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=========

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, ``descartes`` and ``matplotlib`` are optional dependencies, 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](examples/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](examples/test_buffer.png)

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:

    >>> 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](examples/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](examples/nyc_hull.png)
