Skip to main content

NumPy: array processing for numbers, strings, records, and objects.

Project description

NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays. NumPy is built on the Numeric code base and adds features introduced by numarray as well as an extended C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose data-base applications.

There are also basic facilities for discrete fourier transform, basic linear algebra and random number generation.

All numpy wheels distributed from pypi are BSD licensed.

Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted to SSE2 instructions, so may not give optimal linear algebra performance for your machine. See http://docs.scipy.org/doc/numpy/user/install.html for alternatives.

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-1.14.6.zip (4.9 MB view details)

Uploaded Source

Built Distributions

numpy-1.14.6-cp37-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.7 Windows x86-64

numpy-1.14.6-cp37-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.7 Windows x86

numpy-1.14.6-cp37-cp37m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.7m

numpy-1.14.6-cp37-cp37m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 3.7m

numpy-1.14.6-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 (24.4 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-1.14.6-cp36-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.6 Windows x86-64

numpy-1.14.6-cp36-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.6 Windows x86

numpy-1.14.6-cp36-cp36m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.6m

numpy-1.14.6-cp36-cp36m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 3.6m

numpy-1.14.6-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 (24.4 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-1.14.6-cp35-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.5 Windows x86-64

numpy-1.14.6-cp35-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.5 Windows x86

numpy-1.14.6-cp35-cp35m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.5m

numpy-1.14.6-cp35-cp35m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 3.5m

numpy-1.14.6-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 (24.4 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-1.14.6-cp34-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.4 Windows x86-64

numpy-1.14.6-cp34-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.4 Windows x86

numpy-1.14.6-cp34-cp34m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.4m

numpy-1.14.6-cp34-cp34m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 3.4m

numpy-1.14.6-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (24.3 MB view details)

Uploaded CPython 3.4m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

numpy-1.14.6-cp27-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 2.7 Windows x86-64

numpy-1.14.6-cp27-none-win32.whl (9.9 MB view details)

Uploaded CPython 2.7 Windows x86

numpy-1.14.6-cp27-cp27mu-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 2.7mu

numpy-1.14.6-cp27-cp27mu-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 2.7mu

numpy-1.14.6-cp27-cp27m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 2.7m

numpy-1.14.6-cp27-cp27m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 2.7m

numpy-1.14.6-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 (24.4 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-1.14.6.zip.

File metadata

  • Download URL: numpy-1.14.6.zip
  • Upload date:
  • Size: 4.9 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/2.7.15

File hashes

Hashes for numpy-1.14.6.zip
Algorithm Hash digest
SHA256 1250edf6f6c43e1d7823f0967416bc18258bb271dc536298eb0ea00a9e45b80a
MD5 9118b06f0ff86f9545beee4a10a80717
BLAKE2b-256 86c284dc6f58171bca90326f71e098438b87aa0c0d4a21bceda9caba2af6554e

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp37-none-win_amd64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp37-none-win_amd64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 3.7, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp37-none-win_amd64.whl
Algorithm Hash digest
SHA256 fe909f8d14b2f16ea5d9ec2234fc0ffbfccccaef1ba6bc27d9d21acfe8cc72e1
MD5 4660539e780b295ab849fe9cd6491994
BLAKE2b-256 3cfe8d2eee6d18281415904ef40a12bd7e11162d5304943dbceda4b4e2d50f33

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp37-none-win32.whl.

File metadata

  • Download URL: numpy-1.14.6-cp37-none-win32.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.7, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp37-none-win32.whl
Algorithm Hash digest
SHA256 d3f22c0781ad5fe51d7210f36a91f01620355520996fc332a1d0cf24e0cab794
MD5 d957e060a892311bd19df11fd2efbce3
BLAKE2b-256 230f0230860abdb92bdd59967996a6dbb3a3febea7a3518ef827a1c556175459

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 df2937c62d8d3059c1396c7cacfc12577c0923e2a37557592759358848b1544c
MD5 29539a787aa1c04c5026c7b9c4e611e4
BLAKE2b-256 188449b7f268741119328aeee0802aafb9bc2e164b36fc312daf83af95dae646

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp37-cp37m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.14.6-cp37-cp37m-manylinux1_i686.whl
  • Upload date:
  • Size: 10.1 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 a1cafe27328c1f01127297f11e2be25d5d3821d2654a7459e017cfce98258995
MD5 f816dd37be0320767994c18aaca1f530
BLAKE2b-256 9997683ab59b08a6854c53c02e891150340b00cdbd0982c4d7479fa36d28a775

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-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-1.14.6-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 d37f058ea9a2fd2a9160b0cc65bbfb302dfcea8d5fe178299938d95d7bfa2b83
MD5 7cd2d7d164af228289e2a2dd7dc2f6b0
BLAKE2b-256 5b671141ed8b5bb7a9210da089b0897ec4678ba7118f957b6829fea1073b2475

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp36-none-win_amd64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp36-none-win_amd64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 3.6, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 6d3e10394dada2cdf8ba354025ddf15a744b4e833c77347e31547c4b5c77deab
MD5 fb0334939e7f0716415971c1566a3da5
BLAKE2b-256 dc99f824a73251589d9fcef2384f9dd21bd1601597fda92ced5882940586ec37

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp36-none-win32.whl.

