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-1.22.1.zip (11.4 MB view details)

Uploaded Source

Built Distributions

numpy-1.22.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

numpy-1.22.1-cp310-cp310-win_amd64.whl (14.7 MB view details)

Uploaded CPython 3.10 Windows x86-64

numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

numpy-1.22.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl (12.8 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl (17.7 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl (27.8 MB view details)

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

numpy-1.22.1-cp39-cp39-win_amd64.whl (14.7 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.22.1-cp39-cp39-win32.whl (12.2 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.22.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

numpy-1.22.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl (12.8 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl (17.7 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl (27.8 MB view details)

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

numpy-1.22.1-cp38-cp38-win_amd64.whl (14.7 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.22.1-cp38-cp38-win32.whl (12.2 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

numpy-1.22.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl (12.7 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl (17.6 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl (27.7 MB view details)

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

File details

Details for the file numpy-1.22.1.zip.

File metadata

  • Download URL: numpy-1.22.1.zip
  • Upload date:
  • Size: 11.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1.zip
Algorithm Hash digest
SHA256 e348ccf5bc5235fc405ab19d53bec215bb373300e5523c7b476cc0da8a5e9973
MD5 c25dad73053350dd0278605d8ed8a5c7
BLAKE2b-256 0ac8a62767a6b374a0dfb02d2a0456e5f56a372cdd1689dbc6ffb6bf1ddedbc0

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e60ef82c358ded965fdd3132b5738eade055f48067ac8a5a8ac75acc00cad31f
MD5 74cb5dba2f37dc445ffd3068eb1d58fe
BLAKE2b-256 781138124ae301ba19678ae7b77e15545acdecc0597fa271daa70fad8de127f5

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 14.7 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 7e957ca8112c689b728037cea9c9567c27cf912741fabda9efc2c7d33d29dfa1
MD5 1a8359c6436d1bcfe84a094337903a48
BLAKE2b-256 62c0a1953bfd8c5ad4531fcb14c63c5022b75a9285cf8687aa4c711bc7b6dbc0

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 45a7dfbf9ed8d68fd39763940591db7637cf8817c5bce1a44f7b56c97cbe211e
MD5 8d40c6fd64389c05646b5ef95cded6e5
BLAKE2b-256 29f03f4e8289ae04033f21f1ded5e00e2a0fa8e70ffbfb05b77196a4fb844c61

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 69958735d5e01f7b38226a6c6e7187d72b7e4d42b6b496aca5860b611ca0c193
MD5 2ddc25b9c9d7b517610689055f9f553a
BLAKE2b-256 d6eca8b5f1b6d00bc4fd1bc91043d5dfb029536ec5c7769588d3f4c982240008

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.8 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8d1563060e77096367952fb44fca595f2b2f477156de389ce7c0ade3aef29e21
MD5 96f4fc3f321625278ca3807c7c8c789c
BLAKE2b-256 b5babe72b012495f1fc0b5aead70f01a826c9f641dbfce51afd93c12ed4bf1a0

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 17.7 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 831f2df87bd3afdfc77829bc94bd997a7c212663889d56518359c827d7113b1f
MD5 e4858aafd41cdba76cd14161bfc512c3
BLAKE2b-256 4cf3f41548d402da52b1eed72ee1b919037589b87e0e22338ae85587da769b40

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 27.8 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 3d62d6b0870b53799204515145935608cdeb4cebb95a26800b6750e48884cc5b
MD5 8edd68c8998cb694e244ce793b2d088c
BLAKE2b-256 b4858097082c4794d854e40f84639c83e33e516431faaeb9cecba39eba6921d5

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.22.1-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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 4ac4d7c9f8ea2a79d721ebfcce81705fc3cd61a10b731354f1049eb8c99521e8
MD5 eb9a0655d16897f0adf6ea53b9f3bda4
BLAKE2b-256 40edc9d2760a6ca8e76984766221cb68441e83fc590c640aeb4140c22ead488e

