # without distance col joined = sjoin_nearest(left, right, how="right") assert_geodataframe_equal(expected_gdf, joined) # with distance col expected_gdf["distance_col"] = np.array(distances, dtype=float) joined = sjoin_nearest(left, right, how="right", distance_col="distance_col") assert_geodataframe_equal(expected_gdf, joined) @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS") def test_sjoin_nearest_inner(self): # check equivalency of left and inner join countries = read_file(geopandas.datasets.get_path("naturalearth_lowres")) cities = read_file(geopandas.datasets.get_path("naturalearth_cities")) countries = countries[["geometry", "name"]].rename(columns={"name": "country"}) # default: inner and left give the same result result1 = sjoin_nearest(cities, countries, distance_col="dist") assert result1.shape[0] == cities.shape[0] result2 = sjoin_nearest(cities, countries, distance_col="dist", how="inner") assert_geodataframe_equal(result2, result1) result3 = sjoin_nearest(cities, countries, distance_col="dist", how="left") assert_geodataframe_equal(result3, result1, check_like=True) # with max_distance: rows that go above are dropped in case of inner result4 = sjoin_nearest(cities, countries, distance_col="dist", max_distance=1) assert_geodataframe_equal( result4, result1[result1["dist"] < 1], check_like=True ) result5 = sjoin_nearest( cities, countries, distance_col="dist", max_distance=1, how="left" ) assert result5.shape[0] == cities.shape[0] result5 = result5.dropna() result5["index_right"] = result5["index_right"].astype("int64") assert_geodataframe_equal(result5, result4, check_like=True) geopandas-0.10.2/geopandas/tools/tests/test_tools.py000066400000000000000000000056121413247402600225560ustar00rootroot00000000000000from distutils.version import LooseVersion from shapely.geometry import LineString, MultiPoint, Point import pyproj from pyproj import CRS from geopandas import GeoSeries from geopandas.tools import collect from geopandas.tools.crs import epsg_from_crs, explicit_crs_from_epsg import pytest # pyproj 2.3.1 fixed a segfault for the case working in an environment with # 'init' dicts (https://github.com/pyproj4/pyproj/issues/415) PYPROJ_LT_231 = LooseVersion(pyproj.__version__) < LooseVersion("2.3.1") class TestTools: def setup_method(self): self.p1 = Point(0, 0) self.p2 = Point(1, 1) self.p3 = Point(2, 2) self.mpc = MultiPoint([self.p1, self.p2, self.p3]) self.mp1 = MultiPoint([self.p1, self.p2]) self.line1 = LineString([(3, 3), (4, 4)]) def test_collect_single(self): result = collect(self.p1) assert self.p1.equals(result) def test_collect_single_force_multi(self): result = collect(self.p1, multi=True) expected = MultiPoint([self.p1]) assert expected.equals(result) def test_collect_multi(self): result = collect(self.mp1) assert self.mp1.equals(result) def test_collect_multi_force_multi(self): result = collect(self.mp1) assert self.mp1.equals(result) def test_collect_list(self): result = collect([self.p1, self.p2, self.p3]) assert self.mpc.equals(result) def test_collect_GeoSeries(self): s = GeoSeries([self.p1, self.p2, self.p3]) result = collect(s) assert self.mpc.equals(result) def test_collect_mixed_types(self): with pytest.raises(ValueError): collect([self.p1, self.line1]) def test_collect_mixed_multi(self): with pytest.raises(ValueError): collect([self.mpc, self.mp1]) @pytest.mark.skipif(PYPROJ_LT_231, reason="segfault") def test_epsg_from_crs(self): with pytest.warns(FutureWarning): assert epsg_from_crs({"init": "epsg:4326"}) == 4326 assert epsg_from_crs({"init": "EPSG:4326"}) == 4326 assert epsg_from_crs("+init=epsg:4326") == 4326 @pytest.mark.skipif(PYPROJ_LT_231, reason="segfault") def test_explicit_crs_from_epsg(self): with pytest.warns(FutureWarning): assert explicit_crs_from_epsg(epsg=4326) == CRS.from_epsg(4326) assert explicit_crs_from_epsg(epsg="4326") == CRS.from_epsg(4326) assert explicit_crs_from_epsg(crs={"init": "epsg:4326"}) == CRS.from_dict( {"init": "epsg:4326"} ) assert explicit_crs_from_epsg(crs="+init=epsg:4326") == CRS.from_proj4( "+init=epsg:4326" ) @pytest.mark.filterwarnings("ignore:explicit_crs_from_epsg:FutureWarning") def test_explicit_crs_from_epsg__missing_input(self): with pytest.raises(ValueError): explicit_crs_from_epsg() geopandas-0.10.2/geopandas/tools/util.py000066400000000000000000000026571413247402600202000ustar00rootroot00000000000000import pandas as pd from shapely.geometry import MultiLineString, MultiPoint, MultiPolygon from shapely.geometry.base import BaseGeometry _multi_type_map = { "Point": MultiPoint, "LineString": MultiLineString, "Polygon": MultiPolygon, } def collect(x, multi=False): """ Collect single part geometries into their Multi* counterpart Parameters ---------- x : an iterable or Series of Shapely geometries, a GeoSeries, or a single Shapely geometry multi : boolean, default False if True, force returned geometries to be Multi* even if they only have one component. """ if isinstance(x, BaseGeometry): x = [x] elif isinstance(x, pd.Series): x = list(x) # We cannot create GeometryCollection here so