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, <3.11
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, <3.11
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

This version

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

Download files

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

Source Distribution

numpy-1.21.3.zip (10.3 MB view details)

Uploaded Source

Built Distributions

numpy-1.21.3-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-1.21.3-cp310-cp310-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.10 Windows x86-64

numpy-1.21.3-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-1.21.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.21.3-cp310-cp310-macosx_11_0_arm64.whl (12.4 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

numpy-1.21.3-cp310-cp310-macosx_10_9_universal2.whl (27.2 MB view details)

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

numpy-1.21.3-cp39-cp39-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.21.3-cp39-cp39-win32.whl (11.7 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.21.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.21.3-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-1.21.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ i686

numpy-1.21.3-cp39-cp39-macosx_11_0_arm64.whl (12.4 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.21.3-cp39-cp39-macosx_10_9_x86_64.whl (17.0 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.21.3-cp39-cp39-macosx_10_9_universal2.whl (27.2 MB view details)

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

numpy-1.21.3-cp38-cp38-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.21.3-cp38-cp38-win32.whl (11.7 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.21.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.21.3-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-1.21.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ i686

numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (14.1 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ x86-64

numpy-1.21.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl (12.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ i686

numpy-1.21.3-cp38-cp38-macosx_11_0_arm64.whl (12.3 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

numpy-1.21.3-cp38-cp38-macosx_10_9_x86_64.whl (16.9 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

numpy-1.21.3-cp38-cp38-macosx_10_9_universal2.whl (27.1 MB view details)

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

numpy-1.21.3-cp37-cp37m-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

numpy-1.21.3-cp37-cp37m-win32.whl (11.7 MB view details)

Uploaded CPython 3.7m Windows x86

numpy-1.21.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

numpy-1.21.3-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-1.21.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ i686

numpy-1.21.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (14.1 MB view details)

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

numpy-1.21.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl (12.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.5+ i686

numpy-1.21.3-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-1.21.3.zip.

File metadata

  • Download URL: numpy-1.21.3.zip
  • Upload date:
  • Size: 10.3 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/3.9.7

File hashes

Hashes for numpy-1.21.3.zip
Algorithm Hash digest
SHA256 63571bb7897a584ca3249c86dd01c10bcb5fe4296e3568b2e9c1a55356b6410e
MD5 59d986f5ccf3edfb7d4d14949c6666ed
BLAKE2b-256 5fd6ad58ded26556eaeaa8c971e08b6466f17c4ac4d786cd3d800e26ce59cc01

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 98339aa9911853f131de11010f6dd94c8cec254d3d1f7261528c3b3e3219f139
MD5 8ce925a0fcbc1062985026215d369276
BLAKE2b-256 62de86fe573b19f0f93673c9bda03eac064f36420041856ef8aa03286c859a5f

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 f8f4625536926a155b80ad2bbff44f8cc59e9f2ad14cdda7acf4c135b4dc8ff2
MD5 8ac48f503f1e22c0c2b5d056772aca27
BLAKE2b-256 98ac8c1e6ce4e8c46eb41f6db2f85de64574e33ab63fe0d0dbb4811e133c345d

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 49c6249260890e05b8111ebfc391ed58b3cb4b33e63197b2ec7f776e45330721
MD5 8576bfd867834182269f72abbaa2e81e
BLAKE2b-256 efc7db3cc47b2ef177c75bafe27ce16c83031db9e160c920a7b2f46d56882e54

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8a10968963640e75cc0193e1847616ab4c718e83b6938ae74dea44953950f6b7
MD5 13cfe83efd261ea1c3d1eb02c1d3af83
BLAKE2b-256 9684546f5aad2a6e6b0f30911cebf6f4e0def4496423d9f6eaf0fd99c31c2977

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5dfe9d6a4c39b8b6edd7990091fea4f852888e41919d0e6722fe78dd421db0eb
MD5 a70f80a4e74a3153a8307c4f0ea8d13d
BLAKE2b-256 0d88d65dd8c0c4e779c027928bc0be399beed44b2af256a895d7b3afb99b7f49

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 508b0b513fa1266875524ba8a9ecc27b02ad771fe1704a16314dc1a816a68737
MD5 9acea9630856659ba48fdb582ecc37b4
BLAKE2b-256 e9d60255f13fc4b89618eb514e8a170429d8aad188638633ba2e224ce88dab75

