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-1.16.4.zip (5.1 MB view details)

Uploaded Source

Built Distributions

numpy-1.16.4-cp37-cp37m-win_amd64.whl (11.9 MB view details)

Uploaded CPython 3.7m Windows x86-64

numpy-1.16.4-cp37-cp37m-win32.whl (10.0 MB view details)

Uploaded CPython 3.7m Windows x86

numpy-1.16.4-cp37-cp37m-manylinux1_x86_64.whl (17.3 MB view details)

Uploaded CPython 3.7m

numpy-1.16.4-cp37-cp37m-manylinux1_i686.whl (14.8 MB view details)

Uploaded CPython 3.7m

numpy-1.16.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (13.9 MB view details)

Uploaded CPython 3.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

numpy-1.16.4-cp36-cp36m-win_amd64.whl (11.9 MB view details)

Uploaded CPython 3.6m Windows x86-64

numpy-1.16.4-cp36-cp36m-win32.whl (10.0 MB view details)

Uploaded CPython 3.6m Windows x86

numpy-1.16.4-cp36-cp36m-manylinux1_x86_64.whl (17.3 MB view details)

Uploaded CPython 3.6m

numpy-1.16.4-cp36-cp36m-manylinux1_i686.whl (14.8 MB view details)

Uploaded CPython 3.6m

numpy-1.16.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (13.9 MB view details)

Uploaded CPython 3.6m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

numpy-1.16.4-cp35-cp35m-win_amd64.whl (11.9 MB view details)

Uploaded CPython 3.5m Windows x86-64

numpy-1.16.4-cp35-cp35m-win32.whl (10.0 MB view details)

Uploaded CPython 3.5m Windows x86

numpy-1.16.4-cp35-cp35m-manylinux1_x86_64.whl (17.2 MB view details)

Uploaded CPython 3.5m

numpy-1.16.4-cp35-cp35m-manylinux1_i686.whl (14.7 MB view details)

Uploaded CPython 3.5m

numpy-1.16.4-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (13.9 MB view details)

Uploaded CPython 3.5m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

numpy-1.16.4-cp27-cp27mu-manylinux1_x86_64.whl (17.0 MB view details)

Uploaded CPython 2.7mu

numpy-1.16.4-cp27-cp27mu-manylinux1_i686.whl (14.5 MB view details)

Uploaded CPython 2.7mu

numpy-1.16.4-cp27-cp27m-win_amd64.whl (11.9 MB view details)

Uploaded CPython 2.7m Windows x86-64

numpy-1.16.4-cp27-cp27m-win32.whl (10.0 MB view details)

Uploaded CPython 2.7m Windows x86

numpy-1.16.4-cp27-cp27m-manylinux1_x86_64.whl (17.0 MB view details)

Uploaded CPython 2.7m

numpy-1.16.4-cp27-cp27m-manylinux1_i686.whl (14.5 MB view details)

Uploaded CPython 2.7m

numpy-1.16.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (13.9 MB view details)

Uploaded CPython 2.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

File details

Details for the file numpy-1.16.4.zip.

File metadata

  • Download URL: numpy-1.16.4.zip
  • Upload date:
  • Size: 5.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4.zip
Algorithm Hash digest
SHA256 7242be12a58fec245ee9734e625964b97cf7e3f2f7d016603f9e56660ce479c7
MD5 74f7d348c55ace4d22d7ad26c65755aa
BLAKE2b-256 d34bf9f4b96c0b1ba43d28a5bdc4b64f0b9d3fbcf31313a51bc766942866a7c7

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 11.9 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 dd9bcd4f294eb0633bb33d1a74febdd2b9018b8b8ed325f861fffcd2c7660bb8
MD5 17b46c338d04cb8b4773fb6b02919f2b
BLAKE2b-256 ce61be72eee50f042db3acf0b1fb86650ad36d6c0d9be9fc29f8505d3b9d6baa

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy-1.16.4-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 10.0 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 3c26010c1b51e1224a3ca6b8df807de6e95128b0908c7e34f190e7775455b0ca
MD5 f84869efe5610e6ad6165237c012ea93
BLAKE2b-256 0746656c25b39fc152ea525eef14b641993624a6325a8ae815b200de57cff0bc

