| Name | pyogrio JSON |
| Version |
0.11.1
JSON |
| download |
| home_page | None |
| Summary | Vectorized spatial vector file format I/O using GDAL/OGR |
| upload_time | 2025-08-02 20:19:20 |
| maintainer | pyogrio contributors |
| docs_url | None |
| author | pyogrio contributors |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2020-2024 Brendan C. Ward and pyogrio contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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|
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# 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 3.9 - 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`.
## 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)
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"description": "# pyogrio - bulk-oriented spatial vector file I/O using GDAL/OGR\n\nPyogrio provides fast, bulk-oriented read and write access to \n[GDAL/OGR](https://gdal.org/en/latest/drivers/vector/index.html) vector data\nsources, such as ESRI Shapefile, GeoPackage, GeoJSON, and several others.\nVector data sources typically have geometries, such as points, lines, or\npolygons, and associated records with potentially many columns worth of data.\n\nThe typical use is to read or write these data sources to/from\n[GeoPandas](https://github.com/geopandas/geopandas) `GeoDataFrames`. Because\nthe geometry column is optional, reading or writing only non-spatial data is\nalso possible. Hence, GeoPackage attribute tables, DBF files, or CSV files are\nalso supported.\n\nPyogrio is fast because it uses pre-compiled bindings for GDAL/OGR to read and\nwrite the data records in bulk. This approach avoids multiple steps of\nconverting to and from Python data types within Python, so performance becomes\nprimarily limited by the underlying I/O speed of data source drivers in\nGDAL/OGR.\n\nWe have seen \\>5-10x speedups reading files and \\>5-20x speedups writing files\ncompared to using row-per-row approaches (e.g. Fiona).\n\nRead the documentation for more information:\n[https://pyogrio.readthedocs.io](https://pyogrio.readthedocs.io/en/latest/).\n\n## Requirements\n\nSupports Python 3.9 - 3.13 and GDAL 3.4.x - 3.9.x.\n\nReading to GeoDataFrames requires `geopandas>=0.12` with `shapely>=2`.\n\nAdditionally, installing `pyarrow` in combination with GDAL 3.6+ enables\na further speed-up when specifying `use_arrow=True`.\n\n## Installation\n\nPyogrio is currently available on\n[conda-forge](https://anaconda.org/conda-forge/pyogrio)\nand [PyPI](https://pypi.org/project/pyogrio/)\nfor Linux, MacOS, and Windows.\n\nPlease read the\n[installation documentation](https://pyogrio.readthedocs.io/en/latest/install.html)\nfor more information.\n\n## Supported vector formats\n\nPyogrio supports most common vector data source formats (provided they are also\nsupported by GDAL/OGR), including ESRI Shapefile, GeoPackage, GeoJSON, and\nFlatGeobuf.\n\nPlease see the [list of supported formats](https://pyogrio.readthedocs.io/en/latest/supported_formats.html)\nfor more information.\n\n## Getting started\n\nPlease read the [introduction](https://pyogrio.readthedocs.io/en/latest/supported_formats.html)\nfor more information and examples to get started using Pyogrio.\n\nYou can also check out the [API documentation](https://pyogrio.readthedocs.io/en/latest/api.html)\nfor full details on using the API.\n\n## Credits\n\nThis project is made possible by the tremendous efforts of the GDAL, Fiona, and\nGeopandas communities.\n\n- Core I/O methods and supporting functions adapted from [Fiona](https://github.com/Toblerity/Fiona)\n- Inspired by [Fiona PR](https://github.com/Toblerity/Fiona/pull/540/files)\n",
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