---
id: rioxarray
version: "0.23.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# rioxarray — geospatial xarray extension powered by rasterio

License: permissive · Maintenance: active · Downloads: 1.1M/mo

## What it is and what it does

rioxarray is an xarray extension that adds geospatial and raster-specific operations to xarray DataArrays and Datasets. It bridges xarray's labeled array interface with rasterio's raster I/O and geospatial capabilities, letting you work with georeferenced gridded data—satellite imagery, climate models, digital elevation models—using xarray's familiar syntax and operations.

The package handles common geospatial tasks like reading and writing raster files, reprojecting between coordinate reference systems, clipping to geographic bounds, and managing spatial metadata. It depends on rasterio for raster I/O, xarray for the array interface, pyproj for coordinate transformations, and numpy for underlying computation. It's actively maintained and supports Python 3.12 and later.

Use it for:

- Load satellite or aerial imagery into xarray and perform analysis using labeled dimensions and coordinate-aware slicing
- Reproject raster data between different coordinate reference systems while preserving spatial metadata
- Read and write raster formats with automatic coordinate and CRS metadata handling
- Clip raster datasets to geographic regions using coordinate-aware operations
- Combine raster analysis with xarray's groupby, resample, and aggregation operations for time-series or multi-band data

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

rioxarray extends xarray with geospatial and raster operations powered by rasterio, enabling you to work with georeferenced gridded data using familiar xarray syntax.

Yes. rioxarray is actively maintained, has no known vulnerabilities, installs with low friction, and fills a genuine gap for developers working with geospatial gridded data in Python. If you use xarray and need raster operations or geospatial metadata handling, it's the standard choice. The Apache-2.0 license is permissive for all use cases.

## Install

pip install rioxarray
uv add rioxarray
poetry add rioxarray

## Installing rioxarray

Before you install: Low friction install with a stable dependency chain. The package is actively maintained (last commit 2026-07-27, 18 days since release) and supports current Python versions (3.12, 3.13, 3.14). Five runtime dependencies are all well-established geospatial and scientific libraries.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions. The package also includes Apache-licensed code adopted from other projects, with those licenses bundled in the repository.

Quickstart:

pip install rioxarray
import rioxarray
import xarray as xr
data = rioxarray.open_rasterio('file.tif')

Requires rasterio, which depends on system-level GDAL/GEOS libraries that may need separate installation depending on your platform.

Verify before relying:

- Whether GDAL/GEOS system dependencies are pre-installed or require separate setup on your platform
- Performance characteristics when working with very large raster datasets or complex reprojection operations

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

xarray raster geospatial, rasterio xarray extension, georeferenced gridded data, geospatial data analysis python, raster reprojection xarray, coordinate reference system xarray, raster io with xarray, geospatial, raster-data, xarray-extension

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