Getting started#

The relationship between Dask-GeoPandas and GeoPandas is the same as the relationship between dask.dataframe and pandas. We recommend checking the Dask documentation to better understand how DataFrames are scaled before diving into Dask-GeoPandas.

Dask-GeoPandas basics#

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)

By default, this repartitions the data naively by rows. However, you can also provide spatial partitioning to take advantage of the spatial structure of the GeoDataFrame.

ddf = ddf.spatial_shuffle()

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


Additionally, if you have a distributed dask.dataframe you can pass columns of x-y points to the set_geometry method.

import dask.dataframe as dd
import dask_geopandas

ddf = dd.read_csv('...')

ddf = ddf.set_geometry(
    dask_geopandas.points_from_xy(ddf, 'latitude', 'longitude')

Writing files (and reading back) is currently supported for the Parquet and Feather file formats.

ddf = dask_geopandas.read_parquet("path/to/dir/")

Traditional GIS file formats can be read into partitioned GeoDataFrame (requires pyogrio) but not written.

ddf = dask_geopandas.read_file("file.gpkg", npartitions=4)