all types # must be the same. If there is more than one element, # they cannot be Multi*, i.e., can't pass in combination of # Point and MultiPoint... or even just MultiPoint t = x[0].type if not all(g.type == t for g in x): raise ValueError("Geometry type must be homogeneous") if len(x) > 1 and t.startswith("Multi"): raise ValueError("Cannot collect {0}. Must have single geometries".format(t)) if len(x) == 1 and (t.startswith("Multi") or not multi): # If there's only one single part geom and we're not forcing to # multi, then just return it return x[0] return _multi_type_map[t](x) geopandas-0.10.2/readthedocs.yml000066400000000000000000000002171413247402600165460ustar00rootroot00000000000000version: 2 formats: [] conda: environment: doc/environment.yml python: version: 3 install: - method: pip path: . geopandas-0.10.2/requirements-dev.txt000066400000000000000000000004751413247402600176040ustar00rootroot00000000000000# required fiona>=1.8 pandas>=0.25 pyproj>=2.2.0 shapely>=1.6 # geodatabase access psycopg2>=2.5.1 SQLAlchemy>=0.8.3 # geocoding geopy # plotting matplotlib>=2.2 mapclassify # testing pytest>=3.1.0 pytest-cov codecov # spatial access methods rtree>=0.8 # styling black pre-commit # PostGIS writing GeoAlchemy2 geopandas-0.10.2/setup.cfg000066400000000000000000000013521413247402600153600ustar00rootroot00000000000000[bdist_wheel] universal = 1 # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] VCS = git style = pep440 versionfile_source = geopandas/_version.py versionfile_build = geopandas/_version.py tag_prefix = v parentdir_prefix = geopandas- [tool:pytest] markers = web: tests that need network connectivity xfail_strict = True [flake8] # Black enforces 88 characters line length max_line_length = 88 ignore = E203, # Space before : (needed for black formatting of slices) W503, # Line break before binary operator (needed for black) F821 # TODO: fix F821 issues (see GH #1120) and remove this line geopandas-0.10.2/setup.py000066400000000000000000000037111413247402600152520ustar00rootroot00000000000000#!/usr/bin/env/python """Installation script """ import os try: from setuptools import setup except ImportError: from distutils.core import setup import versioneer LONG_DESCRIPTION = """GeoPandas is a project to add support for geographic data to `pandas`_ objects. The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of `pandas`_ and `shapely`_, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. .. _pandas: http://pandas.pydata.org .. _shapely: http://shapely.readthedocs.io/en/latest/ """ if os.environ.get("READTHEDOCS", False) == "True": INSTALL_REQUIRES = [] else: INSTALL_REQUIRES = [ "pandas >= 0.25.0", "shapely >= 1.6", "fiona >= 1.8", "pyproj >= 2.2.0", ] # get all data dirs in the datasets module data_files = [] for item in os.listdir("geopandas/datasets"): if not item.startswith("__"): if os.path.isdir(os.path.join("geopandas/datasets/", item)): data_files.append(os.path.join("datasets", item, "*")) elif item.endswith(".zip"): data_files.append(os.path.join("datasets", item)) data_files.append("tests/data/*") setup( name="geopandas", version=versioneer.get_version(), description="Geographic pandas extensions", license="BSD", author="GeoPandas contributors", author_email="kjordahl@alum.mit.edu", url="http://geopandas.org", long_description=LONG_DESCRIPTION, packages=[ "geopandas", "geopandas.io", "geopandas.tools", "geopandas.datasets", "geopandas.tests", "geopandas.tools.tests", ], package_data={"geopandas": data_files}, python_requires=">=3.7", install_requires=INSTALL_REQUIRES, cmdclass=versioneer.get_cmdclass(), ) geopandas-0.10.2/versioneer.py000066400000000000000000002003231413247402600162710ustar00rootroot00000000000000 # Version: 0.16 """The Versioneer - like a rocketeer, but for versions. The Versioneer ============== * like a rocketeer, but for versions! * https://github.com/warner/python-versioneer * Brian Warner * License: Public Domain * Compatible With: python2.6, 2.7, 3.3, 3.4, 3.5, and pypy * [![Latest Version] (https://pypip.in/version/versioneer/badge.svg?style=flat) ](https://pypi.python.org/pypi/versioneer/) * [![Build Status] (https://travis-ci.org/warner/python-versioneer.png?branch=master) ](https://travis-ci.org/warner/python-versioneer) This is a tool for managing a recorded version number in distutils-based python projects. The goal is to remove the tedious and error-prone "update the embedded version string" step from your release process. Making a new release should be as easy as recording a new tag in your version-control system, and maybe making new tarballs. ## Quick Install * `pip install versioneer` to somewhere to your $PATH * add a `[versioneer]` section to your setup.cfg (see below) * run `versioneer install` in your source tree, commit the results ## Version Identifiers Source trees come from a variety of places: * a version-control system checkout (mostly used by developers) * a nightly tarball, produced by build automation * a snapshot tarball, produced by a web-based VCS browser, like github's "tarball from tag" feature * a release tarball, produced by "setup.py sdist", distributed through PyPI Within each source tree, the version identifier (either a string or a number, this tool is format-agnostic) can come from a variety of places: * ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows about recent "tags" and an absolute revision-id * the name of the directory into which the tarball was unpacked * an expanded VCS keyword ($Id$, etc) * a `_version.py` created by some earlier build step For released software, the version identifier is closely related to a VCS tag. Some projects use tag names that include more than just the version string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool needs to strip the tag prefix to extract the version identifier. For unreleased software (between tags), the version identifier should provide enough information to help developers recreate the same tree, while also giving them an idea of roughly how old the tree is (after version 1.2, before version 1.3). Many VCS systems can report a description that captures this, for example `git describe --tags --dirty --always` reports things like "0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the 0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has uncommitted changes. The version identifier is used for multiple purposes: * to allow the module to self-identify its version: `myproject.__version__` * to choose a name and prefix for a 'setup.py sdist' tarball ## Theory of Operation Versioneer works by adding a special `_version.py` file into your source tree, where your `__init__.py` can import it. This `_version.py` knows how to dynamically ask the VCS tool for version information at import time. `_version.py` also contains `$Revision$` markers, and the installation process marks `_version.py` to have this marker rewritten with a tag name during the `git archive` command. As a result, generated tarballs will contain enough information to get the proper version. To allow `setup.py` to compute a version too, a `versioneer.py` is added to the top level of your source tree, next to `setup.py` and the `setup.cfg` that configures it. This overrides several distutils/setuptools commands to compute the version when invoked, and changes `setup.py build` and `setup.py sdist` to replace `_version.py` with a small static file that contains just the generated version data. ## Installation First, decide on values for the following configuration variables: * `VCS`: the version control system you use. Currently accepts "git". * `style`: the style of version string to be produced. See "Styles" below for details. Defaults to "pep440", which looks like `TAG[+DISTANCE.gSHORTHASH[.dirty]]`. * `versionfile_source`: A project-relative pathname into which the generated version strings should be written. This is usually a `_version.py` next to your project's main `__init__.py` file, so it can be imported at runtime. If your project uses `src/myproject/__init__.py`, this should be `src/myproject/_version.py`. This file should be checked in to your VCS as usual: the copy created below by `setup.py setup_versioneer` will include code that parses expanded VCS keywords in generated tarballs. The 'build' and 'sdist' commands will replace it with a copy that has just the calculated version string. This must be set even if your project does not have any modules (and will therefore never import `_version.py`), since "setup.py sdist" -based trees still need somewhere to record the pre-calculated version strings. Anywhere in the source tree should do. If there is a `__init__.py` next to your `_version.py`, the `setup.py setup_versioneer` command (described below) will append some `__version__`-setting assignments, if they aren't already present. * `versionfile_build`: Like `versionfile_source`, but relative to the build directory instead of the source directory. These will differ when your setup.py uses 'package_dir='. If you have `package_dir={'myproject': 'src/myproject'}`, then you will probably have `versionfile_build='myproject/_version.py'` and `versionfile_source='src/myproject/_version.py'`. If this is set to None, then `setup.py build` will not attempt to rewrite any `_version.py` in the built tree. If your project does not have any libraries (e.g. if it only builds a script), then you should use `versionfile_build = None`. To actually use the computed version string, your `setup.py` will need to override `distutils.command.build_scripts` with a subclass that explicitly inserts a copy of `versioneer.get_version()` into your script file. See `test/demoapp-script-only/setup.py` for an example. * `tag_prefix`: a string, like 'PROJECTNAME-', which appears at the start of all VCS tags. If your tags look like 'myproject-1.2.0', then you should use tag_prefix='myproject-'. If you use unprefixed tags like '1.2.0', this should be an empty string, using either `tag_prefix=` or `tag_prefix=''`. * `parentdir_prefix`: a optional string, frequently the same as tag_prefix, which appears at the start of all unpacked tarball filenames. If your