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-1.15.0.zip (4.5 MB view details)

Uploaded Source

Built Distributions

numpy-1.15.0-cp37-none-win_amd64.whl (13.5 MB view details)

Uploaded CPython 3.7 Windows x86-64

numpy-1.15.0-cp37-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.7 Windows x86

numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.7m

numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl (10.2 MB view details)

Uploaded CPython 3.7m

numpy-1.15.0-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 (24.5 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-1.15.0-cp36-none-win_amd64.whl (13.5 MB view details)

Uploaded CPython 3.6 Windows x86-64

numpy-1.15.0-cp36-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.6 Windows x86

numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl (13.9 MB view details)

Uploaded CPython 3.6m

numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl (10.2 MB view details)

Uploaded CPython 3.6m

numpy-1.15.0-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 (24.5 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-1.15.0-cp35-none-win_amd64.whl (13.5 MB view details)

Uploaded CPython 3.5 Windows x86-64

numpy-1.15.0-cp35-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.5 Windows x86

numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.5m

numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 3.5m

numpy-1.15.0-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-1.15.0-cp34-none-win_amd64.whl (13.5 MB view details)

Uploaded CPython 3.4 Windows x86-64

numpy-1.15.0-cp34-none-win32.whl (9.9 MB view details)

Uploaded CPython 3.4 Windows x86

numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 3.4m

numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 3.4m

numpy-1.15.0-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 (24.4 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-1.15.0-cp27-none-win_amd64.whl (13.5 MB view details)

Uploaded CPython 2.7 Windows x86-64

numpy-1.15.0-cp27-none-win32.whl (9.9 MB view details)

Uploaded CPython 2.7 Windows x86

numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 2.7mu

numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 2.7mu

numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl (13.8 MB view details)

Uploaded CPython 2.7m

numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl (10.1 MB view details)

Uploaded CPython 2.7m

numpy-1.15.0-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 (24.5 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-1.15.0.zip.

File metadata

  • Download URL: numpy-1.15.0.zip
  • Upload date:
  • Size: 4.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.15.0.zip
Algorithm Hash digest
SHA256 f28e73cf18d37a413f7d5de35d024e6b98f14566a10d82100f9dc491a7d449f9
MD5 20e13185089011116a98e11c9bf8aa07
BLAKE2b-256 3a20c81632328b1a4e1db65f45c0a1350a9c5341fd4bbb8ea66cdd98da56fe2e

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp37-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp37-none-win_amd64.whl
Algorithm Hash digest
SHA256 61efc65f325770bbe787f34e00607bc124f08e6c25fdf04723848585e81560dc
MD5 cfef18ee246468752f1686147c70bd0a
BLAKE2b-256 8b8a5edea6c9759b9c569542ad4da07bba0c03ffe7cfb15d8bbe59b417e99a84

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp37-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp37-none-win32.whl
Algorithm Hash digest
SHA256 fb4c33a404d9eff49a0cdc8ead0af6453f62f19e071b60d283f9dc05581e4134
MD5 4482a89fa4540c8bbf76028621931266
BLAKE2b-256 82d11c837a9c705a45e2765bb6e2f504209569685657772fee10473770159ac5

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 50718eea8e77a1bedcc85befd22c8dbf5a24c9d2c0c1e36bbb8d7a38da847eb3
MD5 36ed60bef7c5cb252b9d0e8dc5029e08
BLAKE2b-256 2792c01d3a6c58ceab0e6ec36ad3af41bc076014cc916afcb979ab4c9558f347

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 d690a2ff49f6c3bc35336693c9924fe5916be3cc0503fe1ea6c7e2bf951409ee
MD5 b2fc4551651fae84eb01b8a37f2e1e69
BLAKE2b-256 e73b82e9371b389a06b9d79347a7ed101f5b80217853a4792a7dd4a4ccce842e

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-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-1.15.0-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 21041014b7529237994a6b578701c585703fbb3b1bea356cdb12a5ea7804241c
MD5 e232fbba29585812bf7fa547f671b768
BLAKE2b-256 3adb5c4dab58c03a7ea2561353cb240e96198415f09d65dd63d58058e135f2f9

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp36-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 73a816e441dace289302e04a7a34ec4772ed234ab6885c968e3ca2fc2d06fe2d
MD5 6423497ad5a610c1deed606ce44893bd
BLAKE2b-256 53d12499797c88de95ea3239ad7f6e6a47895fe51aad1aa2a116f50ec9e0ee74

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp36-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp36-none-win32.whl
Algorithm Hash digest
SHA256 7f17efe9605444fcbfd990ba9b03371552d65a3c259fc2d258c24fb95afdd728
MD5 166e901c1a86da5ffb8c6d3090ed917e
BLAKE2b-256 80754c1cd85e891b97a01bc96a232c00f8e60bff4608b6be356a8aed1180fd79

