Skip to main content

Fundamental package for array computing in Python

Project description


Powered by NumFOCUS PyPI Downloads Conda Downloads Stack Overflow Nature Paper OpenSSF Scorecard

NumPy is the fundamental package for scientific computing with Python.

It provides:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code
  • useful linear algebra, Fourier transform, and random number capabilities

Testing:

NumPy requires pytest and hypothesis. Tests can then be run after installation with:

python -c 'import numpy; numpy.test()'

Code of Conduct

NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes our community thrive.

Call for Contributions

The NumPy project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through the mailing list first.

Writing code isn’t the only way to contribute to NumPy. You can also:

  • review pull requests
  • help us stay on top of new and old issues
  • develop tutorials, presentations, and other educational materials
  • maintain and improve our website
  • develop graphic design for our brand assets and promotional materials
  • translate website content
  • help with outreach and onboard new contributors
  • write grant proposals and help with other fundraising efforts

For more information about the ways you can contribute to NumPy, visit our website. If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or here, on GitHub, by opening a new issue or leaving a comment on a relevant issue that is already open.

Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact our community coordinators at numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for an invitation).

We also have a biweekly community call, details of which are announced on the mailing list. You are very welcome to join.

If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

numpy-1.24.2.tar.gz (10.9 MB view details)

Uploaded Source

Built Distributions

numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl (14.7 MB view details)

Uploaded PyPy Windows x86-64

numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.7 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (19.2 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

numpy-1.24.2-cp311-cp311-win_amd64.whl (14.8 MB view details)

Uploaded CPython 3.11 Windows x86-64

numpy-1.24.2-cp311-cp311-win32.whl (12.4 MB view details)

Uploaded CPython 3.11 Windows x86

numpy-1.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

numpy-1.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl (13.8 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

numpy-1.24.2-cp310-cp310-win_amd64.whl (14.8 MB view details)

Uploaded CPython 3.10 Windows x86-64

numpy-1.24.2-cp310-cp310-win32.whl (12.4 MB view details)

Uploaded CPython 3.10 Windows x86

numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

numpy-1.24.2-cp39-cp39-win_amd64.whl (14.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.24.2-cp39-cp39-win32.whl (12.5 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

numpy-1.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.24.2-cp39-cp39-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.24.2-cp39-cp39-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.24.2-cp38-cp38-win_amd64.whl (14.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.24.2-cp38-cp38-win32.whl (12.5 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

numpy-1.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.24.2-cp38-cp38-macosx_11_0_arm64.whl (13.8 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

numpy-1.24.2-cp38-cp38-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file numpy-1.24.2.tar.gz.

File metadata

  • Download URL: numpy-1.24.2.tar.gz
  • Upload date:
  • Size: 10.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2.tar.gz
Algorithm Hash digest
SHA256 003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22
MD5 c4212a8da1ecf17ece37e2afd0319806
BLAKE2b-256 e4a96704bb5e1d1d778d3a6ee1278a8d8134f0db160e09d52863a24edb58eab5

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 150947adbdfeceec4e5926d956a06865c1c690f2fd902efede4ca6fe2e657c3f
MD5 4fea9d95e0489d06c3a24a87697d2fc0
BLAKE2b-256 c7a98efd41b9fd69b791ccdc9075b5f82c2770b5bb6b2f7c04a18346fe8b805d

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f9006288bcf4895917d02583cf3411f98631275bc67cce355a7f39f8c14338fa
MD5 4c1466ae486b39d1a35aacb46256ec1e
BLAKE2b-256 96d287a37d505439bb92dd516c882a701fcbcae0efd95d3f1900baef8d88de93

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 92011118955724465fb6853def593cf397b4a1367495e0b59a7e69d40c4eb71d
MD5 07b6361e36e0093b580dc05799b1f03d
BLAKE2b-256 e3831d6e5de945573bf865f05fd92144b4c08c895e4b23fcd9c5ee4955185333

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.2-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 14.8 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 557d42778a6869c2162deb40ad82612645e21d79e11c1dc62c6e82a2220ffb04
MD5 e0b850f9c20871cd65ecb35235688f4d
BLAKE2b-256 175782c3a9321f5dbcbdbe407476ea93dc4fabcadc819fd9baddf3511ddd5833

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp311-cp311-win32.whl.

