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Python tools for geographic data

GeoPandas is a project to add support for geographic data topandas objects. It currently implementsGeoSeries andGeoDataFrame types which are subclasses ofpandas.Series andpandas.DataFrame respectively. GeoPandasobjects can act onshapelygeometry objects and perform geometric operations.

GeoPandas geometry operations are cartesian. The coordinate referencesystem (crs) can be stored as an attribute on an object, and isautomatically set when loading from a file. Objects may betransformed to new coordinate systems with theto_crs() method.There is currently no enforcement of like coordinates for operations,but that may change in the future.

Documentation is available atgeopandas.org(current release) andRead the Docs(release and development versions).

The GeoPandas project uses anopen governance modeland is fiscally sponsored byNumFOCUS. Consider makingatax-deductible donation to help the projectpay for developer time, professional services, travel, workshops, and a variety of other needs.

See theinstallation docsfor all details. GeoPandas depends on the following packages:

• pandas
• shapely
• pyogrio
• pyproj
• packaging

Further,matplotlib is an optional dependency, required for plotting.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 theconda package manager. See theinstallation docs for more details.

• Ask usage questions ("How do I?") onStackOverflow orGIS StackExchange.
• Get involved indiscussions on GitHub
• Report bugs, suggest features or view the source codeon GitHub.
• For a quick question about a bug report or feature request, or Pull Request, head over to thegitter channel.
• For less well defined questions or ideas, or to announce other projects of interest to GeoPandas users, ... use themailing list.

>>> 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])>>> g0         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

Some geographic operations return normal pandas objects. Thearea property of aGeoSeries will return apandas.Series containing the area of each item in theGeoSeries:

>>> print(g.area)0    0.51    1.02    1.0dtype: 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

GeoPandas objects also know how to plot themselves. GeoPandas usesmatplotlib for plotting. To generate a plot of aGeoSeries, use:

>>> g.plot()

GeoPandas also implements alternate constructors that can read any data format recognized bypyogrio. To read a zip file containing an ESRI shapefile with theboroughs boundaries of New York City (the example can be fetched using thegeodatasets package):

>>> import geodatasets>>> nybb_path = geodatasets.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  \BoroCode1             Manhattan  359299.096471  6.364715e+082                 Bronx  464392.991824  1.186925e+093              Brooklyn  741080.523166  1.937479e+094                Queens  896344.047763  3.045213e+095         Staten Island  330470.010332  1.623820e+09                                                   geometryBoroCode1         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...

>>> boros['geometry'].convex_hullBoroCode1    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