Pyogrio provides a GeoPandas-oriented API to OGR vector data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data.
Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and from OGR vector data sources in order to give you faster interoperability. It uses pre-compiled bindings for GDAL/OGR so that the performance is primarily limited by the underlying I/O speed of data source drivers in GDAL/OGR rather than multiple steps of converting to and from Python data types within Python.
We have seen >5-10x speedups reading files and >5-20x speedups writing files compared to using non-vectorized approaches (Fiona and current I/O support in GeoPandas).
You can read these data sources into
GeoDataFrames, read just the non-geometry columns into Pandas DataFrames,
or even read non-spatial data sources that exist alongside vector data sources,
such as tables in a ESRI File Geodatabase, or antiquated DBF files.
Pyogrio also enables you to write GeoDataFrames to at least a few different
OGR vector data source formats.
Read the documentation for more information: https://pyogrio.readthedocs.io.
WARNING: Pyogrio is still at an early version and the API is subject to substantial change. Please see CHANGES.
Supports Python 3.8 - 3.11 and GDAL 3.4.x - 3.8.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 some of the 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 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.