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 17.3 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 cbddc56b2502d3f87fda4f98d948eb5b11f36ff3902e17cb6cc44727f2200525
MD5 e98fc6a8d90ff7ed26d0ed7faad3aa8d
BLAKE2b-256 fcd145be1144b03b6b1e24f9a924f23f66b4ad030d834ad31fb9e5581bd328af

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp37-cp37m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.16.4-cp37-cp37m-manylinux1_i686.whl
  • Upload date:
  • Size: 14.8 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 f12b4f7e2d8f9da3141564e6737d79016fe5336cc92de6814eba579744f65b0a
MD5 c1d3c38c67396809c51f5c98aead5e13
BLAKE2b-256 dd4027395e0ab15dbcc5015899f4cc4ecbb535864db17cfb3b9a5bae66a98ea7

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.16.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 f58ac38d5ca045a377b3b377c84df8175ab992c970a53332fa8ac2373df44ff7
MD5 dab4ec8a1c07a7a1a54932c461933992
BLAKE2b-256 6bbe608b7f72b851472388eafc010a5d46dae5d41610d0ac5df4c98c2ed1b865

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 11.9 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 ad3399da9b0ca36e2f24de72f67ab2854a62e623274607e37e0ce5f5d5fa9166
MD5 de4fa9f01692ec94932a289440f18255
BLAKE2b-256 20ede036d31a9b2c750f270cbb1cfc1c0f94ac78ae504eea7eec3267be4e294a

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp36-cp36m-win32.whl.

File metadata

  • Download URL: numpy-1.16.4-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 10.0 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 dc2ca26a19ab32dc475dbad9dfe723d3a64c835f4c23f625c2b6566ca32b9f29
MD5 6fcb9a8f601795413ceaf06767caca2d
BLAKE2b-256 7398cecf557b7f3f1dfac93171392887e4f7a606d6867752311c56a30742d581

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 17.3 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 27e11c7a8ec9d5838bc59f809bfa86efc8a4fd02e58960fa9c49d998e14332d5
MD5 255ae62cf215e647ee437d432b6511c2
BLAKE2b-256 872de4656149cbadd3a8a0369fcd1a9c7d61cc7b87b3903b85389c70c989a696

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp36-cp36m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.16.4-cp36-cp36m-manylinux1_i686.whl
  • Upload date:
  • Size: 14.8 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 ec31fe12668af687b99acf1567399632a7c47b0e17cfb9ae47c098644ef36797
MD5 833f763fb0d69c850fae175c65f7b502
BLAKE2b-256 e4ca037f4d2b7788bd077af2bbe887f7225c74c5df8bab4824514d7decb8a904

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.16.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 a89e188daa119ffa0d03ce5123dee3f8ffd5115c896c2a9d4f0dbb3d8b95bfa3
MD5 1376e801040a91f8b325e827e6d53f91
BLAKE2b-256 0fc93526a357b6c35e5529158fbcfac1bb3adc8827e8809a6d254019d326d1cc

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 11.9 MB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 14270a1ee8917d11e7753fb54fc7ffd1934f4d529235beec0b275e2ccf00333b
MD5 cf671f2b0e651e701472456107c8e644
BLAKE2b-256 b41b36bd20a4a1f41729c406014974925598edaeca1ca2510a2843892329b2f1

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp35-cp35m-win32.whl.

