GeoPandas

Python tools for geographic data

Introduction

GeoPandas is a 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 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 (current release) and Read the Docs (release and development versions).

Install

Requirements

For the installation of GeoPandas, the following packages are required:

• pandas
• shapely
• fiona
• pyproj

Further, descartes and matplotlib are optional dependencies, required for plotting, and rtree is an optional dependency, required for spatial joins. rtree requires the C library libspatialindex. If using brew, you can install using brew install Spatialindex.

Install

GeoPandas depends on several low-level libraries for geospatial analysis. Depending on the system and package manager that you use, this may cause dependency conflicts if you are not careful.

Using conda

We suggest that you use the anaconda distribution to install GeoPandas (miniconda is fine as well).

Use conda and the conda-forge channel to install GeoPandas on a clean environment:

conda create -n geopandas
source activate geopandas  # 'activate geopandas' on Windows
conda install -c conda-forge geopandas

NOTE: Creating a new environment is not strictly necessary, but installing other geospatial packages from a different channel than conda-forge may cause dependency conflicts, so we recommend starting fresh if possible. See the conda-forge gotcha page for more information.

Using pip

GeoPandas is also pip-installable. If you choose to use pip, make sure that you have the proper non-python libraries installed and linked properly.

pip install geopandas

Examples

>>> 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 ((0.0000000000000000 0.000000000000000...
1    POLYGON ((0.0000000000000000 0.000000000000000...
2    POLYGON ((2.0000000000000000 0.000000000000000...
dtype: object

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)
Out[15]:
0    POLYGON ((-0.3535533905932737 0.35355339059327...
1    POLYGON ((-0.5000000000000000 0.00000000000000...
2    POLYGON ((1.5000000000000000 0.000000000000000...
dtype: object

GeoPandas objects also know how to plot themselves. GeoPandas uses descartes to generate a matplotlib 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. To read a zip file containing an ESRI shapefile with the boroughs boundaries of New York City (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()
>>> boros
               BoroName    Shape_Area     Shape_Leng  \
BoroCode
1             Manhattan  6.364422e+08  358532.956418
2                 Bronx  1.186804e+09  464517.890553
3              Brooklyn  1.959432e+09  726568.946340
4                Queens  3.049947e+09  861038.479299
5         Staten Island  1.623853e+09  330385.036974

                                                   geometry
BoroCode
1         (POLYGON ((981219.0557861328125000 188655.3157...
2         (POLYGON ((1012821.8057861328125000 229228.264...
3         (POLYGON ((1021176.4790039062500000 151374.796...
4         (POLYGON ((1029606.0765991210937500 156073.814...
5         (POLYGON ((970217.0223999023437500 145643.3322...

>>> boros['geometry'].convex_hull
0    POLYGON ((915517.6877458114176989 120121.88125...
1    POLYGON ((1000721.5317993164062500 136681.7761...
2    POLYGON ((988872.8212280273437500 146772.03179...
3    POLYGON ((977855.4451904296875000 188082.32238...
4    POLYGON ((1017949.9776000976562500 225426.8845...
dtype: object