rioxarray
geospatial xarray extension powered by rasterio
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires rasterio, which depends on system-level GDAL/GEOS libraries that may need separate installation depending on your platform.
- 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).
License · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-07-27 (18 days) · last repo commit 2026-07-27 · 621 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,141,789 downloads/mo, #4,306 on PyPI
Alternatives
Verify before relying
pip install rioxarray
import rioxarray
import xarray as xr
data = rioxarray.open_rasterio('file.tif')- 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
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 on this page. Verify before relying on it.
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
rioxarray on PyPI
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.
Requires rasterio, which depends on system-level GDAL/GEOS libraries that may need separate installation depending on your platform.
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')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 the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagespackagingrasterioxarraypyprojnumpy |
| Maintenance | Actively maintained 18 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,141,789 / month, #4,306 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: GISTopic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: rioxarray-0.23.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “xarray raster geospatial”
- rioxarrayrioxarray extends xarray with geospatial and raster operations…
- rasterixRasterix provides tools for analyzing raster data within Xarray…
- odc-loaderConstructs xarray objects from parsed geospatial metadata, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also rasterix · rasterio · xproj · rio-tiler · xarray-spatial · odc-loader · rio-cogeo · odc-geo · exactextract · xarray