NumPy is the fundamental package for array computing with Python.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for matthew.brett from gravatar.com matthew.brett Avatar for mattip from gravatar.com mattip Avatar for rgommers from gravatar.com rgommers Avatar for teoliphant from gravatar.com teoliphant

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: BSD License (BSD)
  • Author: Travis E. Oliphant et al.
  • Maintainer: NumPy Developers
  • Requires: Python >=3.7
Classifiers

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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for matthew.brett from gravatar.com matthew.brett Avatar for mattip from gravatar.com mattip Avatar for rgommers from gravatar.com rgommers Avatar for teoliphant from gravatar.com teoliphant

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: BSD License (BSD)
  • Author: Travis E. Oliphant et al.
  • Maintainer: NumPy Developers
  • Requires: Python >=3.7
Classifiers

Release history Release notifications | RSS feed

2.1.3

2.1.2

2.1.1

2.1.0

2.1.0rc1 pre-release

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

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

This version

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

Download files

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

Source Distribution

numpy-1.20.3.zip (7.8 MB view details)

Uploaded Source

Built Distributions

numpy-1.20.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (14.7 MB view details)

Uploaded PyPy manylinux: glibc 2.12+ x86-64

numpy-1.20.3-cp39-cp39-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.20.3-cp39-cp39-win32.whl (11.4 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.20.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (12.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ x86-64

numpy-1.20.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl (13.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ i686

numpy-1.20.3-cp39-cp39-macosx_10_9_x86_64.whl (16.1 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.20.3-cp38-cp38-win_amd64.whl (13.7 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.20.3-cp38-cp38-win32.whl (11.4 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.20.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (12.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.20.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

numpy-1.20.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl (13.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ i686

numpy-1.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (13.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ x86-64

numpy-1.20.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl (12.1 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ i686

numpy-1.20.3-cp38-cp38-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

numpy-1.20.3-cp37-cp37m-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.7m Windows x86-64

numpy-1.20.3-cp37-cp37m-win32.whl (11.3 MB view details)

Uploaded CPython 3.7m Windows x86

numpy-1.20.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (12.7 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.3 MB view details)

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

numpy-1.20.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl (13.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ i686

numpy-1.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.5+ x86-64

numpy-1.20.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl (12.1 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.5+ i686

numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl (16.0 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file numpy-1.20.3.zip.

File metadata

  • Download URL: numpy-1.20.3.zip
  • Upload date:
  • Size: 7.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3.zip
Algorithm Hash digest
SHA256 e55185e51b18d788e49fe8305fd73ef4470596b33fc2c1ceb304566b99c71a69
MD5 949d9114af9accc25ede1daa359c4227
BLAKE2b-256 f31ffe9459e39335e7d0e372b5e5dcd60f4381d3d1b42f0b9c8222102ff29ded

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 4e465afc3b96dbc80cf4a5273e5e2b1e3451286361b4af70ce1adb2984d392f9
MD5 6abc979843929b41b099e4e6c0253063
BLAKE2b-256 09eb614d096c99434689671684e020e15dafee6a7f5b003fff1853c2c6b0d022

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 6690080810f77485667bfbff4f69d717c3be25e5b11bb2073e76bb3f578d99b4
MD5 59f1dba95dedc7a1bebc58ee7e7a945a
BLAKE2b-256 9a1368830a20f816c62c6123dc2b11d8f39df6be33e01fc974b8694ece7ceb04

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.20.3-cp39-cp39-win32.whl
  • Upload date:
  • Size: 11.4 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 16f221035e8bd19b9dc9a57159e38d2dd060b48e93e1d843c49cb370b0f415fd
MD5 85e575735877094f3a76106e9d2a9cac
BLAKE2b-256 c590d0159d9aed98fa9ac2536de354d7d3b0f0e80d57d606ee294efa70f1e015

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a9c65473ebc342715cb2d7926ff1e202c26376c0dcaaee85a1fd4b8d8c1d3b2f
MD5 cdef3fb002bb5e3036f056ea0528c804
BLAKE2b-256 3939eddd9945f336041bb6e21c6aa867101a51ef6b285401fa67aa4e4d87dcdc

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 5d050e1e4bc9ddb8656d7b4f414557720ddcca23a5b88dd7cff65e847864c400
MD5 1d1451f9a5a2eeef666fc512a101a6ca
BLAKE2b-256 cc7f0d414a6826b626c6fea98d98336e1f62cf9dd387c776da8dc83ce2a082b6

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.20.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.9, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 55b745fca0a5ab738647d0e4db099bd0a23279c32b31a783ad2ccea729e632df
MD5 319300952bd42455cb2ad98188c74b5f
BLAKE2b-256 ce37403bddc60f0d9bbfdd437c816c32f65abd044660dc34b8b8f365b4a52298

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.1 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 830b044f4e64a76ba71448fce6e604c0fc47a0e54d8f6467be23749ac2cbd2fb
MD5 18efbadcb513054c765f826fc3bb1645
BLAKE2b-256 f3b7918baf7c42621e479921f8bc6b274157f295edce79d467e5fdb8f53d1d5a

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 1676b0a292dd3c99e49305a16d7a9f42a4ab60ec522eac0d3dd20cdf362ac010
MD5 58c12a54d1b5bc14d36ed2b0d8617fef
BLAKE2b-256 2eed3f3f6a1a8eac1f5e11c87dd19a633043660c72453f55012f58a15a011cdf

