Vectorized spatial vector file format I/O using GDAL/OGR

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for bcward from gravatar.com bcward Avatar for jorisvandenbossche from gravatar.com jorisvandenbossche Avatar for martinfleis from gravatar.com martinfleis

Unverified details

These details have not been verified by PyPI
Project links
Meta

Project description

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 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.

WARNING: Pyogrio is still at an early version and the API is subject to substantial change. Please see CHANGES.

Requirements

Supports Python 3.8 - 3.11 and GDAL 3.4.x - 3.8.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 and PyPI for Linux, MacOS, and Windows.

Please read the installation documentation 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 for more information.

Getting started

Please read the introduction for more information and examples to get started using Pyogrio.

You can also check out the the API documentation 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
  • Inspired by Fiona PR

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for bcward from gravatar.com bcward Avatar for jorisvandenbossche from gravatar.com jorisvandenbossche Avatar for martinfleis from gravatar.com martinfleis

Unverified details

These details have not been verified by PyPI
Project links
Meta

Release history Release notifications | RSS feed

0.10.0

0.9.0

This version

0.8.0

0.7.2

0.7.1

0.7.0

0.6.0

0.5.1

0.5.0

0.4.2

0.4.1

0.4.0

0.4.0b1 pre-release

0.4.0a1 pre-release

0.3.0

0.2.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyogrio-0.8.0.tar.gz (349.8 kB view details)

Uploaded Source

Built Distributions

pyogrio-0.8.0-cp312-cp312-win_amd64.whl (15.9 MB view details)

Uploaded CPython 3.12 Windows x86-64

pyogrio-0.8.0-cp312-cp312-manylinux_2_28_aarch64.whl (22.4 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.28+ ARM64

pyogrio-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.3 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

pyogrio-0.8.0-cp312-cp312-macosx_11_0_arm64.whl (14.6 MB view details)

Uploaded CPython 3.12 macOS 11.0+ ARM64

pyogrio-0.8.0-cp312-cp312-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.12 macOS 10.9+ x86-64

pyogrio-0.8.0-cp311-cp311-win_amd64.whl (15.9 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyogrio-0.8.0-cp311-cp311-manylinux_2_28_aarch64.whl (22.5 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.28+ ARM64

pyogrio-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.3 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pyogrio-0.8.0-cp311-cp311-macosx_11_0_arm64.whl (14.6 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pyogrio-0.8.0-cp311-cp311-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

pyogrio-0.8.0-cp310-cp310-win_amd64.whl (15.9 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyogrio-0.8.0-cp310-cp310-manylinux_2_28_aarch64.whl (22.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.28+ ARM64

pyogrio-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pyogrio-0.8.0-cp310-cp310-macosx_11_0_arm64.whl (14.6 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pyogrio-0.8.0-cp310-cp310-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pyogrio-0.8.0-cp39-cp39-win_amd64.whl (15.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyogrio-0.8.0-cp39-cp39-manylinux_2_28_aarch64.whl (22.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.28+ ARM64

pyogrio-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.1 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pyogrio-0.8.0-cp39-cp39-macosx_11_0_arm64.whl (14.6 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pyogrio-0.8.0-cp39-cp39-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pyogrio-0.8.0-cp38-cp38-win_amd64.whl (15.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyogrio-0.8.0-cp38-cp38-manylinux_2_28_aarch64.whl (22.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.28+ ARM64

pyogrio-0.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pyogrio-0.8.0-cp38-cp38-macosx_11_0_arm64.whl (14.6 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pyogrio-0.8.0-cp38-cp38-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file pyogrio-0.8.0.tar.gz.

