pyogrio
Vectorized spatial vector file format I/O using GDAL/OGR
Decision gist · record as of 2026-08-14
Yes. Pyogrio is production-stable, actively maintained, and offers significant performance gains for vector spatial I/O. The MIT license is permissive. Medium install friction (compiled wheels, GDAL system dependency) is standard for geospatial tools. Install if you work with vector geographic data and need faster bulk read/write than row-by-row approaches.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires GDAL >= 3.6 installed on the system; Python >= 3.10 required.
- Reading to GeoDataFrames requires geopandas >= 0.12 and shapely >= 2.
- Medium install friction due to compiled wheels and GDAL dependency.
License · maintenance · safety
permissive license (permissive) — MIT License (permissive) allows unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and private projects.
last release 2026-06-26 (49 days) · last repo commit 2026-08-10 · 332 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 17,198,986 downloads/mo, #1,127 on PyPI
Alternatives
Verify before relying
pip install pyogrio
import pyogrio
# Read a shapefile
data = pyogrio.read_dataframe('file.shp')
# Write to GeoPackage
pyogrio.write_dataframe(data, 'output.gpkg')- Exact performance improvements (description claims >5-100x speedups reading, >5-20x writing) compared to row-per-row approaches—magnitude and test conditions not detailed.
- Whether pyarrow integration for use_arrow=True parameter is automatically available or requires separate installation and configuration.
What it is and what it does
Pyogrio is a Python interface to GDAL/OGR that reads and writes vector spatial data in bulk rather than row-by-row. It supports common formats like Shapefile, GeoPackage, GeoJSON, and FlatGeobuf, and integrates naturally with GeoPandas GeoDataFrames. The bulk approach avoids repeated Python type conversions, making I/O performance primarily limited by the underlying driver speed in GDAL/OGR itself.
The package is designed for workflows where you need to load or save geographic vector data efficiently—points, lines, polygons, and their associated attributes. It also handles non-spatial data (DBF, CSV attribute tables) when geometry is not needed. Installation requires GDAL >= 3.6 on your system and Python >= 3.10; pre-built wheels are available for Linux, macOS, and Windows.
Use it for
- Load a Shapefile or GeoPackage into a GeoPandas GeoDataFrame for spatial analysis and manipulation.
- Export processed geographic data from GeoPandas back to Shapefile, GeoPackage, or GeoJSON format.
- Bulk read or write large vector datasets where row-by-row I/O would be prohibitively slow.
- Read non-spatial tabular data from DBF or CSV files stored in GeoPackage or other GDAL-supported sources.
- Integrate GDAL/OGR vector I/O into Python geospatial pipelines without managing low-level C bindings.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Pyogrio is production-stable, actively maintained, and offers significant performance gains for vector spatial I/O. The MIT license is permissive. Medium install friction (compiled wheels, GDAL system dependency) is standard for geospatial tools. Install if you work with vector geographic data and need faster bulk read/write than row-by-row approaches.
Install
pyogrio on PyPI
Before you install
Medium install friction due to compiled wheels and GDAL dependency. Active maintenance (last commit 2026-08-10, release 49 days ago) with stable production status. Supports modern Python versions (3.10+) across Linux, macOS, and Windows with pre-built wheels.
Requires GDAL >= 3.6 installed on the system; Python >= 3.10 required. Reading to GeoDataFrames requires geopandas >= 0.12 and shapely >= 2.
License in practice
MIT License (permissive) allows unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and private projects.
Quickstart
pip install pyogrio
import pyogrio
# Read a shapefile
data = pyogrio.read_dataframe('file.shp')
# Write to GeoPackage
pyogrio.write_dataframe(data, 'output.gpkg')Verify before relying
- Exact performance improvements (description claims >5-100x speedups reading, >5-20x writing) compared to row-per-row approaches—magnitude and test conditions not detailed.
- Whether pyarrow integration for use_arrow=True parameter is automatically available or requires separate installation and configuration.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagescertifinumpypackaging |
| Maintenance | Actively maintained 49 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 17,198,986 / month, #1,127 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: Free Threading :: 2 - BetaTopic :: Scientific/Engineering :: GIS |
Evidence: pyogrio-0.13.0-cp310-cp310-macosx_12_0_arm64.whl; pyogrio-0.13.0-cp310-cp310-macosx_12_0_x86_64.whl; pyogrio-0.13.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; pyogrio-0.13.0-cp310-cp310-manylinux_2_28_aarch64.whl; pyogrio-0.13.0-cp310-cp310-manylinux_2_28_x86_64.whl; pyogrio-0.13.0-cp310-cp310-win_amd64.whl; pyogrio-0.13.0-cp311-abi3-macosx_12_0_arm64.whl; pyogrio-0.13.0-cp311-abi3-macosx_12_0_x86_64.whl; pyogrio-0.13.0-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; pyogrio-0.13.0-cp311-abi3-manylinux_2_28_aarch64.whl; pyogrio-0.13.0-cp311-abi3-manylinux_2_28_x86_64.whl; pyogrio-0.13.0-cp311-abi3-win_amd64.whl; pyogrio-0.13.0-cp314-cp314t-macosx_12_0_arm64.whl; pyogrio-0.13.0-cp314-cp314t-macosx_12_0_x86_64.whl; pyogrio-0.13.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; pyogrio-0.13.0-cp314-cp314t-manylinux_2_28_aarch64.whl; pyogrio-0.13.0-cp314-cp314t-manylinux_2_28_x86_64.whl; pyogrio-0.13.0-cp314-cp314t-win_amd64.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 › “spatial vector file I/O”
- pyogrioPyogrio provides fast, bulk-oriented read and write access to vector…
- GDALGDAL provides Python bindings to read, write, and manipulate…
- fionaFiona reads and writes vector GIS data (Shapefile, GeoPackage, etc.)…
Give your agent the search over MCP, or paste the wish link into any chat.
More GIS packages
Shapely provides Python tools for creating, manipulating, and analyzing 2D geometric objects (points, lines, polygons) using the GEOS library, with both scalar and vectorized NumPy-based operations.
pyproj provides a Python interface to PROJ, enabling cartographic projections and coordinate system transformations for geospatial applications.
GeoPandas extends pandas DataFrames to handle geographic data, combining pandas operations with shapely geometry and spatial analysis capabilities that would otherwise require a spatial database.
Install it if you work with geographic data in Python and want to avoid setting up a spatial database or learning a separate GIS tool.
geopy is a Python client for geocoding and distance calculation that converts addresses to coordinates and vice versa using multiple web-based geocoding services, and computes geodesic and great-circle distances between geographic points.
Install it if you need geocoding or distance calculations in your application.
h3 provides Python bindings to Uber's H3 geospatial indexing library, converting geographic coordinates into hierarchical hexagonal grid cells and performing spatial operations on them.
Fiona reads and writes vector GIS data (Shapefile, GeoPackage, etc.) via a Python API and command-line interface, wrapping GDAL with a focus on readable, productive code.
See also fudgeo · GDAL · pyshp · overturemaps · odc-geo · pygeoif · dask-geopandas · arcgis2geojson