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-1.21.0.zip (10.3 MB view details)

Uploaded Source

Built Distributions

numpy-1.21.0-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.1 MB view details)

Uploaded PyPy manylinux: glibc 2.12+ x86-64

numpy-1.21.0-cp39-cp39-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.21.0-cp39-cp39-win32.whl (11.6 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.21.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.21.0-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-1.21.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ i686

numpy-1.21.0-cp39-cp39-macosx_11_0_arm64.whl (12.1 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.21.0-cp39-cp39-macosx_10_9_x86_64.whl (16.9 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

numpy-1.21.0-cp39-cp39-macosx_10_9_universal2.whl (26.9 MB view details)

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

numpy-1.21.0-cp38-cp38-win_amd64.whl (14.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

numpy-1.21.0-cp38-cp38-win32.whl (11.7 MB view details)

Uploaded CPython 3.8 Windows x86

numpy-1.21.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (13.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.21.0-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-1.21.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ i686

numpy-1.21.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (14.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ x86-64

numpy-1.21.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl (12.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ i686

numpy-1.21.0-cp38-cp38-macosx_11_0_arm64.whl (12.1 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

numpy-1.21.0-cp38-cp38-macosx_10_9_x86_64.whl (16.9 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

numpy-1.21.0-cp38-cp38-macosx_10_9_universal2.whl (26.8 MB view details)

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

numpy-1.21.0-cp37-cp37m-win_amd64.whl (13.9 MB view details)

Uploaded CPython 3.7m Windows x86-64

numpy-1.21.0-cp37-cp37m-win32.whl (11.6 MB view details)

Uploaded CPython 3.7m Windows x86

numpy-1.21.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (12.9 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

numpy-1.21.0-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-1.21.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl (13.7 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ i686

numpy-1.21.0-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-1.21.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl (12.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.5+ i686

numpy-1.21.0-cp37-cp37m-macosx_10_9_x86_64.whl (16.8 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file numpy-1.21.0.zip.

File metadata

  • Download URL: numpy-1.21.0.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/3.9.5

File hashes

Hashes for numpy-1.21.0.zip
Algorithm Hash digest
SHA256 e80fe25cba41c124d04c662f33f6364909b985f2eb5998aaa5ae4b9587242cce
MD5 930ebfdffd10fed701a7823691f02983
BLAKE2b-256 6603818876390c7ff4484d5a05398a618cfdaf0a2b9abb3a7c7ccd59fe181008

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 3c40e6b860220ed862e8097b8f81c9af6d7405b723f4a7af24a267b46f90e461
MD5 42d05fcbab6137a404be36f27fc254f0
BLAKE2b-256 19078af06839b07a7ce857e1d859bc88a5a04b59c8c71e81b2e191d2b1a66947

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2ba579dde0563f47021dcd652253103d6fd66165b18011dce1a0609215b2791e
MD5 29d1bf596981d930bb1c95c944b4b3d8
BLAKE2b-256 a5893dbb820397d01ad3e8eb4aee6b6df97ecef46701e72d01d33d69e11fffde

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.21.0-cp39-cp39-win32.whl
  • Upload date:
  • Size: 11.6 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 d95d16204cd51ff1a1c8d5f9958ce90ae190be81d348b514f9be39f878b8044a
MD5 e2287cd16300b363d376b661646fded9
BLAKE2b-256 4bd7d38020964f35fe2f06dfc08ba228c3a1ce362f4c98f948cda308579d921a

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 cf680682ad0a3bef56dae200dbcbac2d57294a73e5b0f9864955e7dd7c2c2491
MD5 a627acdfcd302807cf8592d5bd958d35
BLAKE2b-256 5908ee06f745a737d4bfc32ebbe6fd67305dfacf437c1361ba94b19dc18dadd3

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 bebab3eaf0641bba26039fb0b2c5bf9b99407924b53b1ea86e03c32c64ef5aef
MD5 c10e13fef152ed1c64151c8b6f6d0799
BLAKE2b-256 3356f21ff9325b9c5b42e007f58e46e2eb20ba9d02c92788460ac7e21602ff79

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 709884863def34d72b183d074d8ba5cfe042bc3ff8898f1ffad0209161caaa99
MD5 e49cd2db6ec712b8b1d516154b5a034a
BLAKE2b-256 36d27def785434e4113fd20c43504cbe516f5e86eeba5c0d7aeef842374f7cf1

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.0-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.1 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ccc6c650f8700ce1e3a77668bb7c43e45c20ac06ae00d22bdf6760b38958c883
MD5 31cf2152b4151912be9d165633a7d8eb
BLAKE2b-256 1f1e02ba9cddc4447123597714fb74c1f37bb5127338d8aa99a4b60f15833552

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.0-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.9 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3537b967b350ad17633b35c2f4b1a1bbd258c018910b518c30b48c8e41272717
MD5 96d7d3a438296bfc68b819b3624936a5
BLAKE2b-256 bf3a8c42772b0ba18e603b4b9131925bcd05e5f111d6f640c9b62331654ba881

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.0-cp39-cp39-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 26.9 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 a290989cd671cd0605e9c91a70e6df660f73ae87484218e8285c6522d29f6e38
MD5 c6e9fa30e82e3ca1551d2f048d4a1dc4
BLAKE2b-256 466e241ab0c4b69c44ae7e466384cb4499b238b23492d7b47b8f35065525ef68

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 d2910d0a075caed95de1a605df00ee03b599de5419d0b95d55342e9a33ad1fb3
MD5 5c8c3e94f5a55123b1a0d3a4df14b505
BLAKE2b-256 df22b74e5cedeef1e3f108c986bd0b75600997d8b25def334a68f08d372db523

