extractrs

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Fast exact zonal statistics for xarray — backend powered by Rust.

Install

pip install extractrs
pip install extractrs[rio]  # adds automatic CRS reprojection via rioxarray

Quick start

import xarray as xr
import geopandas as gpd
import extractrs

ds = xr.open_dataset("temperature.nc")
basins = gpd.read_file("basins.shp")

result = ds.extrs.zonal_stats(basins, stat="mean", id_col="COMID")
# result is an xarray Dataset with dims (time, COMID)

result.to_zarr("output.zarr")

API

ds.extrs.zonal_stats(...)

ds.extrs.zonal_stats(
    gdf,                # GeoDataFrame of polygons
    stat="mean",        # statistic to compute
    id_col=None,        # column to use as zone IDs (default: integer index)
    var=None,           # single variable name to process
    vars=None,          # list of variable names to process
)

Returns an xarray.Dataset with spatial dimensions replaced by the zone ID dimension. When neither var nor vars is specified, all data variables are processed. The same interface is available on DataArrays via da.extrs.zonal_stats(gdf, stat, id_col).

Supported statistics

mean | sum | count | min | max | variance | stdev

mean, sum, variance, and stdev are coverage-weighted.

Low-level API

See extractrs.build_cache() and extractrs.apply_stat() for direct cache control when processing many timesteps against the same geometries.