File metadata

  • Download URL: numpy-1.14.6-cp36-none-win32.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.6, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp36-none-win32.whl
Algorithm Hash digest
SHA256 33acfba9f453b0b6465c0aa5fe5cb0d32b8483850bc8cc776b4d3cc96595aa03
MD5 7ece416512eb587d237e0ea35a764387
BLAKE2b-256 61a4a97ff0dc7f1443f2166792d48d58ddb5c9c45613cc2facbed1fecf8e71b3

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4ab59a69a627ee73a2723b60723abfe0404947c16acef7b0880d6bbec93ba7cd
MD5 b40851c94f1c7586a1f5b4e9602a748a
BLAKE2b-256 e5c4395ebb218053ba44d64935b3729bc88241ec279915e72100c5979db10945

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp36-cp36m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.14.6-cp36-cp36m-manylinux1_i686.whl
  • Upload date:
  • Size: 10.1 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 7ce70ef6fd9bdfafd896c617761129fafaa06e4683d0bfbf3c56a87c89e02d61
MD5 200cdb3ed59ced85a6fe4255b4e93c32
BLAKE2b-256 0f116e342e5bd5a05506c0330d73ab0fddcb47943b7c105d0590f4608b607020

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-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-1.14.6-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 db10d3d10658a847f85fe9df0d5fe6df190a30d32423d670c3824580e373c0a8
MD5 afc5355fe367e833bf8b503e2be19e11
BLAKE2b-256 d8dde852dcb7cf593d2a912e76d5492850c3662fe69742aeee55b9bc8971c7fd

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp35-none-win_amd64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp35-none-win_amd64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 3.5, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 057ca467673a4b0422a9365ea0b53572813764f60896d3d1423f5cc9d2dd0d02
MD5 db451ea9b296b95644bbdb0dfe133d38
BLAKE2b-256 3dfb0398507e791b8ae71eca0bdc658c526574fa1f65c07a52dbeb417bce7aaa

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp35-none-win32.whl.

File metadata

  • Download URL: numpy-1.14.6-cp35-none-win32.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.5, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp35-none-win32.whl
Algorithm Hash digest
SHA256 686869ff6adc49b3066fdb44198c0655603b33e2c4d852a04c6a84cd8b224786
MD5 b969c8694c91918927b74f82dcbd6e51
BLAKE2b-256 32f40f23eab2798bdd6d59b05184a36e858f4cc31bbb33fb00e9cf6d8875a31a

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 1454aca5a62fe18bb2828ea1b2f9d1534afed7216c13404a6657cda57937c54b
MD5 25cc365ada785dd26ed74eae5b90630d
BLAKE2b-256 43f4ffc029e0ffe001746b288f80e9b2143dafe442a3e87ac1fb9038964d72ab

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp35-cp35m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.14.6-cp35-cp35m-manylinux1_i686.whl
  • Upload date:
  • Size: 10.1 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 6f2a52bb05c560fd6f29d7b49dfe8b4d7c5445c448e5587969446a0f10cf9164
MD5 9027e902724fe6d0468f30f9fed878c9
BLAKE2b-256 a75a330943d1967bcdbff336ec4fc789ac80be8d86c968b93180904dc9101934

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-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-1.14.6-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 0e7c5e5358be186e0d6c73a9b34e1b890602ac1db413adc61794e2e3e02ec65c
MD5 0f25ad62a1f7627729296d47a72d5fe4
BLAKE2b-256 349e995b7f85025a1a28d3820019d5fd2d1184a58e94afab07b1cee350560546

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp34-none-win_amd64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp34-none-win_amd64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 3.4, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 e5daec856ea0e1111391179449b855aa29f1433ac507adc3d6c00a96abb438cb
MD5 92ad00143740a54180bb6f2015004940
BLAKE2b-256 f8203465758b1cdf2c79c28552a82724ab8967070c8b8e87295880e357824f4a

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp34-none-win32.whl.