tarball unpacks into 'myproject-1.2.0', this should be 'myproject-'. To disable this feature, just omit the field from your `setup.cfg`. This tool provides one script, named `versioneer`. That script has one mode, "install", which writes a copy of `versioneer.py` into the current directory and runs `versioneer.py setup` to finish the installation. To versioneer-enable your project: * 1: Modify your `setup.cfg`, adding a section named `[versioneer]` and populating it with the configuration values you decided earlier (note that the option names are not case-sensitive): ```` [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- ```` * 2: Run `versioneer install`. This will do the following: * copy `versioneer.py` into the top of your source tree * create `_version.py` in the right place (`versionfile_source`) * modify your `__init__.py` (if one exists next to `_version.py`) to define `__version__` (by calling a function from `_version.py`) * modify your `MANIFEST.in` to include both `versioneer.py` and the generated `_version.py` in sdist tarballs `versioneer install` will complain about any problems it finds with your `setup.py` or `setup.cfg`. Run it multiple times until you have fixed all the problems. * 3: add a `import versioneer` to your setup.py, and add the following arguments to the setup() call: version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), * 4: commit these changes to your VCS. To make sure you won't forget, `versioneer install` will mark everything it touched for addition using `git add`. Don't forget to add `setup.py` and `setup.cfg` too. ## Post-Installation Usage Once established, all uses of your tree from a VCS checkout should get the current version string. All generated tarballs should include an embedded version string (so users who unpack them will not need a VCS tool installed). If you distribute your project through PyPI, then the release process should boil down to two steps: * 1: git tag 1.0 * 2: python setup.py register sdist upload If you distribute it through github (i.e. users use github to generate tarballs with `git archive`), the process is: * 1: git tag 1.0 * 2: git push; git push --tags Versioneer will report "0+untagged.NUMCOMMITS.gHASH" until your tree has at least one tag in its history. ## Version-String Flavors Code which uses Versioneer can learn about its version string at runtime by importing `_version` from your main `__init__.py` file and running the `get_versions()` function. From the "outside" (e.g. in `setup.py`), you can import the top-level `versioneer.py` and run `get_versions()`. Both functions return a dictionary with different flavors of version information: * `['version']`: A condensed version string, rendered using the selected style. This is the most commonly used value for the project's version string. The default "pep440" style yields strings like `0.11`, `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section below for alternative styles. * `['full-revisionid']`: detailed revision identifier. For Git, this is the full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". * `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that this is only accurate if run in a VCS checkout, otherwise it is likely to be False or None * `['error']`: if the version string could not be computed, this will be set to a string describing the problem, otherwise it will be None. It may be useful to throw an exception in setup.py if this is set, to avoid e.g. creating tarballs with a version string of "unknown". Some variants are more useful than others. Including `full-revisionid` in a bug report should allow developers to reconstruct the exact code being tested (or indicate the presence of local changes that should be shared with the developers). `version` is suitable for display in an "about" box or a CLI `--version` output: it can be easily compared against release notes and lists of bugs fixed in various releases. The installer adds the following text to your `__init__.py` to place a basic version in `YOURPROJECT.__version__`: from ._version import get_versions __version__ = get_versions()['version'] del get_versions ## Styles The setup.cfg `style=` configuration controls how the VCS information is rendered into a version string. The default style, "pep440", produces a PEP440-compliant string, equal to the un-prefixed tag name for actual releases, and containing an additional "local version" section with more detail for in-between builds. For Git, this is TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags --dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and that this commit is two revisions ("+2") beyond the "0.11" tag. For released software (exactly equal to a known tag), the identifier will only contain the stripped tag, e.g. "0.11". Other styles are available. See details.md in the Versioneer source tree for descriptions. ## Debugging Versioneer tries to avoid fatal errors: if something goes wrong, it will tend to return a version of "0+unknown". To investigate the problem, run `setup.py version`, which