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).
NOTE: If this package has multiple flavors (see below), then use one of them instead of the name specified above. NOTE: This is a Python port. Instead of py312-pyogrio listed in the above command, you can pick from the names under the Packages section.
graphics/py-pyogrio: Add py-pyogrio 0.12.1
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).
