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118 lines
5.4 KiB
Markdown
118 lines
5.4 KiB
Markdown
GeoPandas [](https://travis-ci.org/geopandas/geopandas) [](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)
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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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Examples
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--------
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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 ((0.0000000000000000 0.000000000000000...
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1 POLYGON ((0.0000000000000000 0.000000000000000...
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2 POLYGON ((2.0000000000000000 0.000000000000000...
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dtype: object
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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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Out[15]:
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0 POLYGON ((-0.3535533905932737 0.35355339059327...
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1 POLYGON ((-0.5000000000000000 0.00000000000000...
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2 POLYGON ((1.5000000000000000 0.000000000000000...
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dtype: object
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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()
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>>> boros
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BoroName Shape_Area Shape_Leng \
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BoroCode
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1 Manhattan 6.364422e+08 358532.956418
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2 Bronx 1.186804e+09 464517.890553
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3 Brooklyn 1.959432e+09 726568.946340
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4 Queens 3.049947e+09 861038.479299
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5 Staten Island 1.623853e+09 330385.036974
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geometry
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BoroCode
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1 (POLYGON ((981219.0557861328125000 188655.3157...
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2 (POLYGON ((1012821.8057861328125000 229228.264...
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3 (POLYGON ((1021176.4790039062500000 151374.796...
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4 (POLYGON ((1029606.0765991210937500 156073.814...
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5 (POLYGON ((970217.0223999023437500 145643.3322...
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>>> boros['geometry'].convex_hull
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0 POLYGON ((915517.6877458114176989 120121.88125...
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1 POLYGON ((1000721.5317993164062500 136681.7761...
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2 POLYGON ((988872.8212280273437500 146772.03179...
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3 POLYGON ((977855.4451904296875000 188082.32238...
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4 POLYGON ((1017949.9776000976562500 225426.8845...
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dtype: object
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