will run the version-lookup code in a verbose mode, and will display the full contents of `get_versions()` (including the `error` string, which may help identify what went wrong). ## Updating Versioneer To upgrade your project to a new release of Versioneer, do the following: * install the new Versioneer (`pip install -U versioneer` or equivalent) * edit `setup.cfg`, if necessary, to include any new configuration settings indicated by the release notes * re-run `versioneer install` in your source tree, to replace `SRC/_version.py` * commit any changed files ### Upgrading to 0.16 Nothing special. ### Upgrading to 0.15 Starting with this version, Versioneer is configured with a `[versioneer]` section in your `setup.cfg` file. Earlier versions required the `setup.py` to set attributes on the `versioneer` module immediately after import. The new version will refuse to run (raising an exception during import) until you have provided the necessary `setup.cfg` section. In addition, the Versioneer package provides an executable named `versioneer`, and the installation process is driven by running `versioneer install`. In 0.14 and earlier, the executable was named `versioneer-installer` and was run without an argument. ### Upgrading to 0.14 0.14 changes the format of the version string. 0.13 and earlier used hyphen-separated strings like "0.11-2-g1076c97-dirty". 0.14 and beyond use a plus-separated "local version" section strings, with dot-separated components, like "0.11+2.g1076c97". PEP440-strict tools did not like the old format, but should be ok with the new one. ### Upgrading from 0.11 to 0.12 Nothing special. ### Upgrading from 0.10 to 0.11 You must add a `versioneer.VCS = "git"` to your `setup.py` before re-running `setup.py setup_versioneer`. This will enable the use of additional version-control systems (SVN, etc) in the future. ## Future Directions This tool is designed to make it easily extended to other version-control systems: all VCS-specific components are in separate directories like src/git/ . The top-level `versioneer.py` script is assembled from these components by running make-versioneer.py . In the future, make-versioneer.py will take a VCS name as an argument, and will construct a version of `versioneer.py` that is specific to the given VCS. It might also take the configuration arguments that are currently provided manually during installation by editing setup.py . Alternatively, it might go the other direction and include code from all supported VCS systems, reducing the number of intermediate scripts. ## License To make Versioneer easier to embed, all its code is dedicated to the public domain. The `_version.py` that it creates is also in the public domain. Specifically, both are released under the Creative Commons "Public Domain Dedication" license (CC0-1.0), as described in https://creativecommons.org/publicdomain/zero/1.0/ . """ from __future__ import print_function try: import configparser except ImportError: import ConfigParser as configparser import errno import json import os import re import subprocess import sys class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_root(): """Get the project root directory. We require that all commands are run from the project root, i.e. the directory that contains setup.py, setup.cfg, and versioneer.py . """ root = os.path.realpath(os.path.abspath(os.getcwd())) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): # allow 'python path/to/setup.py COMMAND' root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): err = ("Versioneer was unable to run the project root directory. " "Versioneer requires setup.py to be executed from " "its immediate directory (like 'python setup.py COMMAND'), " "or in a way that lets it use sys.argv[0] to find the root " "(like 'python path/to/setup.py COMMAND').") raise VersioneerBadRootError(err) try: # Certain runtime workflows (setup.py install/develop in a setuptools # tree) execute all dependencies in a single python process, so # "versioneer" may be imported multiple times, and python's shared # module-import table will cache the first one. So we can't use # os.path.dirname(__file__), as that will find whichever # versioneer.py was first imported, even in later projects. me = os.path.realpath(os.path.abspath(__file__)) if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]: print("Warning: build in %s is using versioneer.py from %s" % (os.path.dirname(me), versioneer_py)) except NameError: pass return root def get_config_from_root(root): """Read the project setup.cfg file to determine Versioneer config.""" # This might raise EnvironmentError (if setup.cfg is missing), or # configparser.NoSectionError (if it lacks a [versioneer] section), or # configparser.NoOptionError (if it lacks "VCS="). See the docstring at # the top of versioneer.py for instructions on writing your setup.cfg . setup_cfg = os.path.join(root, "setup.cfg") parser = configparser.SafeConfigParser() with open(setup_cfg, "r") as f: parser.readfp(f) VCS = parser.get("versioneer", "VCS") # mandatory def