pyogrio - Vectorized spatial vector file format I/O using GDAL/OGR
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
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.
Pyogrio is still at an early version and the API is subject to substantial change.
- Concepts and Terminology
- Supported vector formats
- Introduction to Pyogrio
- Display GDAL version
- List available drivers
- List available layers
- Read basic information about a data layer
- Read a data layer into a GeoPandas GeoDataFrame
- Read a subset of columns
- Read a subset of features
- Filter records by attribute value
- Filter records by spatial extent
- Execute a sql query
- Force geometries to be read as 2D geometries
- Read without geometry into a Pandas DataFrame
- Read feature bounds
- Write a GeoPandas GeoDataFrame
- Reading from compressed files / archives
- Reading from remote filesystems
- Configuration options
- API reference
- Error handling
- Limitations and Known Issues