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.20.3-cp38-cp38-win32.whl
  • Upload date:
  • Size: 11.4 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 f39a995e47cb8649673cfa0579fbdd1cdd33ea497d1728a6cb194d6252268e48
MD5 e7ffa27f1c75cf11529d90967fa15bbc
BLAKE2b-256 d075f488637799ce2bfea661a0b723e2b133c750e720cdec7949133b8dade4ec

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c1c09247ccea742525bdb5f4b5ceeacb34f95731647fe55774aa36557dbb5fa4
MD5 ff69ad241598607fdfea24155625a6e3
BLAKE2b-256 22bc167ee56ac14f314544f393009b2cc62250c6e8d4d048d10304c8124b374b

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e515c9a93aebe27166ec9593411c58494fa98e5fcc219e47260d9ab8a1cc7f9f
MD5 c9411ef729b8ebe9ed3b8e9dee3da4ac
BLAKE2b-256 a39d5cc11fb882e628d21f8d490f56610f2e6612c9f55ecb25b2b57d4729c5bc

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.20.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 6e51534e78d14b4a009a062641f465cfaba4fdcb046c3ac0b1f61dd97c861b1b
MD5 c651fdb4829703e164bc78613c1a90a8
BLAKE2b-256 72c2c301c9e4837d1832d1d181968e3213266c2b79c231d8decabcbafea39be2

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.8, manylinux: glibc 2.5+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 ea9cff01e75a956dbee133fa8e5b68f2f92175233de2f88de3a682dd94deda65
MD5 d144fdfe141442a7f362d498bc9a40c2
BLAKE2b-256 b14f73eb767af6a93f4dd1d4770d12d98a30114a9931b1071331a9278030e864

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.20.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
  • Upload date:
  • Size: 12.1 MB
  • Tags: CPython 3.8, manylinux: glibc 2.5+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 66fbc6fed94a13b9801fb70b96ff30605ab0a123e775a5e7a26938b717c5d71a
MD5 9fd8d44d8a5f19e434e9dfb7738d954f
BLAKE2b-256 eee76786f5b6f1e2eba354cd19777cc04726d8d7f43d64f6e463a404bb33110b

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.0 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f1452578d0516283c87608a5a5548b0cdde15b99650efdfd85182102ef7a7c17
MD5 445da50ae14b3318170ccf996baca72c
BLAKE2b-256 f7c347a197baf6d0a634a2850b30eec420eb8d9fc02b4f95f685a5763601d675

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 43909c8bb289c382170e0282158a38cf306a8ad2ff6dfadc447e90f9961bef43
MD5 c7d3ae93743d6c0ea2c9dfcad1d42cb4
BLAKE2b-256 cede0ed39fd77c5584cd9e44b4305ee4444ea7af1b38d4d71734ae684fc14184

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy-1.20.3-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 11.3 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 67d44acb72c31a97a3d5d33d103ab06d8ac20770e1c5ad81bdb3f0c086a56cf6
MD5 0f6a36724d5477c8fca6c34e73683db6
BLAKE2b-256 775f0335a2d53bc5f38bbbc0a5e1f3366bea1bc7eef3a99f479392633bf1dc71

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 db250fd3e90117e0312b611574cd1b3f78bec046783195075cbd7ba9c3d73f16
MD5 126b1a5d46cc7d9b9b426f56d075a1e0
BLAKE2b-256 a356a2f001be422a0012974abf780c29c63d7e29cf238e5a4dd1a9e73856729e

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c5bf0e132acf7557fc9bb8ded8b53bbbbea8892f3c9a1738205878ca9434206a
MD5 d1b42dd67dc228088cf822eaab86d424
BLAKE2b-256 a542560d269f604d3e186a57c21a363e77e199358d054884e61b73e405dd217c

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 6ca2b85a5997dabc38301a22ee43c82adcb53ff660b89ee88dded6b33687e1d8
MD5 3d0284b39b20c243b74f6690ad5ae27f
BLAKE2b-256 c8763604f6b1389ad450caa5dbd1ff0b61627cd1f1e1bb61f7633473475067dc

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.5+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 8b7bb4b9280da3b2856cb1fc425932f46fba609819ee1c62256f61799e6a51d2
MD5 02bd4a2c764882e8af353c16344cb633
BLAKE2b-256 ac16c219bb25f862e9b82ad352e55bb70c97a3b43fda2eb40541cc1c38fbcf5f

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.20.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
  • Upload date:
  • Size: 12.1 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.5+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 637d827248f447e63585ca3f4a7d2dfaa882e094df6cfa177cc9cf9cd6cdf6d2
MD5 5b0445346f08b610025dbd2064d4b482
BLAKE2b-256 b073fa7d49858ace10a526af75092a3b710621e8f0caa23822f2bebaa435da94

See more details on using hashes here.

File details

Details for the file numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.0 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 70eb5808127284c4e5c9e836208e09d685a7978b6a216db85960b1a112eeace8
MD5 702d0185042f1ff9a5d7e72a29f4e1c0
BLAKE2b-256 b650ecda32e07ec70235a828dcd8ec32395ef7772120ccbe5a73df9cc3db1090

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