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 300321e3985c968e3ae7fbda187237b225f3ffe6528395a5b7a5407f73cf093e
MD5 b2e1dc59b6fa224ce11728d94be740a6
BLAKE2b-256 fb80373c1516ab27a87a97dcd5d94b7ac48524d09270799266cdba550798ec1a

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.21.3-cp39-cp39-win32.whl
  • Upload date:
  • Size: 11.7 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 dd0482f3fc547f1b1b5d6a8b8e08f63fdc250c58ce688dedd8851e6e26cff0f3
MD5 e0d35451ba1c37f96e032bc6f75ccdf7
BLAKE2b-256 90369de73ffecd31db66af224d1c89c2083728dfc155a27ea0b6663b119b7a18

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 cc14e7519fab2a4ed87d31f99c31a3796e4e1fe63a86ebdd1c5a1ea78ebd5896
MD5 c653a096da47b64b42e8f1536a21f7d4
BLAKE2b-256 94008c85c5b21dad597217ffcfcf35eefa2922f1465dc0b7c6a8fa19bb717edb

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 bffa2eee3b87376cc6b31eee36d05349571c236d1de1175b804b348dc0941e3f
MD5 df7344ae04c5a54249fa1b63a256ce61
BLAKE2b-256 d3b37f13d41fa5c3ddc393cbc9eeb369960e74a77cfb2bb2297103a4fc60321d

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 c6c2d535a7beb1f8790aaa98fd089ceab2e3dd7ca48aca0af7dc60e6ef93ffe1
MD5 5b61a91221931af4a78c3bd20925a91f
BLAKE2b-256 377a621c146eede08371614e0aa1323710a18317be4c2905173072281db2228d

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 28f15209fb535dd4c504a7762d3bc440779b0e37d50ed810ced209e5cea60d96
MD5 ad05d5c412d15e7880cd65cc6cdd4aac
BLAKE2b-256 8d1f12169f78b32e1d8607f20fe6a01180791575ca1ecde2b5ac659307f5d7f8

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5c4193f70f8069550a1788bd0cd3268ab7d3a2b70583dfe3b2e7f421e9aace06
MD5 6f5b02152bd0b08a77b79657788ce59c
BLAKE2b-256 2b1077cca478c3286739e5c799a2138ce52d76234be00cd82398f37c8964e2c9

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 2a6ee9620061b2a722749b391c0d80a0e2ae97290f1b32e28d5a362e21941ee4
MD5 44d6bd26fb910710ab4002d0028c9020
BLAKE2b-256 f444a5e6f7dce2f8f78b4a92bd3b4f84f24ffa6b1e743ebdd386f855af0860e6

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 75eb7cadc8da49302f5b659d40ba4f6d94d5045fbd9569c9d058e77b0514c9e4
MD5 980303a7e6317faf9a56ba8fc80795d9
BLAKE2b-256 5898130f4c2ad489a4283aeac672c9b16a8e382997a1453c0c65c187eb4ad739

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.21.3-cp38-cp38-win32.whl
  • Upload date:
  • Size: 11.7 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 fe52dbe47d9deb69b05084abd4b0df7abb39a3c51957c09f635520abd49b29dd
MD5 889202c6bdaf8c1ae0803925e9e1a8f7
BLAKE2b-256 2c107c45ce81bad1e3e9400fa01d596e7ab417b81bef19bc5e43c80bee7c0db8

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e606e6316911471c8d9b4618e082635cfe98876007556e89ce03d52ff5e8fcf0
MD5 34e6f5f9e9534ef8772f024170c2bd2d
BLAKE2b-256 d430f4c4375cb2b17ddf9f393fa80adb0bdbe574d65a27893c19328691772fe6

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 32437f0b275c1d09d9c3add782516413e98cd7c09e6baf4715cbce781fc29912
MD5 7cb0b7dd6aee667ecdccae1829260186
BLAKE2b-256 61b78d6c93938deef21dabaf8c32af62311fd9e9ec5c82ec6aa78d3afa10d9bb

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 29fb3dcd0468b7715f8ce2c0c2d9bbbaf5ae686334951343a41bd8d155c6ea27
MD5 93ad32cc87866e9242156bdadc61e5f5
BLAKE2b-256 e79d27a017cb6dc6d96eed032e0e5933de3430fae228802fdc82d03fc6f1971e