File metadata

  • Download URL: numpy-1.24.2-cp311-cp311-win32.whl
  • Upload date:
  • Size: 12.4 MB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 e428c4fbfa085f947b536706a2fc349245d7baa8334f0c5723c56a10595f9b95
MD5 5afd966db0b59655618c1859d98d87f6
BLAKE2b-256 fa72979b755c09696de035d835a78df94b079e3e51ea967efa0c800cff608847

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9a23f8440561a633204a67fb44617ce2a299beecf3295f0d13c495518908e910
MD5 13b57957a1f40e13f8826d14b031a6fe
BLAKE2b-256 b6d7b208a4a534732e4a978003768ac7b8c14fcd4ca5b1653ce4fb4c2826f3a4

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4cecaed30dc14123020f77b03601559fff3e6cd0c048f8b5289f4eeabb0eb281
MD5 c0dc33697d156e2b9a029095efeb1b10
BLAKE2b-256 8b1a34ba69424c19e4c3bd5d393d58ec5b8ff85711e77209a2d43563bf7fb178

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4173bde9fa2a005c2c6e2ea8ac1618e2ed2c1c6ec8a7657237854d42094123a0
MD5 03d71e3d9a086b56837c461fd7c9188b
BLAKE2b-256 0104a8b0bb5ffd6b36cb9ff9b67ca6966d55c4a9fdb40ace81a2b33d1559c3b7

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7de8fdde0003f4294655aa5d5f0a89c26b9f22c0a58790c38fae1ed392d44a5a
MD5 c093e61421be01ffff435387839949f1
BLAKE2b-256 1ba79582b169194a05642fcd05026b2e55fa7539230bfc28de7e13f116b0cd0b

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 14.8 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 97cf27e51fa078078c649a51d7ade3c92d9e709ba2bfb97493007103c741f1d0
MD5 2f939228a8c33265f2a8a1fce349d6f1
BLAKE2b-256 fadf53e8c0c8ccecf360b827a3d2b1b6060644c635c3149a9d6415a6fe4ccf44

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp310-cp310-win32.whl.

File metadata

  • Download URL: numpy-1.24.2-cp310-cp310-win32.whl
  • Upload date:
  • Size: 12.4 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 2620e8592136e073bd12ee4536149380695fbe9ebeae845b81237f986479ffc9
MD5 6df575dff02feac835d22debb15d190e
BLAKE2b-256 e73d0c52834c6c8f9e35b71e7a7202ca35bec639984aaea60056c763ade26f67

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a51725a815a6188c662fb66fb32077709a9ca38053f0274640293a14fdd22978
MD5 969f4f33baaff53dbbbaf1a146c43534
BLAKE2b-256 c521275cfa7731ee2e121b1bf85ddb21b8712fe2f409f02a8b61521af6e4993d

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 6524630f71631be2dabe0c541e7675db82651eb998496bbe16bc4f77f0772253
MD5 9ddadbf9cac2742318d8b292cb9ca579
BLAKE2b-256 34dc7470dde137734e311c5203d0a5854e03da12d7bef60784937efcbb1f8c08

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e8d2859428712785e8a8b7d2b3ef0a1d1565892367b32f915c4a4df44d0e64f5
MD5 2dbbe6f8a14e14978d24de9fcc8b49fe
BLAKE2b-256 8e322bd17fccc5decf3b904888f4f86b89e367a009273c665cbbbbfe515b43df

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 eef70b4fc1e872ebddc38cddacc87c19a3709c0e3e5d20bf3954c147b1dd941d
MD5 73fe0b507f56c0baf43171a76ad2003f
BLAKE2b-256 39fd217e9bf573f710827416e1e6f56a6355b90c2ce7fbf8b83d5729d5b2e0b6