get(parser, name): if parser.has_option("versioneer", name): return parser.get("versioneer", name) return None cfg = VersioneerConfig() cfg.VCS = VCS cfg.style = get(parser, "style") or "" cfg.versionfile_source = get(parser, "versionfile_source") cfg.versionfile_build = get(parser, "versionfile_build") cfg.tag_prefix = get(parser, "tag_prefix") if cfg.tag_prefix in ("''", '""'): cfg.tag_prefix = "" cfg.parentdir_prefix = get(parser, "parentdir_prefix") cfg.verbose = get(parser, "verbose") return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" # these dictionaries contain VCS-specific tools LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) return None return stdout LONG_VERSION_PY['git'] = ''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.16 (https://github.com/warner/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" keywords = {"refnames": git_refnames, "full": git_full} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "%(STYLE)s" cfg.tag_prefix = "%(TAG_PREFIX)s" cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %%s" %% dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %%s" %% (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %%s (error)" %% dispcmd) return None return stdout def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%%s', but '%%s' doesn't start with " "prefix '%%s'" %% (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %%d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%%s', no digits" %% ",".join(refs-tags)) if verbose: print("likely tags: %%s" %% ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %%s" %% r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags"} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %%s" %% root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%%s*" %% tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%%s'" %% describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%%s' doesn't start with prefix '%%s'" print(fmt %% (full_tag, tag_prefix)) pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" %% (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%%d" %% pieces["distance"] else: # exception #1 rendered = "0.post.dev%%d" %% pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%%s" %% pieces["short"] else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%%s" %% pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%%s'" %% style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for i in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree"} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} ''' @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs-tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags"} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %s" % root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s*" % tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces def do_vcs_install(manifest_in, versionfile_source, ipy): """Git-specific installation logic for Versioneer. For Git, this means creating/changing .gitattributes to mark _version.py for export-time keyword substitution. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] files = [manifest_in, versionfile_source] if ipy: files.append(ipy) try: me = __file__ if me.endswith(".pyc") or me.endswith(".pyo"): me = os.path.splitext(me)[0] + ".py" versioneer_file = os.path.relpath(me) except NameError: versioneer_file = "versioneer.py" files.append(versioneer_file) present = False try: f = open(".gitattributes", "r") for line in f.readlines(): if line.strip().startswith(versionfile_source): if "export-subst" in line.strip().split()[1:]: present = True f.close() except EnvironmentError: pass if not present: f = open(".gitattributes", "a+") f.write("%s export-subst\n" % versionfile_source) f.close() files.append(".gitattributes") run_command(GITS, ["add", "--"] + files) def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%s', but '%s' doesn't start with " "prefix '%s'" % (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} SHORT_VERSION_PY = """ # This file was generated by 'versioneer.py' (0.16) from # revision-control system data, or from the parent directory name of an # unpacked source archive. Distribution tarballs contain a pre-generated copy # of this file. import json import sys version_json = ''' %s ''' # END VERSION_JSON def get_versions(): return json.loads(version_json) """ def versions_from_file(filename): """Try to determine the version from _version.py if present.""" try: with open(filename) as f: contents = f.read() except EnvironmentError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1)) def write_to_version_file(filename, versions): """Write the given version number to the given _version.py file.""" os.unlink(filename) contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": ")) with open(filename, "w") as f: f.write(SHORT_VERSION_PY % contents) print("set %s to '%s'" % (filename, versions["version"])) def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%d" % pieces["distance"] else: # exception #1 rendered = "0.post.