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.22.1-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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 b5ec9a5eaf391761c61fd873363ef3560a3614e9b4ead17347e4deda4358bca4
MD5 c5059bd82d8f2c509c889fba09251307
BLAKE2b-256 8c0264be9469418b4ba5ae59667e0a583a15e7fc3e2be25eedd88f3af4d8a672

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 88d54b7b516f0ca38a69590557814de2dd638d7d4ed04864826acaac5ebb8f01
MD5 48e2d2905822f78a96d400c78bd16cbb
BLAKE2b-256 a3d0c58e64e9faf58aa91967a11acec2793acf831285ff5aec897ecf3a6c4054

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c51124df17f012c3b757380782ae46eee85213a3215e51477e559739f57d9bf6
MD5 58d8dc02dd884898c1b7ee1bee1dd216
BLAKE2b-256 56caacad3b681867c97b32d01c67e66595216c6b5947d954e2d829b1583ac380

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.8 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 78bfbdf809fc236490e7e65715bbd98377b122f329457fffde206299e163e7f3
MD5 463b365c80efffd807194c78b4796235
BLAKE2b-256 cb655921a13f712392a7e0730c9ed11c736c2a874c8b356c964d05745c671d48

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 17.7 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 bcd19dab43b852b03868796f533b5f5561e6c0e3048415e675bec8d2e9d286c1
MD5 1458d42b26da341baaee134d85e3fd70
BLAKE2b-256 e9703649a82928e54dbc4f922e45d44845b9188109ef84cd0a2322c5e7fae628

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 27.8 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 2db01d9838a497ba2aa9a87515aeaf458f42351d72d4e7f3b8ddbd1eba9479f2
MD5 067e734594c67d8141190b7eabb979ee
BLAKE2b-256 1b34551604bc2d7b026f8c80f4f87f73302ef266e4fbe6dae6340ee638f9b4d4

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.22.1-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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 60f19c61b589d44fbbab8ff126640ae712e163299c2dd422bfe4edc7ec51aa9b
MD5 15557a847a78bcbf651ca6689ae37935
BLAKE2b-256 e9f86b9630c0a2d30f1789043893362549e3ea8e30a50febc9023153a91ef42b

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.22.1-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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 f8ad59e6e341f38266f1549c7c2ec70ea0e3d1effb62a44e5c3dba41c55f0187
MD5 defe48b3b5f44c3991e830f7cde0a79c
BLAKE2b-256 c25f4632d3e0d870149949e428652726a89d1e8748498452e02b88671ec14274

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 26b4018a19d2ad9606ce9089f3d52206a41b23de5dfe8dc947d2ec49ce45d015
MD5 200c0a7bc3a24cfa6f4358d7274b5535
BLAKE2b-256 c1fd4648328a72f44fc9a463236448ad33a0674b0fc9045814b31b642f72b9e8

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.22.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0d245a2bf79188d3f361137608c3cd12ed79076badd743dc660750a9f3074f7c
MD5 546b2a0866561673d5b7eadcc086af24
BLAKE2b-256 3458a5e910ad22a701d855396233ccef2642ff1c91ffc3a6cd9f10e6bd8218fe

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.7 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 632e062569b0fe05654b15ef0e91a53c0a95d08ffe698b66f6ba0f927ad267c2
MD5 3ce885a0c10e95f5756d7c1878eaa246
BLAKE2b-256 63e38f16e19787975933d0f350bdd472da935178fef7dc165385a1a06e82695c

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 17.6 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 65f5e257987601fdfc63f1d02fca4d1c44a2b85b802f03bd6abc2b0b14648dd2
MD5 59e13abecdf4194f75b654f1d853b244
BLAKE2b-256 5d4f59aaa65b1e2d7bb75600a2c69a59436014f81c83bec50f597151acb7a1f7

See more details on using hashes here.

File details

Details for the file numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 27.7 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/2.27.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 800dfeaffb2219d49377da1371d710d7952c9533b57f3d51b15e61c4269a1b5b
MD5 033f9aa72a732646f3fb4563226320ee
BLAKE2b-256 c81f7b07ce261b6f0d6859c5ce75be338c78f6960ee55d9d04cd676abaa104c6

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