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Martin Fleischmann
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129 lines
6.3 KiB
Markdown
129 lines
6.3 KiB
Markdown
GeoPandas [](https://github.com/geopandas/geopandas/actions?query=workflow%3ATests) [](https://codecov.io/gh/geopandas/geopandas) [](https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [](https://mybinder.org/v2/gh/geopandas/geopandas/master) [](https://zenodo.org/badge/latestdoi/11002815)
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=========
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Python tools for geographic data
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Introduction
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------------
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GeoPandas is a project to add support for geographic data to
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[pandas](http://pandas.pydata.org) objects. It currently implements
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`GeoSeries` and `GeoDataFrame` types which are subclasses of
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`pandas.Series` and `pandas.DataFrame` respectively. GeoPandas
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objects can act on [shapely](http://shapely.readthedocs.io/en/latest/)
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geometry objects and perform geometric operations.
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GeoPandas geometry operations are cartesian. The coordinate reference
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system (crs) can be stored as an attribute on an object, and is
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automatically set when loading from a file. Objects may be
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transformed to new coordinate systems with the `to_crs()` method.
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There is currently no enforcement of like coordinates for operations,
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but that may change in the future.
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Documentation is available at [geopandas.org](http://geopandas.org)
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(current release) and
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[Read the Docs](http://geopandas.readthedocs.io/en/latest/)
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(release and development versions).
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Install
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--------
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See the [installation docs](https://geopandas.readthedocs.io/en/latest/install.html)
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for all details. GeoPandas depends on the following packages:
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- ``pandas``
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- ``shapely``
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- ``fiona``
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- ``pyproj``
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Further, ``descartes`` and ``matplotlib`` are optional dependencies, required
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for plotting, and [``rtree``](https://github.com/Toblerity/rtree) is an optional
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dependency, required for spatial joins. ``rtree`` requires the C library [``libspatialindex``](https://github.com/libspatialindex/libspatialindex).
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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.
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Get in touch
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------------
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- Ask usage questions ("How do I?") on [StackOverflow](https://stackoverflow.com/questions/tagged/geopandas) or [GIS StackExchange](https://gis.stackexchange.com/questions/tagged/geopandas).
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- Report bugs, suggest features or view the source code [on GitHub](https://github.com/geopandas/geopandas).
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- 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).
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- 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).
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Examples
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--------
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>>> import geopandas
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>>> from shapely.geometry import Polygon
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>>> p1 = Polygon([(0, 0), (1, 0), (1, 1)])
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>>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
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>>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)])
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>>> g = geopandas.GeoSeries([p1, p2, p3])
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>>> g
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0 POLYGON ((0 0, 1 0, 1 1, 0 0))
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1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0))
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2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0))
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dtype: geometry
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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`:
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>>> print(g.area)
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0 0.5
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1 1.0
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2 1.0
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dtype: float64
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Other operations return GeoPandas objects:
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>>> g.buffer(0.5)
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0 POLYGON ((-0.3535533905932737 0.35355339059327...
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1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ...
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2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04...
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dtype: geometry
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GeoPandas objects also know how to plot themselves. GeoPandas uses [descartes](https://pypi.python.org/pypi/descartes) to generate a [matplotlib](http://matplotlib.org) plot. To generate a plot of our GeoSeries, use:
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>>> g.plot()
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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):
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>>> nybb_path = geopandas.datasets.get_path('nybb')
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>>> boros = geopandas.read_file(nybb_path)
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>>> boros.set_index('BoroCode', inplace=True)
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>>> boros.sort_index(inplace=True)
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>>> boros
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BoroName Shape_Leng Shape_Area \
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BoroCode
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1 Manhattan 359299.096471 6.364715e+08
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2 Bronx 464392.991824 1.186925e+09
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3 Brooklyn 741080.523166 1.937479e+09
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4 Queens 896344.047763 3.045213e+09
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5 Staten Island 330470.010332 1.623820e+09
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geometry
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BoroCode
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1 MULTIPOLYGON (((981219.0557861328 188655.31579...
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2 MULTIPOLYGON (((1012821.805786133 229228.26458...
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3 MULTIPOLYGON (((1021176.479003906 151374.79699...
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4 MULTIPOLYGON (((1029606.076599121 156073.81420...
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5 MULTIPOLYGON (((970217.0223999023 145643.33221...
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>>> boros['geometry'].convex_hull
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BoroCode
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1 POLYGON ((977855.4451904297 188082.3223876953,...
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2 POLYGON ((1017949.977600098 225426.8845825195,...
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3 POLYGON ((988872.8212280273 146772.0317993164,...
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4 POLYGON ((1000721.531799316 136681.776184082, ...
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5 POLYGON ((915517.6877458114 120121.8812543372,...
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dtype: geometry
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