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} class VersioneerBadRootError(Exception): """The project root directory is unknown or missing key files.""" def get_versions(verbose=False): """Get the project version from whatever source is available. Returns dict with two keys: 'version' and 'full'. """ if "versioneer" in sys.modules: # see the discussion in cmdclass.py:get_cmdclass() del sys.modules["versioneer"] root = get_root() cfg = get_config_from_root(root) assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" handlers = HANDLERS.get(cfg.VCS) assert handlers, "unrecognized VCS '%s'" % cfg.VCS verbose = verbose or cfg.verbose assert cfg.versionfile_source is not None, \ "please set versioneer.versionfile_source" assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" versionfile_abs = os.path.join(root, cfg.versionfile_source) # extract version from first of: _version.py, VCS command (e.g. 'git # describe'), parentdir. This is meant to work for developers using a # source checkout, for users of a tarball created by 'setup.py sdist', # and for users of a tarball/zipball created by 'git archive' or github's # download-from-tag feature or the equivalent in other VCSes. get_keywords_f = handlers.get("get_keywords") from_keywords_f = handlers.get("keywords") if get_keywords_f and from_keywords_f: try: keywords = get_keywords_f(versionfile_abs) ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) if verbose: print("got version from expanded keyword %s" % ver) return ver except NotThisMethod: pass try: ver = versions_from_file(versionfile_abs) if verbose: print("got version from file %s %s" % (versionfile_abs, ver)) return ver except NotThisMethod: pass from_vcs_f = handlers.get("pieces_from_vcs") if from_vcs_f: try: pieces = from_vcs_f(cfg.tag_prefix, root, verbose) ver = render(pieces, cfg.style) if verbose: print("got version from VCS %s" % ver) return ver except NotThisMethod: pass try: if cfg.parentdir_prefix: ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) if verbose: print("got version from parentdir %s" % ver) return ver except NotThisMethod: pass if verbose: print("unable to compute version") return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} def get_version(): """Get the short version string for this project.""" return get_versions()["version"] def get_cmdclass(): """Get the custom setuptools/distutils subclasses used by Versioneer.""" if "versioneer" in sys.modules: del sys.modules["versioneer"] # this fixes the "python setup.py develop" case (also 'install' and # 'easy_install .'), in which subdependencies of the main project are # built (using setup.py bdist_egg) in the same python process. Assume # a main project A and a dependency B, which use different versions # of Versioneer. A's setup.py imports A's Versioneer, leaving it in # sys.modules by the time B's setup.py is executed, causing B to run # with the wrong versioneer. Setuptools wraps the sub-dep builds in a # sandbox that restores sys.modules to it's pre-build state, so the # parent is protected against the child's "import versioneer". By # removing ourselves from sys.modules here, before the child build # happens, we protect the child from the parent's versioneer too. # Also see https://github.com/warner/python-versioneer/issues/52 cmds = {} # we add "version" to both distutils and setuptools from distutils.core import Command class cmd_version(Command): description = "report generated version string" user_options = [] boolean_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): vers = get_versions(verbose=True) print("Version: %s" % vers["version"]) print(" full-revisionid: %s" % vers.get("full-revisionid")) print(" dirty: %s" % vers.get("dirty")) if vers["error"]: print(" error: %s" % vers["error"]) cmds["version"] = cmd_version # we override "build_py" in both distutils and setuptools # # most invocation pathways end up running build_py: # distutils/build -> build_py # distutils/install -> distutils/build ->.. # setuptools/bdist_wheel -> distutils/install ->.. # setuptools/bdist_egg -> distutils/install_lib -> build_py # setuptools/install -> bdist_egg ->.. # setuptools/develop -> ? # we override different "build_py" commands for both environments if "setuptools" in sys.modules: from setuptools.command.build_py import build_py as _build_py else: from distutils.command.build_py import build_py as _build_py class cmd_build_py(_build_py): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_py.run(self) # now locate _version.py in the new build/ directory and replace # it with an updated value if cfg.versionfile_build: target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_py"] = cmd_build_py if "cx_Freeze" in sys.modules: # cx_freeze enabled? from cx_Freeze.dist import build_exe as _build_exe class cmd_build_exe(_build_exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _build_exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["build_exe"] = cmd_build_exe del cmds["build_py"] # we override different "sdist" commands for both environments if "setuptools" in sys.modules: from setuptools.command.sdist