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

  • and much more

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

All NumPy wheels distributed on PyPI are BSD licensed.

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

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

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.5.2)

Owner

PyPI data
Data sourced directly from PyPI's database.

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for mattip from gravatar.com mattip

Credits

Author: Travis E. Oliphant et al.

Maintainer: NumPy Developers

License

BSD License (BSD)

Requires

Python >=3.8

Classifiers

Report project as malware

Download files

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

Source Distribution

numpy-1.23.3.tar.gz (10.7 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

numpy-1.23.3-pp38-pypy38_pp73-win_amd64.whl (14.5 MB view details)

Uploaded PyPyWindows x86-64

numpy-1.23.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

numpy-1.23.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (17.5 MB view details)

Uploaded PyPymacOS 10.9+ x86-64

numpy-1.23.3-cp311-cp311-win_amd64.whl (14.6 MB view details)

Uploaded CPython 3.11Windows x86-64

numpy-1.23.3-cp311-cp311-win32.whl (12.2 MB view details)

Uploaded CPython 3.11Windows x86

numpy-1.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

numpy-1.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

numpy-1.23.3-cp311-cp311-macosx_11_0_arm64.whl (13.3 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

numpy-1.23.3-cp311-cp311-macosx_10_9_x86_64.whl (18.1 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

numpy-1.23.3-cp310-cp310-win_amd64.whl (14.6 MB view details)

Uploaded CPython 3.10Windows x86-64

numpy-1.23.3-cp310-cp310-win32.whl (12.2 MB view details)

Uploaded CPython 3.10Windows x86

numpy-1.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

numpy-1.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

numpy-1.23.3-cp310-cp310-macosx_11_0_arm64.whl (13.3 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

numpy-1.23.3-cp310-cp310-macosx_10_9_x86_64.whl (18.1 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

numpy-1.23.3-cp39-cp39-win_amd64.whl (14.7 MB view details)

Uploaded CPython 3.9Windows x86-64

numpy-1.23.3-cp39-cp39-win32.whl (12.2 MB view details)

Uploaded CPython 3.9Windows x86

numpy-1.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

numpy-1.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

numpy-1.23.3-cp39-cp39-macosx_11_0_arm64.whl (13.4 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

numpy-1.23.3-cp39-cp39-macosx_10_9_x86_64.whl (18.1 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

numpy-1.23.3-cp38-cp38-win_amd64.whl (14.7 MB view details)

Uploaded CPython 3.8Windows x86-64

numpy-1.23.3-cp38-cp38-win32.whl (12.2 MB view details)

Uploaded CPython 3.8Windows x86

numpy-1.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

numpy-1.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

numpy-1.23.3-cp38-cp38-macosx_11_0_arm64.whl (13.3 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

numpy-1.23.3-cp38-cp38-macosx_10_9_x86_64.whl (18.1 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file numpy-1.23.3.tar.gz.

File metadata

  • Download URL: numpy-1.23.3.tar.gz
  • Upload date:
  • Size: 10.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3.tar.gz
Algorithm Hash digest
SHA256 51bf49c0cd1d52be0a240aa66f3458afc4b95d8993d2d04f0d91fa60c10af6cd
MD5 6efc60a3f6c1b74c849d53fbcc07807b
BLAKE2b-256 0a88f4f0c7a982efdf7bf22f283acf6009b29a9cc5835b684a49f8d3a4adb22f

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-pp38-pypy38_pp73-win_amd64.whl.

File metadata

  • Download URL: numpy-1.23.3-pp38-pypy38_pp73-win_amd64.whl
  • Upload date:
  • Size: 14.5 MB
  • Tags: PyPy, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 94c15ca4e52671a59219146ff584488907b1f9b3fc232622b47e2cf832e94fb8
MD5 5338d997a3178750834e742a257dfa4a
BLAKE2b-256 c6fd9c3a4e030858115bfee2f81351c4614c475883cb97a3297d8de039dac046

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 91b8d6768a75247026e951dce3b2aac79dc7e78622fc148329135ba189813584
MD5 56a0c90a303979d5bf8fc57e86e57ccb
BLAKE2b-256 aaa792b7d2698d7deabd0b846c3ad487495ef3af1dceac8a1f2b358cd20caecf

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.23.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 17.5 MB
  • Tags: PyPy, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 17c0e467ade9bda685d5ac7f5fa729d8d3e76b23195471adae2d6a6941bd2c18
MD5 3b5a51f78718a1a82d2750ec159f9acf
BLAKE2b-256 80d929eb382c47203f3ee7a758eb1e2620daad08c3f74372a752b2965d8d8c7a

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 14.6 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 8355fc10fd33a5a70981a5b8a0de51d10af3688d7a9e4a34fcc8fa0d7467bb7f
MD5 237dbd94e5529065c0c5cc4e47ceeb7e
BLAKE2b-256 2ebd286dacf2655c4db1a5076390337c746452a08def20daa53b4903722545d2

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp311-cp311-win32.whl.