File metadata

  • Download URL: numpy-1.16.4-cp35-cp35m-win32.whl
  • Upload date:
  • Size: 10.0 MB
  • Tags: CPython 3.5m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp35-cp35m-win32.whl
Algorithm Hash digest
SHA256 d79f18f41751725c56eceab2a886f021d70fd70a6188fd386e29a045945ffc10
MD5 cc84f9555a711a2bc867d3b941992a68
BLAKE2b-256 5813f5e2b4057707b62457085d48f27cde6caa594bfa0254aceb29405fb8b5a4

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 17.2 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 6e4f8d9e8aa79321657079b9ac03f3cf3fd067bf31c1cca4f56d49543f4356a5
MD5 07b33ea867cf2657e23dbf93069eff99
BLAKE2b-256 bbefd5a21cbc094d3f4d5b5336494dbcc9550b70c766a8345513c7c24ed18418

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp35-cp35m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.16.4-cp35-cp35m-manylinux1_i686.whl
  • Upload date:
  • Size: 14.7 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 141c7102f20abe6cf0d54c4ced8d565b86df4d3077ba2343b61a6db996cefec7
MD5 d6550e24ff69d4a175d278f39f871d39
BLAKE2b-256 38613704bcbedb6fbcef9b92fe66d08af2b4328d10f199251e9b7a6db71547dc

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.16.4-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 52c40f1a4262c896420c6ea1c6fda62cf67070e3947e3307f5562bd783a90336
MD5 32b18d06069d3d86b8e3193b2f455c15
BLAKE2b-256 48b4266431019b3b2e0f343a4f98db31add8a5ce2d464e30cdd9deaca29a8751

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 17.0 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b0348be89275fd1d4c44ffa39530c41a21062f52299b1e3ee7d1c61f060044b8
MD5 038f16384a2af6bd3db61dc773ffbe10
BLAKE2b-256 1fc7198496417c9c2f6226616cff7dedf2115a4f4d0276613bab842ec8ac1e23

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27mu-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.16.4-cp27-cp27mu-manylinux1_i686.whl
  • Upload date:
  • Size: 14.5 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 0778076e764e146d3078b17c24c4d89e0ecd4ac5401beff8e1c87879043a0633
MD5 52c8e342f110b2fba426fca60b1c2774
BLAKE2b-256 35517eae9042f5904463cb27fea567afc15e90956bd4b7cba98ec1969e58f74a

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp27-cp27m-win_amd64.whl
  • Upload date:
  • Size: 11.9 MB
  • Tags: CPython 2.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 7dc253b542bfd4b4eb88d9dbae4ca079e7bf2e2afd819ee18891a43db66c60c7
MD5 6dd36dfd23338844c1ecac8b92efd938
BLAKE2b-256 a6db18770d6b8419188d56b8ddd9794cb34c2d9f1d272ed8b40fa1ee38a3ca06

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27m-win32.whl.

File metadata

  • Download URL: numpy-1.16.4-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 10.0 MB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 94f5bd885f67bbb25c82d80184abbf7ce4f6c3c3a41fbaa4182f034bba803e69
MD5 c96618196f6dfc29f4931a2f6fea44ad
BLAKE2b-256 132d0fa2e8de7022a4a39497f4a9e384b8b129dbcf5d1b059f1043e21f6f0a48

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.16.4-cp27-cp27m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 17.0 MB
  • Tags: CPython 2.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 2a04dda79606f3d2f760384c38ccd3d5b9bb79d4c8126b67aff5eb09a253763e
MD5 b62eca40cbab3e24c4962e22633d92a5
BLAKE2b-256 202c4d64f1cd4d2170b91d24ae45725de837bd40c34c9c04c94255c0f51c513d

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.16.4-cp27-cp27m-manylinux1_i686.whl
  • Upload date:
  • Size: 14.5 MB
  • Tags: CPython 2.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.1 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/2.7.16

File hashes

Hashes for numpy-1.16.4-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 e8baab1bc7c9152715844f1faca6744f2416929de10d7639ed49555a85549f52
MD5 efcfb51254d83060a2af0d30aa1d1b81
BLAKE2b-256 f2348de93582f74bf3b9a277054b436b9cf53128d7b84820bc6eb859d0afac74

See more details on using hashes here.

File details

Details for the file numpy-1.16.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.16.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 b5554368e4ede1856121b0dfa35ce71768102e4aa55e526cb8de7f374ff78722
MD5 a24c599ae3445d9d085e77ce4d072259
BLAKE2b-256 8f0b1a2c21bb69138337dc079841aa4a45e5b2fc7a4260c0907f5254fb08f02e

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