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 e58ddb53a7b4959932f5582ac455ff90dcb05fac3f8dcc8079498d43afbbde6c
MD5 0b39eb396a1d5983f6eb2075a867a1a6
BLAKE2b-256 ad64c2ba261eaef66d4c3640340b556c1f26f4367b6a28a1818c61713db68c65

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b9205711e5440954f861ceeea8f1b415d7dd15214add2e878b4d1cf2bcb1a914
MD5 baf409eb08b7462899d45c42a7c1d854
BLAKE2b-256 d8aded9494e945bb227346ffdf25171e3f7ae8a81cf832538bbd1bc43547b13c

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 620732f42259eb2c4642761bd324462a01cdd13dd111740ce3d344992dd8492f
MD5 3e60589e3325a3583880bf6998cfaca6
BLAKE2b-256 bba607cf2c2d26d181329daf00e606e74d5452957b90ad086db48d2e56e2d4f6

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 e4d5a86a5257843a18fb1220c5f1c199532bc5d24e849ed4b0289fb59fbd4d8f
MD5 b81545a2924a201817d433c3bad0bc7d
BLAKE2b-256 4741cf9e5deb8410a19d6b18c882a35dd798607cbcd819f092c097a6d11ad5fb

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
  • Upload date:
  • Size: 14.0 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a1f2fb2da242568af0271455b89aee0f71e4e032086ee2b4c5098945d0e11cf6
MD5 1a79926ad8d3dda573f5c2d8d06e0e38
BLAKE2b-256 97843aeca9d6363e017557d1f1e753517b79700e235acae0be797d9f5a1273dc

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 ad09f55cc95ed8d80d8ab2052f78cc21cb231764de73e229140d81ff49d8145e
MD5 4f311de7973503dde6ad3915f158fd63
BLAKE2b-256 a6abd41ef8d78c85aca2c6071d5ac2c8c3db493d77a2f38fc987377a6359e29e

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: numpy-1.21.0-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 12.1 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fd0a359c1c17f00cb37de2969984a74320970e0ceef4808c32e00773b06649d9
MD5 5faa22dffa53cfe7d1d40d48aa817670
BLAKE2b-256 fc65bb9f27c197177e5e8e75b3cda6eca1ce2e37459479dbad548d4ad82bf3f7

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 bba474a87496d96e61461f7306fba2ebba127bed7836212c360f144d1e72ac54
MD5 9589cfe5a22f54956101b7131be5cabd
BLAKE2b-256 b162f68683f8954e94ae89f82045efa4c4e307fd1b9ea4e9d5dcfdba5908beb1

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

  • Download URL: numpy-1.21.0-cp38-cp38-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 26.8 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 1a784e8ff7ea2a32e393cc53eb0003eca1597c7ca628227e34ce34eb11645a0e
MD5 e6d77cae6054b738603415faf9cb4358
BLAKE2b-256 c92b4fb9a344fd4f71eb6d14fa699f9bb22fa1684db4971285ae3ec640dc4f84

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.21.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 13.9 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 eda2829af498946c59d8585a9fd74da3f810866e05f8df03a86f70079c7531dd
MD5 6d771c7670b95adb62627e383c883804
BLAKE2b-256 412503a646a3430554649a8fc71df35f42b8a67f2f6b3ef4793c7c08f5b8271a

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy-1.21.0-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 11.6 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 d89b0dc7f005090e32bb4f9bf796e1dcca6b52243caf1803fdd2b748d8561f63
MD5 aa9f94fa6eabfa193902676825934196
BLAKE2b-256 4087a92af48c7ccca06bda513da2e06d32ae542d55900bca776e012329e60495

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7c55407f739f0bfcec67d0df49103f9333edc870061358ac8a8c9e37ea02fcd2
MD5 82e267da77628b96cdf8832e475f6ef3
BLAKE2b-256 bbdd278e3d0565bcc0bf6ed90bab14da37d47bbd651450ec7910863c5a17a139

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 598fe100b2948465cf3ed64b1a326424b5e4be2670552066e17dfaa67246011d
MD5 e2fc116043d1b91c627f3c8884151f33
BLAKE2b-256 3f03c3526fb4e79a793498829ca570f2f868204ad9a8040afcd72d82a8f121db

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 ac4fd578322842dbda8d968e3962e9f22e862b6ec6e3378e7415625915e2da4d
MD5 111e09f3fddd8e14540cf56493dd786a
BLAKE2b-256 ea1bbd4dfef72b9a892b7cdfa463a04ec92899889932e0ba517084393aa8cf46

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 cc367c86eb87e5b7c9592935620f22d13b090c609f1b27e49600cd033b529f54
MD5 aba24836f51bb0a855434c41de122e3d
BLAKE2b-256 2cd28973eb282fc3c7e6c4db0469f0390d81d8eb9ae56dfaa2a7e6db07283682

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.21.0-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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 75579acbadbf74e3afd1153da6177f846212ea2a0cc77de53523ae02c9256513
MD5 baa416fe77b840a19556f5d808eb3165
BLAKE2b-256 6e4fedaf17171a1c6356966b71096f4137efc3b372d8aeb9fa6ca200d441c46f

See more details on using hashes here.

File details

Details for the file numpy-1.21.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: numpy-1.21.0-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.8 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/3.9.5

File hashes

Hashes for numpy-1.21.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d5caa946a9f55511e76446e170bdad1d12d6b54e17a2afe7b189112ed4412bb8
MD5 e4b31fd5cb97e50238b3dbb3487b2cb7
BLAKE2b-256 4c39d81f946b01bfe81fcaf8e788cb5b21934c583b74ec3423a32e4916d820ad

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