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-1.24.3.tar.gz (10.9 MB view details)

Uploaded Source

Built Distributions

numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl (14.7 MB view details)

Uploaded PyPy Windows x86-64

numpy-1.24.3-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-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (19.2 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

numpy-1.24.3-cp311-cp311-win_amd64.whl (14.8 MB view details)

Uploaded CPython 3.11 Windows x86-64

numpy-1.24.3-cp311-cp311-win32.whl (12.4 MB view details)

Uploaded CPython 3.11 Windows x86

numpy-1.24.3-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-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl (13.8 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

numpy-1.24.3-cp310-cp310-win_amd64.whl (14.8 MB view details)

Uploaded CPython 3.10 Windows x86-64

numpy-1.24.3-cp310-cp310-win32.whl (12.4 MB view details)

Uploaded CPython 3.10 Windows x86

numpy-1.24.3-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-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

numpy-1.24.3-cp39-cp39-win_amd64.whl (14.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.24.3-cp39-cp39-win32.whl (12.5 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.24.3-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-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.24.3-cp38-cp38-win_amd64.whl (14.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.24.3-cp38-cp38-win32.whl (12.5 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.24.3-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-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl (13.8 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

numpy-1.24.3-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-1.24.3.tar.gz.

File metadata

  • Download URL: numpy-1.24.3.tar.gz
  • Upload date:
  • Size: 10.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.3

File hashes

Hashes for numpy-1.24.3.tar.gz
Algorithm Hash digest
SHA256 ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155
MD5 89e5e2e78407032290ae6acf6dcaea46
BLAKE2b-256 2cd4590ae7df5044465cc9fa2db152ae12468694d62d952b1528ecff328ef7fc

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 35400e6a8d102fd07c71ed7dcadd9eb62ee9a6e84ec159bd48c28235bbb0f8e4
MD5 0848bd41c08dd5ebbc5a7f0788678e0e
BLAKE2b-256 15b8cbe1750b9ec78062e5a00ef39ff8bdf189ce753b411b6b35931ababaee47

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1a7d6acc2e7524c9955e5c903160aa4ea083736fde7e91276b0e5d98e6332812
MD5 6abd9dba54405182e6e7bb32dbe377bb
BLAKE2b-256 eb102c3c672034d860bcca50b65d656e24c4e2ace9fb452fdd81da78cb7418a1

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 352ee00c7f8387b44d19f4cada524586f07379c0d49270f87233983bc5087ca0
MD5 374695eeef5aca32a5b7f2f518dd3ba1
BLAKE2b-256 5aabd0eff89e0c05cc86fa7955c5e54e8ed0957a8a97a2516384b9ffd82008cc

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 5342cf6aad47943286afa6f1609cad9b4266a05e7f2ec408e2cf7aea7ff69d80
MD5 e6de5b7d77dc43ed47f516eb10bbe8b6
BLAKE2b-256 f0e81ea9adebdccaadfc208c7517e09f5145ed5a73069779ff436393085d47a2

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp311-cp311-win32.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 c91c4afd8abc3908e00a44b2672718905b8611503f7ff87390cc0ac3423fb096
MD5 e97699a4ef96a81e0916bdf15440abe0
BLAKE2b-256 9484ed45416c8319c02348a5812d5647796a0833e3fb5576d01758f2a72e9200

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a1d3c026f57ceaad42f8231305d4653d5f05dc6332a730ae5c0bea3513de0950
MD5 fe18b810bcf284572467ce585dbc533b
BLAKE2b-256 8219321d369ede7458500f59151101470129d14f3b6768bb9b99bb7156f526b5

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 76e3f4e85fc5d4fd311f6e9b794d0c00e7002ec122be271f2019d63376f1d385
MD5 d75bbfb06ed00d04232dce0e865eb42c
BLAKE2b-256 72eb9c77bbc4d2b4ca17ef253621794a2d42897d896f86cd493db3eabe1a7d25

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d6cc757de514c00b24ae8cf5c876af2a7c3df189028d68c0cb4eaa9cd5afc2bf
MD5 cfa001dcd07cdf6414ced433e88959d4
BLAKE2b-256 ee6c7217a8844dfe22e349bccbecd35571fa72c5d7fe8b33d8c5540e8cc2535c

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9a7721ec204d3a237225db3e194c25268faf92e19338a35f3a224469cb6039a3
MD5 5323fb0323d1ec10ee3c35a2fa79cbcd
BLAKE2b-256 ec7df69c47ea3db0cd8ca444aec241a80b538eb176ae756820489a9d2946ec8c

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 ab5f23af8c16022663a652d3b25dcdc272ac3f83c3af4c02eb8b824e6b3ab9d7
MD5 a3329efa646012fa4ee06ce5e08eadaf
BLAKE2b-256 655d46da284b0bf6cfbf04082c3c5e84399664d69e41c11a33587ad49b0c64e5

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp310-cp310-win32.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 f21c442fdd2805e91799fbe044a7b999b8571bb0ab0f7850d0cb9641a687092b
MD5 3c72962360bcd0938a6bddee6cdca766
BLAKE2b-256 89e3e2f478b2ff131e7c3171044a87e74df61db4b67fbcb90be479c07a44d0a7