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 14.9 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 a77d3e1163a7770164404607b7ba3967fb49b24782a6ef85d9b5f54126cc39e5
MD5 7705c6b0bcf22b5e64cf248144b2f554
BLAKE2b-256 9d3b13404993b5dec7403abcf9518569316b5d72d9a3081cd90aca130e6d8b00

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.24.2-cp39-cp39-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 63e45511ee4d9d976637d11e6c9864eae50e12dc9598f531c035265991910468
MD5 c25f7fbb185f1b8f7761bc22082d9939
BLAKE2b-256 b5f8a775da630e8bacfd2650fea40ff82659dc8e7baa2f9e09e8e57fce2d1279

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f64bb98ac59b3ea3bf74b02f13836eb2e24e48e0ab0145bbda646295769bd780
MD5 0c0ea440190705f98abeaa856e7da690
BLAKE2b-256 f4f445e6e3f7a23b9023554903a122c95585e9787f9403d386bafb7a95d24c9b

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 889b2cc88b837d86eda1b17008ebeb679d82875022200c6e8e4ce6cf549b7acb
MD5 ce97d81e4ae6e10241d471492391b1be
BLAKE2b-256 ba2b404f675b848033b23d688e5bdc55ec1d62b62f5568dda7f80edb147b637e

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 adbdce121896fd3a17a77ab0b0b5eedf05a9834a18699db6829a64e1dfccca7f
MD5 e0281b96c490ba00f1382eb3984b4e51
BLAKE2b-256 3877b0afa98a670cb255f15155a856ef257a82aa0b72e435f5f58da31d9dc944

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4199e7cfc307a778f72d293372736223e39ec9ac096ff0a2e64853b866a8e18a
MD5 93a4984da83c6811367d3daf709ed25c
BLAKE2b-256 cae0f719500114ec3d291718ddbb1bfc3d1db7f9adb17b5c69aa617fe95c17fc

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 14.9 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 76807b4063f0002c8532cfeac47a3068a69561e9c8715efdad3c642eb27c0756
MD5 66e93d70fad16b4ccb4531e31aad36e3
BLAKE2b-256 bf8c3d36cef521739bd481e9a5b30e5c0f9faf8b7fe7b904238368908a9d149d

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.24.2-cp38-cp38-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.2-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 e3ab5d32784e843fc0dd3ab6dcafc67ef806e6b6828dc6af2f689be0eb4d781d
MD5 1a45f4373945eaeabeaa4020ce04e8fd
BLAKE2b-256 c9806b576acc5098d31c135ad7acd38045169bdb19bfbfdf554d914b13929823

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2eabd64ddb96a1239791da78fa5f4e1693ae2dadc82a76bc76a14cbb2b966e96
MD5 e77155c010f9dd63ea2815579a28c503
BLAKE2b-256 9cee77768cade9607687fadbcc1dcbb82dba0554154b3aa641f9c17233ffabe8

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c29e6bd0ec49a44d7690ecb623a8eac5ab8a923bce0bea6293953992edf3a76a
MD5 4f930a9030d77d45a1cb6f374c91fb53
BLAKE2b-256 56164a1ccd05d4f77f78f64cb9cb9f3121edeeebd23d9264b0dcb903889f9c1e

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c72a6b2f4af1adfe193f7beb91ddf708ff867a3f977ef2ec53c0ffb8283ab9f5
MD5 bdd6eede4524a230574b37e1f631f2c0
BLAKE2b-256 28e48acb46849784d2cefa383596299123d3f0330c627fa55c95bfd4a0ef5172

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d0a2db9d20117bf523dde15858398e7c0858aadca7c0f088ac0d6edd360e9ad2
MD5 9a30452135ab0387b8ea9007e94e9f81
BLAKE2b-256 ff987b71cabcce208f8c67398e812068524e473a143342583d55955cbb92b463

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