File metadata

  • Download URL: numpy-1.14.6-cp34-none-win32.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.4, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp34-none-win32.whl
Algorithm Hash digest
SHA256 1b07024c4d87bf7a0738c438aa7fb709f9d7c093513bb8ffb2ac849f4553658c
MD5 29f8f49c0c3b3282fcd644d66bf15001
BLAKE2b-256 e770a3e33c068f50b723926a7fde40ad007db3ce97b290529fda3265b1881f55

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp34-cp34m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 MB
  • Tags: CPython 3.4m
  • 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 6eb031402a278a6fa5838e543cf36ed6d21a6ee90e9a2803570d47908ca5e9fd
MD5 e326047645ebee9bfac01922663488c7
BLAKE2b-256 99a7755f57ff4e5aa74f55479fc47a35d186d2613dc80b1625629c7b82a12ca9

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp34-cp34m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.14.6-cp34-cp34m-manylinux1_i686.whl
  • Upload date:
  • Size: 10.1 MB
  • Tags: CPython 3.4m
  • 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 1718e009ac6699868c82c4ed154e64945479f5c3d8826b2e10c470e9fad7bd18
MD5 e341e9d58654c8afd15728495a523473
BLAKE2b-256 9feb62faed8c2fa7e52785bab5043686edc15e30bd5947431bbfcace651e7149

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp34-cp34m-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-1.14.6-cp34-cp34m-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 e11e5eba43e0d8b077aafa11e43db7a77af4fa435557972dd3570898e0cbb736
MD5 1ba6477836db55255943977535bf6821
BLAKE2b-256 b61c31c4430cd303edbca6d83b98c8ec75dc8a066943c966515b94cb8abd1790

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp27-none-win_amd64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp27-none-win_amd64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 2.7, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 35be3f06ad20030bfba9ae199fa5d5474aebeabb3197d2ce9fcd8c417f7415e3
MD5 7e2bb331cc8fc5939a362df46cf60081
BLAKE2b-256 10b21e4997ce4cef81e045f567a085e8716f71b725f1d6c639617e57e14c7b73

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp27-none-win32.whl.

File metadata

  • Download URL: numpy-1.14.6-cp27-none-win32.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 2.7, 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp27-none-win32.whl
Algorithm Hash digest
SHA256 4e2f4c7031507b23b14056a4bc2b4cbe865607f55b45bfc15cc745a268bc817e
MD5 cfe9797b5bb22896aae777a356e77eab
BLAKE2b-256 9b64b64a9fcf3699959d25bd259cac49e8192e30e982262cc5bed6b7214863a3

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 964c2c6a9e0ecac54a368effa26a89a97b2e15266dc68dc78f2519f3040be623
MD5 d9e0e8d2aa9a198bcebb9e6627669c7b
BLAKE2b-256 4e5b1077ec0ebfa06f42057e8315bc8e05f5978b6fd0f582879f35f4d62ff124

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp27-cp27mu-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.14.6-cp27-cp27mu-manylinux1_i686.whl
  • Upload date:
  • Size: 10.1 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 40f9c0ae71453e4d28d40e502e531e72810bf3b12b2d55cad939ab86a26ead42
MD5 4d45b10bc3be5e2e87aaf530bbcd9e48
BLAKE2b-256 cf17e08b2855734ce36c8b6fce9f6108e1d30e614c9324972db7e46953fb2eab

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

  • Download URL: numpy-1.14.6-cp27-cp27m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 13.8 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4c774c852cad87f692e6b3e374ba7074c7a9897cf4bafcc47ad48142d455f3ae
MD5 a02a64177b422b6059242f01fc39eba9
BLAKE2b-256 b6555f48d56c2559384f875a8d37eb940533ee52a004b9c9e307aa13646d6ee1

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-cp27-cp27m-manylinux1_i686.whl.

File metadata

  • Download URL: numpy-1.14.6-cp27-cp27m-manylinux1_i686.whl
  • Upload date:
  • Size: 10.1 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/2.7.15

File hashes

Hashes for numpy-1.14.6-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 f1dd9a8ecbe9f8f13652afe04c470bb837578e402a3641649ddc41764d0e4326
MD5 a9325f87cd57dca3164e8920bd93ed30
BLAKE2b-256 74bc3b180aed420341d425e16eacfc858e62379a2dc9c5df83c2fdc61d494b1f

See more details on using hashes here.

File details

Details for the file numpy-1.14.6-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-1.14.6-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 bd6b3906a50f9ad755e2c21a78661eff1bbaab3c803c0fcf22927ec50372dba6
MD5 f67c12a012b32b44e39eb057d6c5e5a9
BLAKE2b-256 8a7305d8bd1d972580b03b50c147df554c98ae2f0c69b1b1220ab78fa221cf43

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