# pyogrio > Vectorized spatial vector file format I/O using GDAL/OGR - **URL**: https://www.freshcrate.ai/projects/pyogrio - **Author**: pyogrio contributors - **Category**: Databases - **Latest version**: `0.12.1` (2026-04-21) - **License**: non-standard - **Source**: https://github.com/geopandas/pyogrio - **Homepage**: https://pypi.org/project/pyogrio/ - **Language**: Python - **GitHub**: 320 stars, 32 forks - **Registry**: pypi (`pyogrio`) - **Tags**: `pypi` ## Description [![pypi](https://img.shields.io/pypi/v/pyogrio.svg)](https://pypi.python.org/pypi/pyogrio/) [![Conda version](https://anaconda.org/conda-forge/pyogrio/badges/version.svg)](https://anaconda.org/conda-forge/pyogrio) [![Actions Status](https://github.com/geopandas/pyogrio/actions/workflows/tests-conda.yml/badge.svg?branch=main)](https://github.com/geopandas/pyogrio/actions?branch=main) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) # pyogrio - bulk-oriented spatial vector file I/O using GDAL/OGR Pyogrio provides fast, bulk-oriented read and write access to [GDAL/OGR](https://gdal.org/en/latest/drivers/vector/index.html) vector data sources, such as ESRI Shapefile, GeoPackage, GeoJSON, and several others. Vector data sources typically have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data. The typical use is to read or write these data sources to/from [GeoPandas](https://github.com/geopandas/geopandas) `GeoDataFrames`. Because the geometry column is optional, reading or writing only non-spatial data is also possible. Hence, GeoPackage attribute tables, DBF files, or CSV files are also supported. Pyogrio is fast because it uses pre-compiled bindings for GDAL/OGR to read and write the data records in bulk. This approach avoids multiple steps of converting to and from Python data types within Python, so performance becomes primarily limited by the underlying I/O speed of data source drivers in GDAL/OGR. We have seen \>5-10x speedups reading files and \>5-20x speedups writing files compared to using row-per-row approaches (e.g. Fiona). Read the documentation for more information: [https://pyogrio.readthedocs.io](https://pyogrio.readthedocs.io/en/latest/). ## Requirements Supports Python 3.10 - 3.14 and GDAL 3.6.x - 3.11.x. Reading to GeoDataFrames requires `geopandas>=0.12` with `shapely>=2`. Additionally, installing `pyarrow` in combination with GDAL 3.6+ enables a further speed-up when specifying `use_arrow=True`. ## Installation Pyogrio is currently available on [conda-forge](https://anaconda.org/conda-forge/pyogrio) and [PyPI](https://pypi.org/project/pyogrio/) for Linux, MacOS, and Windows. Please read the [installation documentation](https://pyogrio.readthedocs.io/en/latest/install.html) for more information. ## Supported vector formats Pyogrio supports most common vector data source formats (provided they are also supported by GDAL/OGR), including ESRI Shapefile, GeoPackage, GeoJSON, and FlatGeobuf. Please see the [list of supported formats](https://pyogrio.readthedocs.io/en/latest/supported_formats.html) for more information. ## Getting started Please read the [introduction](https://pyogrio.readthedocs.io/en/latest/supported_formats.html) for more information and examples to get started using Pyogrio. You can also check out the [API documentation](https://pyogrio.readthedocs.io/en/latest/api.html) for full details on using the API. ## Credits This project is made possible by the tremendous efforts of the GDAL, Fiona, and Geopandas communities. - Core I/O methods and supporting functions adapted from [Fiona](https://github.com/Toblerity/Fiona) - Inspired by [Fiona PR](https://github.com/Toblerity/Fiona/pull/540/files) ## Recent releases | Version | Date | Urgency | Changes | | --- | --- | --- | --- | | `0.12.1` | 2026-04-21 | Low | Imported from PyPI (0.12.1) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | | `v0.12.1` | 2025-11-28 | Low | ### Bug fixes - Fix regression in reading date columns (#616) - Fix regression in `read_dataframe` when `use_arrow=True` and `columns` is used to filter out columns of some specific types (#611) | ## Citation - HTML: https://www.freshcrate.ai/projects/pyogrio - Markdown: https://www.freshcrate.ai/projects/pyogrio.md - Dependencies JSON: https://www.freshcrate.ai/api/projects/pyogrio/deps _Generated by freshcrate.ai. 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