Parallel GeoPandas with Dask

Dask-GeoPandas is a project merging the geospatial capabilities of GeoPandas and scalability of Dask. GeoPandas is an open source project designed to make working with geospatial data in Python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. Dask provides advanced parallelism and distributed out-of-core computation with a dask.dataframe module designed to scale pandas. Since GeoPandas is an extension to the pandas DataFrame, the same way Dask scales pandas can also be applied to GeoPandas.

This project is a bridge between Dask and GeoPandas and offers geospatial capabilities of GeoPandas backed by Dask.

See the documentation on https://dask-geopandas.readthedocs.io/en/latest/

This package depends on GeoPandas, Dask and PyGEOS.

One way to install all required dependencies is to use the conda package manager to create a new environment:

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 dask-geopandas

Given a GeoPandas dataframe

import geopandas
df = geopandas.read_file('...')

We can repartition it into a Dask-GeoPandas dataframe:

import dask_geopandas
ddf = dask_geopandas.from_geopandas(df, npartitions=4)

The familiar spatial attributes and methods of GeoPandas are also available and will be computed in parallel:

ddf.geometry.area.compute()
ddf.within(polygon)