---
id: pyogrio
version: "0.13.0"
license: MIT License Copyright (c) 2020-2024 Brendan C. Ward and pyogrio contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# pyogrio — Vectorized spatial vector file format I/O using GDAL/OGR

License: permissive · Maintenance: active · Downloads: 17.2M/mo

## 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 above — verify before relying.

Pyogrio provides fast, bulk-oriented read and write access to vector spatial data formats (Shapefile, GeoPackage, GeoJSON, etc.) via GDAL/OGR bindings, typically for use with GeoPandas GeoDataFrames.

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

pip install pyogrio
uv add pyogrio
poetry add pyogrio

## Installing pyogrio

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.

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')

Requires GDAL >= 3.6 installed on the system; Python >= 3.10 required. Reading to GeoDataFrames requires geopandas >= 0.12 and shapely >= 2.

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: MIT License Copyright (c) 2020-2024 Brendan C. Ward and pyogrio contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 17.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spatial vector file I/O, shapefile geopackage reader, GDAL OGR Python bindings, geospatial data bulk read write, GeoJSON GeoPackage import export, fast vector data access, geographic data format conversion, geospatial, gdal-ogr, geopandas-compatible

[View on SkillFed](https://skillfed.io/packages/pyogrio) · [View on PyPI](https://pypi.org/project/pyogrio/)