File metadata

  • Download URL: numpy-1.23.3-cp311-cp311-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 7cd1328e5bdf0dee621912f5833648e2daca72e3839ec1d6695e91089625f0b4
MD5 a42c3d058bcef47b26841bf9472a89bf
BLAKE2b-256 584f55b0ea97b18e885b67aa41a9929d6a6414da7ddad5ebbd10a9c4ad086640

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 301c00cf5e60e08e04d842fc47df641d4a181e651c7135c50dc2762ffe293dbd
MD5 202bc3a8617f479ebe60ca0dec29964b
BLAKE2b-256 d2099ab4e760206c10081d253db9a3d4db8fd040ffcec9d7cdf5376d99531d54

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bdc9febce3e68b697d931941b263c59e0c74e8f18861f4064c1f712562903411
MD5 55d6a6439913ba84ad89268e0ad59fa0
BLAKE2b-256 df5b7f03caa84950cf02f841c910a249ae526a858de9c87ef4357cfb723b02b1

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp311-cp311-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.11, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2ad3ec9a748a8943e6eb4358201f7e1c12ede35f510b1a2221b70af4bb64295c
MD5 f395dcf622dff0ba44777cbae0442189
BLAKE2b-256 b1840af94541d21dd2d403377209f462b6b463dc4ba15158776285f1af2132ac

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp311-cp311-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 18.1 MB
  • Tags: CPython 3.11, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1f27b5322ac4067e67c8f9378b41c746d8feac8bdd0e0ffede5324667b8a075c
MD5 57cf29f781be955a9cd0de8d07fbce56
BLAKE2b-256 8d51bfaf6dc8279c58fcd8234e9b0f0b86146e3a8814159cd30875154f743061

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 14.6 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 39a664e3d26ea854211867d20ebcc8023257c1800ae89773cbba9f9e97bae036
MD5 605da65b9b66dfce8b62d847cb3841f7
BLAKE2b-256 51b6861f5e9d59c1bb6c05467f5ddcba965cb2c4b1fd62f6bf7b4c4632492625

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp310-cp310-win32.whl.

File metadata

  • Download URL: numpy-1.23.3-cp310-cp310-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 98dcbc02e39b1658dc4b4508442a560fe3ca5ca0d989f0df062534e5ca3a5c1a
MD5 84916178e5f4d073d0008754cba7f300
BLAKE2b-256 7525196d280e3570e19bc9c553af6941b13289ff520069bffa047a80b47e549a

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 004f0efcb2fe1c0bd6ae1fcfc69cc8b6bf2407e0f18be308612007a0762b4089
MD5 a82e2ecc4060a37dae5424e624eabfe3
BLAKE2b-256 c7310298a8f62a8c82b8c542f78f3761e67cb8bf0450b3e61bbe66c5c54c1a81

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0ea3f98a0ffce3f8f57675eb9119f3f4edb81888b6874bc1953f91e0b1d4f440
MD5 f482a4be6954b1b606320f0ffc1995dd
BLAKE2b-256 98e0481ed31801a69089aac50fe57229f8396b1d9cf4c85054275f9c909b13d9

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.10, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ffcf105ecdd9396e05a8e58e81faaaf34d3f9875f137c7372450baa5d77c9a54
MD5 59b43423a692f5351c6a43b852b210d7
BLAKE2b-256 d7da417e298fd3998ed1df4b492b400757ec331c310d4d6cbf5a2f4aa93717e8

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp310-cp310-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 18.1 MB
  • Tags: CPython 3.10, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c9f707b5bb73bf277d812ded9896f9512a43edff72712f31667d0a8c2f8e71ee
MD5 a60bf0b1d440bf18d87c49409036d05a
BLAKE2b-256 3a0c8d8fe64dcfbeac1dabeb8ae74c8b697a18cf48adfced980291abcc266984

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 14.7 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 78a63d2df1d947bd9d1b11d35564c2f9e4b57898aae4626638056ec1a231c40c
MD5 880dc73de09fccda0650e9404fa83608
BLAKE2b-256 23a40c900aa23c934018f714f1c168e6f615bc70fc26a9a996b06185e6d33665

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.23.3-cp39-cp39-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 c1ba66c48b19cc9c2975c0d354f24058888cdc674bebadceb3cdc9ec403fb5d1
MD5 318d0a2a27b7e361295c0382a0ff4a94
BLAKE2b-256 89680400fdd510bc2e22aa658873110a525452de90bd1058eb55183438d8527b

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d5422d6a1ea9b15577a9432e26608c73a78faf0b9039437b075cf322c92e98e7
MD5 fb80d38c37aae1e4d416cd4de068ff0a
BLAKE2b-256 fe8c1dfc141202fb2b6b75f9ca4a6681eb5cb7f328d6d2d9d186e5675c468e6a

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e5d5420053bbb3dd64c30e58f9363d7a9c27444c3648e61460c1237f9ec3fa14
MD5 f8fb0178bc34a198d5ce4e166076e1fc
BLAKE2b-256 07ef282bcb710c7e5a6f56a77529e5f8d42ad05ed44f87e65f2771937d5b84aa

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8c79d7cf86d049d0c5089231a5bcd31edb03555bd93d81a16870aa98c6cfb79d
MD5 bc1782f5d79187d63d14ed69a6a411e9
BLAKE2b-256 243db06a0b15aad299c8e53c752b8deaf2431b4f1a4d281bb536019ce4ec2659

