GeoPandas depends for its spatial functionality on a large geospatial, open
source stack of libraries (GEOS, GDAL, PROJ). See the
Dependencies section below for more details. Those base C
libraries can sometimes be a challenge to install. Therefore, we advise you
to closely follow the recommendations below to avoid installation problems.
To install GeoPandas and all its dependencies, we recommend to use the conda
package manager. This can be obtained by installing the
Anaconda Distribution (a free Python distribution for data science), or
through miniconda (minimal distribution only containing Python and the
conda package manager). See also the installation docs for more information
on how to install Anaconda or miniconda locally.
The advantage of using the conda package manager is that it provides
pre-built binaries for all the required and optional dependencies of GeoPandas
for all platforms (Windows, Mac, Linux).
To install the latest version of GeoPandas, you can then do:
conda install geopandas
conda-forge is a community effort that provides conda packages for a wide
range of software. It provides the conda-forge package channel for conda from
which packages can be installed, in addition to the “defaults” channel
provided by Anaconda.
Depending on what other packages you are working with, the defaults channel
or conda-forge channel may be better for your needs (e.g. some packages are
available on conda-forge and not on defaults).
GeoPandas and all its dependencies are available on the conda-forge
channel, and can be installed as:
conda install --channel conda-forge geopandas
We strongly recommend to either install everything from the defaults
channel, or everything from the conda-forge channel. Ending up with a
mixture of packages from both channels for the dependencies of GeoPandas
can lead to import problems.
See the conda-forge section on using multiple channels
for more details.
Creating a new environment is not strictly necessary, but given that installing
other geospatial packages from different channels may cause dependency conflicts
(as mentioned in the note above), it can be good practice to install the geospatial
stack in a clean environment starting fresh.
The following commands create a new environment with the name geo_env,
configures it to install packages always from conda-forge, and installs
GeoPandas in it:
conda create -n geo_env
conda activate geo_env
conda config --env --add channels conda-forge
conda config --env --set channel_priority strict
conda install python=3 geopandas
GeoPandas can also be installed with pip, if all dependencies can be installed
pip install geopandas
When using pip to install GeoPandas, you need to make sure that all dependencies are
fiona provides binary wheels with the dependencies included for Mac and Linux,
but not for Windows.
pyproj, rtree, and shapely provide binary wheels with dependencies included
for Mac, Linux, and Windows.
Windows wheels for shapely, fiona, pyproj and rtree
can be found at Christopher Gohlke’s website.
Depending on your platform, you might need to compile and install their
C dependencies manually. We refer to the individual packages for more
details on installing those.
Using conda (see above) avoids the need to compile the dependencies yourself.
You may install the latest development version by cloning the
GitHub repository and using pip to install from the local directory:
git clone https://github.com/geopandas/geopandas.git
pip install .
It is also possible to install the latest development version
directly from the GitHub repository with:
pip install git+git://github.com/geopandas/geopandas.git
For installing GeoPandas from source, the same note on
the need to have all dependencies correctly installed applies. But, those
dependencies can also be installed independently with conda before installing
GeoPandas from source:
conda install pandas fiona shapely pyproj rtree
See the section on conda above for more details on
getting running with Anaconda.
pandas (version 0.24 or later)
shapely (interface to GEOS)
fiona (interface to GDAL)
pyproj (interface to PROJ; version 2.2.0 or later)
Further, optional dependencies are:
rtree (optional; spatial index to improve performance and required for
overlay operations; interface to libspatialindex)
psycopg2 (optional; for PostGIS connection)
GeoAlchemy2 (optional; for writing to PostGIS)
geopy (optional; for geocoding)
For plotting, these additional packages may be used:
matplotlib (>= 2.2.0)
mapclassify (>= 2.2.0)
Work is ongoing to improve the performance of GeoPandas. Currently, the
fast implementations of basic spatial operations live in the PyGEOS
package (but work is under way to contribute those improvements to Shapely).
Starting with GeoPandas 0.8, it is possible to optionally use those
experimental speedups by installing PyGEOS. This can be done with conda
(using the conda-forge channel) or pip:
conda install pygeos --channel conda-forge
pip install pygeos
More specifically, whether the speedups are used or not is determined by:
If PyGEOS >= 0.8 is installed, it will be used by default (but installing
GeoPandas will not yet automatically install PyGEOS as dependency, you need
to do this manually).
You can still toggle the use of PyGEOS when it is available, by:
Setting an environment variable (USE_PYGEOS=0/1). Note this variable
is only checked at first import of GeoPandas.
Setting an option: geopandas.options.use_pygeos = True/False. Note,
although this variable can be set during an interactive session, it will
only work if the GeoDataFrames you use are created (e.g. reading a file
with read_file) after changing this value.
geopandas.options.use_pygeos = True/False
The use of PyGEOS is experimental! Although it is passing all tests,
there might still be issues and not all functions of GeoPandas will
already benefit from speedups (one known issue: the to_crs coordinate
transformations lose the z coordinate). But trying this out is very welcome!
Any issues you encounter (but also reports of successful usage are
interesting!) can be reported at https://gitter.im/geopandas/geopandas