101 lines
3.7 KiB
Plaintext
101 lines
3.7 KiB
Plaintext
Metadata-Version: 2.1
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Name: pyogrio
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Version: 0.7.2
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Summary: Vectorized spatial vector file format I/O using GDAL/OGR
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Home-page: https://github.com/geopandas/pyogrio
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Author: Brendan C. Ward
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Author-email: bcward@astutespruce.com
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License: MIT
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Requires-Python: >=3.8
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Requires-Dist: certifi
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Requires-Dist: numpy
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Requires-Dist: packaging
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Provides-Extra: benchmark
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Requires-Dist: pytest-benchmark ; extra == 'benchmark'
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Provides-Extra: dev
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Requires-Dist: Cython ; extra == 'dev'
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Provides-Extra: geopandas
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Requires-Dist: geopandas ; extra == 'geopandas'
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Provides-Extra: test
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Requires-Dist: pytest ; extra == 'test'
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Requires-Dist: pytest-cov ; extra == 'test'
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# pyogrio - Vectorized spatial vector file format I/O using GDAL/OGR
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Pyogrio provides a
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[GeoPandas](https://github.com/geopandas/geopandas)-oriented API to OGR vector
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data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources
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have geometries, such as points, lines, or polygons, and associated records
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with potentially many columns worth of data.
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Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and
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from OGR vector data sources in order to give you faster interoperability. It
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uses pre-compiled bindings for GDAL/OGR so that the performance is primarily
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limited by the underlying I/O speed of data source drivers in GDAL/OGR rather
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than multiple steps of converting to and from Python data types within Python.
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We have seen \>5-10x speedups reading files and \>5-20x speedups writing files
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compared to using non-vectorized approaches (Fiona and current I/O support in
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GeoPandas).
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You can read these data sources into
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`GeoDataFrames`, read just the non-geometry columns into Pandas `DataFrames`,
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or even read non-spatial data sources that exist alongside vector data sources,
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such as tables in a ESRI File Geodatabase, or antiquated DBF files.
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Pyogrio also enables you to write `GeoDataFrames` to at least a few different
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OGR vector data source formats.
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Read the documentation for more information:
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[https://pyogrio.readthedocs.io](https://pyogrio.readthedocs.io/en/latest/).
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WARNING: Pyogrio is still at an early version and the API is subject to
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substantial change. Please see [CHANGES](CHANGES.md).
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## Requirements
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Supports Python 3.8 - 3.11 and GDAL 3.4.x - 3.7.x.
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Reading to GeoDataFrames requires `geopandas>=0.12` with `shapely>=2`.
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Additionally, installing `pyarrow` in combination with GDAL 3.6+ enables
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a further speed-up when specifying `use_arrow=True`.
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## Installation
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Pyogrio is currently available on
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[conda-forge](https://anaconda.org/conda-forge/pyogrio)
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and [PyPI](https://pypi.org/project/pyogrio/)
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for Linux, MacOS, and Windows.
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Please read the
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[installation documentation](https://pyogrio.readthedocs.io/en/latest/install.html)
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for more information.
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## Supported vector formats
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Pyogrio supports some of the most common vector data source formats (provided
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they are also supported by GDAL/OGR), including ESRI Shapefile, GeoPackage,
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GeoJSON, and FlatGeobuf.
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Please see the [list of supported formats](https://pyogrio.readthedocs.io/en/latest/supported_formats.html)
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for more information.
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## Getting started
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Please read the [introduction](https://pyogrio.readthedocs.io/en/latest/supported_formats.html)
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for more information and examples to get started using Pyogrio.
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You can also check out the the [API documentation](https://pyogrio.readthedocs.io/en/latest/api.html)
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for full details on using the API.
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## Credits
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This project is made possible by the tremendous efforts of the GDAL, Fiona, and
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Geopandas communities.
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- Core I/O methods and supporting functions adapted from [Fiona](https://github.com/Toblerity/Fiona)
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- Inspired by [Fiona PR](https://github.com/Toblerity/Fiona/pull/540/files)
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