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 18.1 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 09f6b7bdffe57fc61d869a22f506049825d707b288039d30f26a0d0d8ea05164
MD5 b001f7e17df798f9b949bbe259924c77
BLAKE2b-256 81036b9e924e39d90a67a7c01952b5768818ed24f888b0da9f333ad2246a3514

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 14.7 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e868b0389c5ccfc092031a861d4e158ea164d8b7fdbb10e3b5689b4fc6498df6
MD5 ef430e830a9fea7d8db0218b901671f6
BLAKE2b-256 59ec57f87fe9dc2f8390edd1341d2ee9caa90c251f09524286476f536555ffc1

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.23.3-cp38-cp38-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 f8c02ec3c4c4fcb718fdf89a6c6f709b14949408e8cf2a2be5bfa9c49548fd85
MD5 12817838edc1e1bea27df79f3a83da5d
BLAKE2b-256 48714a3866a3eedf80a2ca3cd5f56e1f7afdd041b2f0e6339b6a0d8a631f6b28

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 efd9d3abe5774404becdb0748178b48a218f1d8c44e0375475732211ea47c67e
MD5 29ccb3a732027ee1abe23a9562c32d0c
BLAKE2b-256 d6e2bed33bdbf513cd6d3fcb4377792ef1b8aad941da542a191e1e2a98c6621f

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a64403f634e5ffdcd85e0b12c08f04b3080d3e840aef118721021f9b48fc1460
MD5 4e75ac61e34f1bf23e7cbd6e2bfc7a32
BLAKE2b-256 243efbbef2c3a04ed1d237fe4711146f111631d02f5155c1dbeb713005787cf5

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.8, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 22d43376ee0acd547f3149b9ec12eec2f0ca4a6ab2f61753c5b29bb3e795ac4d
MD5 054234695ed3d955fb01f661db2c14fc
BLAKE2b-256 3b93e613ce34c908f3228fa181241ae9505c42a72ffc630af9e5173c2f26f406

See more details on using hashes here.

File details

Details for the file numpy-1.23.3-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.23.3-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 18.1 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.6

File hashes

Hashes for numpy-1.23.3-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 bc6e8da415f359b578b00bcfb1d08411c96e9a97f9e6c7adada554a0812a6cc6
MD5 d0587d5b28d3fa7e0ec8fd3df76e4bd4
BLAKE2b-256 e50ec76c8cd19fa0477742e553471373e69f4aeb228b3b18aaac305a66cd5f5b

See more details on using hashes here.

Release history Release notifications | RSS feed

2.5.2

2.5.1

2.5.0

2.5.0rc1 pre-release

2.4.6

2.4.5

2.4.4

2.4.3

2.4.2

2.4.1

2.4.0 yanked

Reason this release was yanked:

Backward compatibility bug

2.4.0rc1 pre-release

2.3.5

2.3.4

2.3.3

2.3.2

2.3.1

2.3.0

2.2.6

2.2.5

2.2.4

2.2.3

2.2.2

2.2.1

2.2.0

2.1.3

2.1.2

2.1.1

2.1.0

2.0.2

2.0.1

2.0.0

1.26.4

1.26.3

1.26.2

1.26.1

1.26.0

1.25.2

1.25.1

1.25.0

1.24.4

1.24.3

1.24.2

1.24.1

1.24.0

1.23.5

1.23.4

This release

1.23.3

1.23.2

1.23.1

1.23.0

1.22.4

1.22.3

1.22.2

1.22.1

1.22.0

1.21.6

1.21.5

1.21.4

1.21.3

1.21.2

1.21.1

1.21.0

1.20.3

1.20.2

1.20.1

1.20.0

1.19.5

1.19.4

1.19.3

1.19.2

1.19.1

1.19.0

1.18.5

1.18.4

1.18.3

1.18.2

1.18.1

1.18.0

1.17.5

1.17.4

1.17.3

1.17.2

1.17.1

1.17.0

1.16.6

1.16.5

1.16.4

1.16.3

1.16.2

1.16.1

1.16.0

1.15.4

1.15.3

1.15.2

1.15.1

1.15.0

1.14.6

1.14.5

1.14.4

1.14.3

1.14.2

1.14.1

1.14.0

1.13.3

1.13.1

1.13.0

1.12.1

1.12.0

1.11.3

1.11.2

1.11.1

1.11.0

1.10.4

1.10.3

1.10.2

1.10.1

1.10.0

1.9.3

1.9.2

1.9.1

1.9.0

1.8.2

1.8.1

1.8.0

1.7.2

1.7.1

1.7.0

1.6.2

1.6.1

1.6.0

1.5.1

1.5.0

1.4.1

1.4.0

1.3.0

1.2.1

1.2.0

1.1.1

1.0.4

1.0.3

1.0

1.0rc3 pre-release

1.0rc2 pre-release

1.0rc1 pre-release

1.0b5 pre-release

1.0b4 pre-release

1.0b1 pre-release

0.9.8

0.9.6

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page