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+Metadata-Version: 2.1
+Name: pyogrio
+Version: 0.7.2
+Summary: Vectorized spatial vector file format I/O using GDAL/OGR
+Home-page: https://github.com/geopandas/pyogrio
+Author: Brendan C. Ward
+Author-email: bcward@astutespruce.com
+License: MIT
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: certifi
+Requires-Dist: numpy
+Requires-Dist: packaging
+Provides-Extra: benchmark
+Requires-Dist: pytest-benchmark ; extra == 'benchmark'
+Provides-Extra: dev
+Requires-Dist: Cython ; extra == 'dev'
+Provides-Extra: geopandas
+Requires-Dist: geopandas ; extra == 'geopandas'
+Provides-Extra: test
+Requires-Dist: pytest ; extra == 'test'
+Requires-Dist: pytest-cov ; extra == 'test'
+
+# pyogrio - Vectorized spatial vector file format I/O using GDAL/OGR
+
+Pyogrio provides a
+[GeoPandas](https://github.com/geopandas/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](https://pyogrio.readthedocs.io/en/latest/).
+
+WARNING: Pyogrio is still at an early version and the API is subject to
+substantial change. Please see [CHANGES](CHANGES.md).
+
+## Requirements
+
+Supports Python 3.8 - 3.11 and GDAL 3.4.x - 3.7.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 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](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 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)