Skip to main content

NumPy is the fundamental package for array computing with Python.

Project description

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.

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-1.21.5.zip (10.7 MB view details)

Uploaded Source

Built Distributions

numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.2 MB view details)

Uploaded PyPy manylinux: glibc 2.12+ x86-64

numpy-1.21.5-cp310-cp310-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.10 Windows x86-64

numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.9 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl (12.4 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl (17.0 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl (27.2 MB view details)

Uploaded CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

numpy-1.21.5-cp39-cp39-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.21.5-cp39-cp39-win32.whl (11.7 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ x86-64

numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ i686

numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl (12.4 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl (17.0 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl (27.2 MB view details)

Uploaded CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

numpy-1.21.5-cp38-cp38-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.21.5-cp38-cp38-win32.whl (11.7 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ i686

numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl (12.3 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl (16.9 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl (27.1 MB view details)

Uploaded CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

numpy-1.21.5-cp37-cp37m-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

numpy-1.21.5-cp37-cp37m-win32.whl (11.7 MB view details)

Uploaded CPython 3.7m Windows x86

numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ i686

numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl (16.9 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file numpy-1.21.5.zip.

File metadata

  • Download URL: numpy-1.21.5.zip
  • 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5.zip
Algorithm Hash digest
SHA256 6a5928bc6241264dce5ed509e66f33676fc97f464e7a919edc672fb5532221ee
MD5 88b5438ded7992fa2e6a810d43cd32a1
BLAKE2b-256 c2a8a924a09492bdfee8c2ec3094d0a13f2799800b4fdc9c890738aeeb12c72e

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 a7c4b701ca418cd39e28ec3b496e6388fe06de83f5f0cb74794fa31cfa384c02
MD5 5be2b6f6cf6fb3a3d98231e891260624
BLAKE2b-256 a0b263edeaba31f648adaa5d52c74d2bc6c4bf8cd4842c3a2a30e53c98fd5c8a

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 14.0 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 cc1b30205d138d1005adb52087ff45708febbef0e420386f58664f984ef56954
MD5 e545f6f85f950f57606efcaeeac2e50a
BLAKE2b-256 2d7956b53c21a2f3933dd786ddf89de210a2d6693244e6cf22e8f6d3697a6eed

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 dc4b2fb01f1b4ddbe2453468ea0719f4dbb1f5caa712c8b21bb3dd1480cd30d9
MD5 8b27b622f58caeeb7f14472651d655e3
BLAKE2b-256 b31c001ee062f215a5a8b44a3e8a80bd6f504a53f55426c3ea37ffdba472e98f

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 58ca1d7c8aef6e996112d0ce873ac9dfa1eaf4a1196b4ff7ff73880a09923ba7
MD5 c5c982a07797c8963b8fec44aae6db09
BLAKE2b-256 3d545c0ed06ca1ddcc3252f0e5244f3d688157882e0cc7525853312c2023144b

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.4 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fc7a7d7b0ed72589fd8b8486b9b42a564f10b8762be8bd4d9df94b807af4a089
MD5 bdbb19e7656d66250aa67bd1c7924764
BLAKE2b-256 97299bd56ffed5e42cda8ad15e71e774615543d3acf0f7041b1f45bfebfd0286

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 17.0 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a7e8f6216f180f3fd4efb73de5d1eaefb5f5a1ee5b645c67333033e39440e63a
MD5 50e0526fa29110fb6033fa8285fba4e1
BLAKE2b-256 b0cd83862fda9dbb7d0e4aa1b51cb00504870243dae0625737b92b05e7679321

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 27.2 MB
  • Tags: CPython 3.10, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 301e408a052fdcda5cdcf03021ebafc3c6ea093021bf9d1aa47c54d48bdad166
MD5 e00a3c2e1461dd2920ab4af6b753d3da
BLAKE2b-256 cacff2a8e3a1fd2ef3ed31e89913720031d586d0f0fd7620b74af64a561774e2

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 14.0 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 1964db2d4a00348b7a60ee9d013c8cb0c566644a589eaa80995126eac3b99ced
MD5 bbc11e31406a9fc48c18a41259bc8866
BLAKE2b-256 afdffe31e3518f7f6a90a387a2f643344a8cde8cbf84767a5a9a404ff2638dcd

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.21.5-cp39-cp39-win32.whl
  • Upload date:
  • Size: 11.7 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 2a9add27d7fc0fdb572abc3b2486eb3b1395da71e0254c5552b2aad2a18b5441
MD5 c6a44c90c2d5124fea6cedbbf575e252
BLAKE2b-256 e95c020b8ba1b46dd01f814864ccb6d885e325a237a187c916c1d3ebac00e953

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 3c978544be9e04ed12016dd295a74283773149b48f507d69b36f91aa90a643e5
MD5 09f202576cbd0ed6121cff10cdea831a
BLAKE2b-256 1a2ecb80429942b6170712a0b4a267e0f1b3636a801957d40bf3c0840ba5f8ee

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c293d3c0321996cd8ffe84215ffe5d269fd9d1d12c6f4ffe2b597a7c30d3e593
MD5 035bde3955ae2f62ada65084d71a7421
BLAKE2b-256 f8cc038b8277fe977ae1f18d11660386af6093547d6c0bd9a9fadbed4795091a

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.9, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 2d8adfca843bc46ac199a4645233f13abf2011a0b2f4affc5c37cd552626f27b
MD5 6801263f51d3b13420b59ff84c716869
BLAKE2b-256 6cbd46a9fe05e8dd7b4e73b204707124ed847b9eaa8fe66f4dd70ed11ac42440