File metadata

  • Download URL: pyogrio-0.8.0.tar.gz
  • Upload date:
  • Size: 349.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pyogrio-0.8.0.tar.gz
Algorithm Hash digest
SHA256 459ec590c3c1cda451f3f73c88a678c32b127de783bf54c41ea6ad708969f020
MD5 965b8881433a8de74e219554d8bf6026
BLAKE2b-256 6bd5e6f53c9db584734f735f63419740a029021e0297920d7d0487d80253d4e3

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: pyogrio-0.8.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 15.9 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pyogrio-0.8.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 1bcca7902eb7c3c5d2d7528f6fa86cd9f0409a993e63c661dfec2415ad5a3a55
MD5 5cdd51f486bf3bf6643ed522f1166fc3
BLAKE2b-256 44ae7815a143e3d84b9e37ebc314da07b616f6adeacec733eef1f7e5bb34d0aa

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 83bdf508a1903328731d3843955a951b717092317e6192ff6d882ceece559da7
MD5 a32f2b43b7a1f4dee15c58155db4b04c
BLAKE2b-256 bde34a2541c33186d84ad5fcd5fcae956b28bedf7cb8bef2bcdfa06f59dc291d

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 12eaa1dd13d4632f12e334f7dccbbf7cea6ae3ed967d8db2b0ade44787f44cfa
MD5 5d503c4f393dd15e5e2b2ce1ba0d8527
BLAKE2b-256 e7f5543730da44138cbf5c8687fca57ba404942fd4027a41e7ee464abad8c760

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e83ddf7e821ec3c1376ab9cf57ba3def18f687a117e43f508c4be1174045ffc7
MD5 611fbe6af568a7c0405708e17c439d12
BLAKE2b-256 2631872b209cf00c8503314d44928f9626ffc23e6b22370400e01f8067153402

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2779f0c04573321b879570e07de3525cadb4e47efa4bc245ff655a419d84a3e2
MD5 c0b52fea9f58bd9ec76496e879a50d5d
BLAKE2b-256 c7438226d638aa8da5099e4663db10f6cbec02fa1092f43638b765b8c95d1138

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pyogrio-0.8.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 15.9 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pyogrio-0.8.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 fc77e8b07cd971601868e1c8ff1fa231773df1c889ca6e2c99a0ad22380aefb2
MD5 5a72bf5e1f93ba14f7316b66776e958b
BLAKE2b-256 03a7b0c02e77d1a841adc1e6b3d32d055157236a42a047611fc46e6a3cd4616c

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f67c0e68896fd49218e6451a95d2dee75816a715fb80048250c21e3b39babc48
MD5 2408feef86634cd92ec0764fe0db6434
BLAKE2b-256 a5bbb07827df08c69e12ca016ea67a0cd355a54c1f956b7a35b25beb778a2837

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 63dc3ef1272c4b01b8dd6a5fd3c696ab81cc6f7030d2dcb266d01fb8909e27cf
MD5 ea52251b47f60da9761351dbfce8fb88
BLAKE2b-256 97ba40d6eff624efa0a420bfff23415bdd404e1a79596ca9006daa19c01569ce

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7a830a0a89f5846c4b7fbb9faf34d3ca5cdb56f427f0eb12dfa46bc2664affda
MD5 2962d17e2680572633e99f1319941546
BLAKE2b-256 73f8f49bcca618d635ad44aecf29047a1594c5af783a6fb6cbf41f6adf1ffe0d

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dca63e63a50ca69e0334d6d4f8904e622974007ed072f179f5c26f81277a6dbe
MD5 99c0607506ab236f9660f38b0d639e58
BLAKE2b-256 f71cc292951110f793ab9561183c13dd4c32d700cfa4a4753a8ec08ecdb25255

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyogrio-0.8.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 15.9 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pyogrio-0.8.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 3a574acccec2a2d8af097a01975688bc059d8d03b49c56b4ee029eb3d9b384e2
MD5 664e206274b1f3dde425776499256373
BLAKE2b-256 bb0db3c3ff216a8af58d22ad5e8433ce1508f2f7f3c6518c033614f20874b2a4

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 ed10c44e7b2fe899ecd25e9bdd82a1b4ba5d2792850924858f7aea04cad3d801
MD5 9f109d512cac212ff5cd3d3bb33548ad
BLAKE2b-256 adb0194a00b89cbc34384cb7326da43b0bda941bf904b83284b0691dd76784e5

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3c2550195df759486337aea76353c467bb7f4b5287df6bc789756d4b7f84aab8
MD5 981fa50316a29c461b3191e281b5f662
BLAKE2b-256 3f18545ad9df7b119955d58c9d19142afb82683fd5b3dc7ca74a6f5f8ac1f5da