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2d926b52ba1367f9acb76b0df6ed21f0b16a1ad87c6720a1121674e5cf63e2b6
MD5 3673aa638746851dd19d5199e1eb3a91
BLAKE2b-256 6f7238f9a536bdb5bfb1682f2520f133ec6e08dde8bcca1f632e347641d90763

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8535303847b89aa6b0f00aa1dc62867b5a32923e4d1681a35b5eef2d9591a463
MD5 a99234799a239e7e9c6fa15c212996df
BLAKE2b-256 62e4cd77d5f3d02c30d9ca8f2995df3cb3974c75cf1cc777fad445753475c4e4

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 202de8f38fc4a45a3eea4b63e2f376e5f2dc64ef0fa692838e31a808520efaf7
MD5 39691ff3d1612438dfcd3266c9765aab
BLAKE2b-256 fa7d8dfb40eecbb6bc83ca00ef979f5cdeca5909a250cb8b642dcf1fbd34c078

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3c1104d3c036fb81ab923f507536daedc718d0ad5a8707c6061cdfd6d184e570
MD5 93a3ce07e3773842c54d831f18e3eb8d
BLAKE2b-256 f3237cc851bae09cf4db90d42a701dfe525780883ada86bece45e3da7a07e76b

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d5036197ecae68d7f491fcdb4df90082b0d4960ca6599ba2659957aafced7c17
MD5 bfe332e577c604d6d62a57381e6aa0a6
BLAKE2b-256 767ccfb8ac4925defbe222aec15ac6b42b2a3d9bab7c9d13a2e767f534b35c2e

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 784c6da1a07818491b0ffd63c6bbe5a33deaa0e25a20e1b3ea20cf0e43f8046c
MD5 c86e648389e333e062bea11c749b9a32
BLAKE2b-256 d5d607b37e7fecad7d158aabb4782a1b941e10afe8b80ec24cd64285a5bbb81b

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 210461d87fb02a84ef243cac5e814aad2b7f4be953b32cb53327bb49fd77fbb4
MD5 1f163b9ea417c253e84480aa8d99dee6
BLAKE2b-256 83bede078ac5e4ff572b1bdac1808b77cea2013b2c6286282f89b1de3e951273

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ea8282b9bcfe2b5e7d491d0bf7f3e2da29700cec05b49e64d6246923329f2b02
MD5 065464a8d918c670c7863d1e72e3e6dd
BLAKE2b-256 96922a8c1356e226311cf885e04eff576df8c357b2626c47c9283024bc24e01e

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0ec87a7084caa559c36e0a2309e4ecb1baa03b687201d0a847c8b0ed476a7187
MD5 3ae7ac30f86c720e42b2324a0ae1adf5
BLAKE2b-256 5441fb17c1d48a574c50422ff3f1b17ed979b755adc6ed291c4a44a76e226c67

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4719d5aefb5189f50887773699eaf94e7d1e02bf36c1a9d353d9f46703758ca4
MD5 44b08a293a4e12d62c27b8f15ba5664e
BLAKE2b-256 0d43643629a4a278b4815541c7d69856c07ddb0e99bdc62b43538d3751eae2d8

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 56e48aec79ae238f6e4395886b5eaed058abb7231fb3361ddd7bfdf4eed54289
MD5 c4708ef009bb5d427ea94a4fc4a10e12
BLAKE2b-256 1a62af7e78a12207608b23e3b2e248fc823fbef75f17d5defc8a127c5661daca

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.24.3-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/3.11.3

File hashes

Hashes for numpy-1.24.3-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 d933fabd8f6a319e8530d0de4fcc2e6a61917e0b0c271fded460032db42a0fe4
MD5 350934bae971d0ebe231a59b640069db
BLAKE2b-256 53f7bf6e2b973c6d6a4c60f722dd95322d4997b4999347d67c5c74a4042a07b7

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4749e053a29364d3452c034827102ee100986903263e89884922ef01a0a6fd2f
MD5 8cc87b88163ed84e70c48fd0f5f8f20e
BLAKE2b-256 8bd9814a619ab84d8eb0d95e08d4c723e665f1e694b5a6068ca505a61bdc3745

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ecde0f8adef7dfdec993fd54b0f78183051b6580f606111a6d789cd14c61ea0c
MD5 7b7dae3309e7ca8a8859633a5d337431
BLAKE2b-256 a7fe72493149c65dcd39d8c8dc09870e242bd689d1db2bde3ec479807bf0d414

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ae8d0be48d1b6ed82588934aaaa179875e7dc4f3d84da18d7eae6eb3f06c242c
MD5 e47ac5521b0bfc3effb040072d8a7902
BLAKE2b-256 ca13c5bc0100b425f007412c3ba5d71e5ae9c08260fecbffd620764a9df1f4de

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.24.3-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.24.3-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7776ea65423ca6a15255ba1872d82d207bd1e09f6d0894ee4a64678dd2204078
MD5 dd04ebf441a8913f4900b56e7a33a75e
BLAKE2b-256 794a63a79242763edde0b5025d104cc2b78c44d89310b1bbc9b0f64a96b72ea0

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