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.4 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 69a5a8d71c308d7ef33ef72371c2388a90e3495dbb7993430e674006f94797d5
MD5 1dd09ad75eff93b274f650871e0b9287
BLAKE2b-256 2315947caaf68f10166843ad1dbcc8097522f3b1a6d986ff4a4d0063bb369d3a

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 17.0 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 00c9fa73a6989895b8815d98300a20ac993c49ac36c8277e8ffeaa3631c0dbbb
MD5 220dd07273aeb0b2ca8f0e4f543e43c3
BLAKE2b-256 51e3231e70770312ae79fae0ea1d5be42c1ad656dc8f6e4d9bffd5b7e40927fa

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 27.2 MB
  • Tags: CPython 3.9, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 507c05c7a37b3683eb08a3ff993bd1ee1e6c752f77c2f275260533b265ecdb6c
MD5 b16dd7103117d051cb6c3b6c4434f7d2
BLAKE2b-256 1316268e725e895cbb1c9c06bd9ae03eb01cc6e3d876d958b264a6c3042ad8ce

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 14.0 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 dbce7adeb66b895c6aaa1fad796aaefc299ced597f6fbd9ceddb0dd735245354
MD5 c2e0744164f8255be70725ef42bc3f5b
BLAKE2b-256 c87afc93f1989d009bd1b40d38efc82f1b262deea8842da6435ff90ec86c0ace

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.21.5-cp38-cp38-win32.whl
  • Upload date:
  • Size: 11.7 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 7b9d6b14fc9a4864b08d1ba57d732b248f0e482c7b2ff55c313137e3ed4d8449
MD5 0c4c5336136e045d02c60ba8115eb6a2
BLAKE2b-256 ca3c0449cae682cdae69f3261fad5871d6d54f3a45cd5b3042b8ddfc959e2224

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 61bada43d494515d5b122f4532af226fdb5ee08fe5b5918b111279843dc6836a
MD5 50432f9cf1d5b2278ceb7a96890353ed
BLAKE2b-256 98c9ec6b0c69b5f65e946036af67cb2a316712e60a79892e27988ef7a248be16

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 eed2afaa97ec33b4411995be12f8bdb95c87984eaa28d76cf628970c8a2d689a
MD5 703c0f54c5ede8cc0c648ef66cafac47
BLAKE2b-256 9cacbd195e349dd5cacd6b8bbae2100c43017a57b27dbd0c6d99471c8f16a41d

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 c5562bcc1a9b61960fc8950ade44d00e3de28f891af0acc96307c73613d18f6e
MD5 52de43977749109509ee708a142a7d97
BLAKE2b-256 cd68e1465ec9d9d095e707cd21987ea0a77ff88d060efe9d9d7a1972e4bb51ac

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.3 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b545ebadaa2b878c8630e5bcdb97fc4096e779f335fc0f943547c1c91540c815
MD5 2169fb8ed40046e1e33d187fc85b91bb
BLAKE2b-256 499d04efd8718a72913bda0edf3429b788eeaf20f8c60c70e1d9fc1a5413d8bb

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.9 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 311283acf880cfcc20369201bd75da907909afc4666966c7895cbed6f9d2c640
MD5 4721e71bdc5697d310cd3a6b6cd60741
BLAKE2b-256 e8b2be38f1036c7e7f6af79201f09de86615599beeb232f37dd288d9631d9b29

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 27.1 MB
  • Tags: CPython 3.8, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 3a5098df115340fb17fc93867317a947e1dcd978c3888c5ddb118366095851f8
MD5 843e3431ba4b56d3fc36b7c4cb6fc10c
BLAKE2b-256 97caa82e530d1857782b82de5160a5635c7b57063ea9c5d22531d210a206d058

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 14.0 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 025b497014bc33fc23897859350f284323f32a2fff7654697f5a5fc2a19e9939
MD5 f55c7ecfd35769fb3f6a408c0c123372
BLAKE2b-256 22a33a5469ebaca59100e50b4300dd011eed943f2aad7c6a80a07966b985e2c6

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy-1.21.5-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 11.7 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 ca9c23848292c6fe0a19d212790e62f398fd9609aaa838859be8459bfbe558aa
MD5 b7498a1d0ea7273ef1af56d58e02a550
BLAKE2b-256 b446a3a0d9b40fd00e1adec8e0e51da09186b1764b86cadfc9275048f67d6e61

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 341dddcfe3b7b6427a28a27baa59af5ad51baa59bfec3264f1ab287aa3b30b13
MD5 d5ab050300748f20cdc9c6e17ba8ffd4
BLAKE2b-256 5bb50c9779b54542f01a376388c30359d20ce2947169273e2b77eab193ffdf80

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 3d893b0871322eaa2f8c7072cdb552d8e2b27645b7875a70833c31e9274d4611
MD5 ec1a9a1333a2bf61897f105ecd9f212a
BLAKE2b-256 5046292cff79f5b30151b027400efdb3f740ea03271b600751b6696cf550c10d

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 4fe6a006557b87b352c04596a6e3f12a57d6e5f401d804947bd3188e6b0e0e76
MD5 b5d080e0fd8b658419b3636f1cf5dc3a
BLAKE2b-256 feeaa131a39cad03e2ecd409e0d1953e226ea01664020664017219f1eada3fba

See more details on using hashes here.

File details

Details for the file numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.9 MB
  • Tags: CPython 3.7m, 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/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 08de8472d9f7571f9d51b27b75e827f5296295fa78817032e84464be8bb905bc
MD5 5c36eefdcb039c0d4db8882fddbeb695
BLAKE2b-256 098cae037b8643aaa405b666c167f48550c1ce6b7c589fe5540de6d83e5931ca

See more details on using hashes here.

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