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4fc58e9e9201605bd5f9056734e67314a296a696f28a71c9a0fb0152108965b0
MD5 de7e768eb42a0b45738b626849803f66
BLAKE2b-256 777cf75b7d7aebcd94dc863bb3005a9180efd3f3806d461864bad3d6cace2d76

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7434b5222e6c373c9fc01937fdea7e1cfa88030770efe650f296cdd7656f7c35
MD5 150c346a61c56349dc44a9db7cbed7fb
BLAKE2b-256 d858dd4233bcdb5d158e02a7f86071e1b7533d6987d24982f805efe817b631ea

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyogrio-0.8.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 15.9 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pyogrio-0.8.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 a06cfd08d9abcc28a1ae07f3c3def02c6698767a8200c0248f3cd06e50e27433
MD5 0974eb90d1d1102cbbcccb7dca38307e
BLAKE2b-256 6e406a907eda3a21dfa9e90922d3abdba9dbe9ff106dc25c96f091cc996ff658

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp39-cp39-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp39-cp39-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 00347b7187eb289819295333cf363161eb4cf8d8abb469af7f5bc84a00916ff5
MD5 326b5124ca70bc2efb4a2a81142c424a
BLAKE2b-256 756c961fec7ef9394791aac3ab7bc656108819ad2bf547b1f74cde78e16b1f3e

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e09b72756700537456007ee7c2734cc76cb2239e20617735b019a2c402b93a91
MD5 052bef5d1f4b035586310c61ce49cdd3
BLAKE2b-256 756ce2e20ca14aa0d503643d2594ff544593202df0c813ea659fc2a6efd74e74

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d78585d100cb86b98582702416e56cd6bc10540a6cbedfd2b5d3d75b9a44e3d3
MD5 6e0280943ff263752d36c2ec49e2d1d1
BLAKE2b-256 47f8ad69d21a8e41d6098a9aa84323ec0410e8052aef4de1a2dd9caa640d2851

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 56997a41ef9cf1e36996526e9f9c1f620d18cfc67cebe94afcd82d44315b490f
MD5 f2716e95acdbcda9e4243932b6661cb1
BLAKE2b-256 ec7b9f20e4e414fcaa901acebf8e45b1b5ef90b920234bda02f4ce9ba60bdf88

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyogrio-0.8.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 15.9 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pyogrio-0.8.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e8cc26bb7685371da471e8b6107bed9fe4a56a4fe649c38dea8aaee99b26bf2e
MD5 df561f5ba1c9d632bd794bad4889b299
BLAKE2b-256 8f987acc15d013a62b9c6f679e5da1433acb7ab32637958aa7c2da108fda29cc

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp38-cp38-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp38-cp38-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 04e673fe5d712711eed4877aed069683f9734948a66e58785f045166343c5f3f
MD5 15883c1351baaa7d6556558d598acb5d
BLAKE2b-256 344835956c0379b1fec0d2e5a95034ae4e0da3cb945213bdc5beddc86c4d6adb

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bd4c7b294c62d48f4263db78d8998d27db29298cfbbbe244ba7687a6280853c7
MD5 4c6b76c55eeb348f92049fed2d534852
BLAKE2b-256 154e4bcfb8e92a97e949a7341db88493d7803551ce40bda0749a98c31463c612

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b8fef111416d149d098e999cf2df2dd3cbe3d8dac2b7c63017b8b931dcbde51b
MD5 0fb11727814ba258497aac7a5a6db893
BLAKE2b-256 fbbcfeaa6191ca52d154cdeab082d485fbf814fa079504d90fefe93ac5ea417b

See more details on using hashes here.

Provenance

File details

Details for the file pyogrio-0.8.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyogrio-0.8.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3b05184adb265d8b1d17b8da4a0d08c311ba09d38d843286184d399997028344
MD5 78503548ed44dad5ab76b80772764680
BLAKE2b-256 8615e847e7c203f91451a2e746d572e2463df8743d928938c3ccb241c348e606

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page