Pyogrio provides fast, bulk-oriented read and write access to GDAL/OGR 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 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.
Supports Python 3.9 - 3.13 and GDAL 3.4.x - 3.9.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.
Pyogrio is currently available on conda-forge and PyPI for Linux, MacOS, and Windows.
Please read the installation documentation for more information.
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 for more information.
Please read the introduction for more information and examples to get started using Pyogrio.
You can also check out the API documentation for full details on using the API.
This project is made possible by the tremendous efforts of the GDAL, Fiona, and Geopandas communities.