import sdist as _sdist else: from distutils.command.sdist import sdist as _sdist class cmd_sdist(_sdist): def run(self): versions = get_versions() self._versioneer_generated_versions = versions # unless we update this, the command will keep using the old # version self.distribution.metadata.version = versions["version"] return _sdist.run(self) def make_release_tree(self, base_dir, files): root = get_root() cfg = get_config_from_root(root) _sdist.make_release_tree(self, base_dir, files) # now locate _version.py in the new base_dir directory # (remembering that it may be a hardlink) and replace it with an # updated value target_versionfile = os.path.join(base_dir, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, self._versioneer_generated_versions) cmds["sdist"] = cmd_sdist return cmds CONFIG_ERROR = """ setup.cfg is missing the necessary Versioneer configuration. You need a section like: [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- You will also need to edit your setup.py to use the results: import versioneer setup(version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ...) Please read the docstring in ./versioneer.py for configuration instructions, edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. """ SAMPLE_CONFIG = """ # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] #VCS = git #style = pep440 #versionfile_source = #versionfile_build = #tag_prefix = #parentdir_prefix = """ INIT_PY_SNIPPET = """ from ._version import get_versions __version__ = get_versions()['version'] del get_versions """ def do_setup(): """Main VCS-independent setup function for installing Versioneer.""" root = get_root() try: cfg = get_config_from_root(root) except (EnvironmentError, configparser.NoSectionError, configparser.NoOptionError) as e: if isinstance(e, (EnvironmentError, configparser.NoSectionError)): print("Adding sample versioneer config to setup.cfg", file=sys.stderr) with open(os.path.join(root, "setup.cfg"), "a") as f: f.write(SAMPLE_CONFIG) print(CONFIG_ERROR, file=sys.stderr) return 1 print(" creating %s" % cfg.versionfile_source) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") if os.path.exists(ipy): try: with open(ipy, "r") as f: old = f.read() except EnvironmentError: old = "" if INIT_PY_SNIPPET not in old: print(" appending to %s" % ipy) with open(ipy, "a") as f: f.write(INIT_PY_SNIPPET) else: print(" %s unmodified" % ipy) else: print(" %s doesn't exist, ok" % ipy) ipy = None # Make sure both the top-level "versioneer.py" and versionfile_source # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so # they'll be copied into source distributions. Pip won't be able to # install the package without this. manifest_in = os.path.join(root, "MANIFEST.in") simple_includes = set() try: with open(manifest_in, "r") as f: for line in f: if line.startswith("include "): for include in line.split()[1:]: simple_includes.add(include) except EnvironmentError: pass # That doesn't cover everything MANIFEST.in can do # (http://docs.python.org/2/distutils/sourcedist.html#commands), so # it might give some false negatives. Appending redundant 'include' # lines is safe, though. if "versioneer.py" not in simple_includes: print(" appending 'versioneer.py' to MANIFEST.in") with open(manifest_in, "a") as f: f.write("include versioneer.py\n") else: print(" 'versioneer.py' already in MANIFEST.in") if cfg.versionfile_source not in simple_includes: print(" appending versionfile_source ('%s') to MANIFEST.in" % cfg.versionfile_source) with open(manifest_in, "a") as f: f.write("include %s\n" % cfg.versionfile_source) else: print(" versionfile_source already in MANIFEST.in") # Make VCS-specific changes. For git, this means creating/changing # .gitattributes to mark _version.py for export-time keyword # substitution. do_vcs_install(manifest_in, cfg.versionfile_source, ipy) return 0 def scan_setup_py(): """Validate the contents of setup.py against Versioneer's expectations.""" found = set() setters = False errors = 0 with open("setup.py", "r") as f: for line in f.readlines(): if "import versioneer" in line: found.add("import") if "versioneer.get_cmdclass()" in line: found.add("cmdclass") if "versioneer.get_version()" in line: found.add("get_version") if "versioneer.VCS" in line: setters = True if "versioneer.versionfile_source" in line: setters = True if len(found) != 3: print("") print("Your setup.py appears to be missing some important items") print("(but I might be wrong). Please make sure it has something") print("roughly like the following:") print("") print(" import versioneer") print(" setup( version=versioneer.get_version(),") print(" cmdclass=versioneer.get_cmdclass(), ...)") print("") errors += 1 if setters: print("You should remove lines like 'versioneer.VCS = ' and") print("'versioneer.versionfile_source = ' . This configuration") print("now lives in setup.cfg, and should be removed from setup.py") print("") errors += 1 return errors if __name__ == "__main__": cmd = sys.argv[1] if cmd == "setup": errors = do_setup() errors += scan_setup_py() if errors: sys.exit(1)