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
  • Upload date:
  • Size: 14.1 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/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 dde972a1e11bb7b702ed0e447953e7617723760f420decb97305e66fb4afc54f
MD5 260ba58f2dc64e779eac7318ec92f36c
BLAKE2b-256 a5a172d6cacbcd35f5a8286db4b7aa50d7bc26bd467a0308b354db02d34148b0

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.21.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
  • Upload date:
  • Size: 12.4 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/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 a99a6b067e5190ac6d12005a4d85aa6227c5606fa93211f86b1dafb16233e57d
MD5 54e6abfb8f600de2ccd1649b1fca820b
BLAKE2b-256 96f8858715a9e237a0310e114b8eb0855fd539d3c632714029a5c21c139b9e21

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 160ccc1bed3a8371bf0d760971f09bfe80a3e18646620e9ded0ad159d9749baa
MD5 9555dc6de8748958434e8f2feba98494
BLAKE2b-256 084fb5864dddafab6cce97a91f78153a2cb4e01e576ad3b1264ee2131cd78d35

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 188031f833bbb623637e66006cf75e933e00e7231f67e2b45cf8189612bb5dc3
MD5 1bc55202f604e30f338bc2ed27b561bc
BLAKE2b-256 6225f7caa74a9853b9275ebb58b4a2b82608022cf43c92d540412204732ec39e

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 75621882d2230ab77fb6a03d4cbccd2038511491076e7964ef87306623aa5272
MD5 88a2cd378412220d618473dd273baf04
BLAKE2b-256 a504d11249b11bce114e6fbd6596e7c0a60dfac74bbd5fa63e5b55081f304768

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 043e83bfc274649c82a6f09836943e4a4aebe5e33656271c7dbf9621dd58b8ec
MD5 f906001213ed0902b1aecfaa12224e94
BLAKE2b-256 f1efdf6ce4d461fa592ae88bd271c3c944f428a2029840ae63ef3e3cd5ef4fde

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy-1.21.3-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 11.7 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 90bec6a86b348b4559b6482e2b684db4a9a7eed1fa054b86115a48d58fbbf62a
MD5 a093fea475b5ed18bd21b3c79e68e388
BLAKE2b-256 0e0cce9715c6972a44732283a7c71b8957258e4fad3bbd6d8c0bf77a66ae8b35

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4cc9b512e9fb590797474f58b7f6d1f1b654b3a94f4fa8558b48ca8b3cfc97cf
MD5 0aa000f3c10cf74bf47770577384b5c8
BLAKE2b-256 ba83f71a60aa7a6b5b8328da9bfa41ffabf422c9c8ca79218fc57b717c2bcf73

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 50cd26b0cf6664cb3b3dd161ba0a09c9c1343db064e7c69f9f8b551f5104d654
MD5 da54c9566f3e3f8c7d60efebfdf7e1ae
BLAKE2b-256 560e85fb62699fa41d980763df35e05d60e53759c1c554f586962df378a7eae1

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 f41b018f126aac18583956c54544db437f25c7ee4794bcb23eb38bef8e5e192a
MD5 0967b18baba13e511c7eb48902a62b39
BLAKE2b-256 56c335fb70ba69ea7342293b1add78b1dac759ddbbf36a80b8b545a91823032f

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
  • Upload date:
  • Size: 14.1 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/3.9.7

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 3c09418a14471c7ae69ba682e2428cae5b4420a766659605566c0fa6987f6b7e
MD5 89e15d979533f8a314e0ab0648ee7153
BLAKE2b-256 b02786c9c52a335695fabc79514a196abd2d703ff8b089ba3207f069b63115ce

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.21.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
  • Upload date:
  • Size: 12.4 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/3.9.7

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 88a5d6b268e9ad18f3533e184744acdaa2e913b13148160b1152300c949bbb5f
MD5 5683501bf91be25c53c52e3b083098c3
BLAKE2b-256 21935cbfac98e554a0cd86a349a65c224da8500ba8e3bfca7664c6ffd6a5f2a9

See more details on using hashes here.

File details

Details for the file numpy-1.21.3-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.3-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/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.7

File hashes

Hashes for numpy-1.21.3-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e54af82d68ef8255535a6cdb353f55d6b8cf418a83e2be3569243787a4f4866f
MD5 cbe0d0d7623de3c2c7593f673d1a880a
BLAKE2b-256 51ce99671abbe61d3c8468f8381d1af1188ebd5a81478df387b59ab7240cc92b

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