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 f2a778dd9bb3e4590dbe3bbac28e7c7134280c4ec97e3bf8678170ee58c67b21
MD5 5635343a70f7cdd17f372966db1526d3
BLAKE2b-256 8829f4c845648ed23264e986cdc5fbab5f8eace1be5e62144ef69ccc7189461d

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 816645178f2180be257a576b735d3ae245b1982280b97ae819550ce8bcdf2b6b
MD5 5606fa1c1e13e789b802102699d613e2
BLAKE2b-256 4dde1e18b66c3badfc4d26c36682d41a6bbaece9faa52a47af096f8d83e417bf

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-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-1.15.0-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 14fb76bde161c87dcec52d91c78f65aa8a23aa2e1530a71f412dabe03927d917
MD5 1a01c8d089d488565acc2836d03a7482
BLAKE2b-256 6ad5218414f0f41cb3f183d55d68dbcd996fa3602d5849bdf2ad6c059e98fa68

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp35-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 f5a758252502b466b9c2b201ea397dae5a914336c987f3a76c3741a82d43c96e
MD5 2ab8080576932775167a6f9c772b91e4
BLAKE2b-256 624754baeff52b37be258dd97442f52d8a2a9c27c4af8fcbc5467827c5ae5eed

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp35-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp35-none-win32.whl
Algorithm Hash digest
SHA256 34033b581bc01b1135ca2e3e93a94daea7c739f21a97a75cca93e29d9f0c8e71
MD5 956c6f7c216b677b27628a97150cd069
BLAKE2b-256 1bf0c90d0068665841f2a303480ae51c192363b46d2d3c78080cce926444623f

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 24f3bb9a5f6c3936a8ccd4ddfc1210d9511f4aeb879a12efd2e80bec647b8695
MD5 d76c54272549cf3a2165d40d3fea5e30
BLAKE2b-256 29b9479ccb55cc7dcff3d4fc7c8c26d4887846875e7d4f04483a36f335bed712

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 b5f8c15cb9173f6cdf0f994955e58d1265331029ae26296232379461a297e5f2
MD5 77655199a4e18719dd5a0b348c44fc92
BLAKE2b-256 49c35017cf2c8ab75c1f70a6ccb8d76ebe7cfa3e7c22e000fa23d0d24adf43c4

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-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-1.15.0-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 55daf757e5f69aa75b4477cf4511bf1f96325c730e4ad32d954ccb593acd2585
MD5 cc463ee62af94c8410fdf95ce9933c3c
BLAKE2b-256 1059da8c94da6eaa44651c254dbaec2c901544ab1f88f410c47e2d3092e2d88f

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 62cb836506f40ce2529bfba9d09edc4b2687dd18c56cf4457e51c3e7145402fd
MD5 1032db03cefd82e87f72f2b04b15b7ae
BLAKE2b-256 119ff7550ba7d6ea7d1da16ac2069157b42d225b02b47e4c30ccc1c5b6be3b8f

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp34-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp34-none-win32.whl
Algorithm Hash digest
SHA256 aaa519335a71f87217ca8a680c3b66b61960e148407bdf5c209c42f50fe30f49
MD5 fd03012584359cd05cee08408df5897d
BLAKE2b-256 ab8a0a0991238811f37dc74895b9af2e5e5ae4c9876757089267b282d20ac83b

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 3fbccb399fe9095b1c1d7b41e7c7867db8aa0d2347fc44c87a7a180cedda112b
MD5 346d9239f7f12bb7042f8bc847928dc1
BLAKE2b-256 fd6ec9e48d1d1ea71323726c55c57e899af32c54ebae5d50b843e2ac2407c214

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 c7c660cc0209fdf29a4e50146ca9ac9d8664acaded6b6ae2f5d0ae2e91a0f0cd
MD5 662f2536cac7b841f86e9b7488e52371
BLAKE2b-256 cdd813a7433aebe3117b81da36246e1294f9468f6801863bed226ec8c5ed7a29

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-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-1.15.0-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 78c35dc7ad184aebf3714dbf43f054714c6e430e14b9c06c49a864fb9e262030
MD5 badfc9f713510d59f478037c88b3d963
BLAKE2b-256 5890e2624959251e36e6f7898c93e724540ae6e6803c91ec0c40b4e476cea118

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 ae3864816287d0e86ead580b69921daec568fe680857f07ee2a87bf7fd77ce24
MD5 7ba5b463728a792dced42fd6259e511f
BLAKE2b-256 3dd6f04730ad69240be04584b3979dcd2f0b25f9e58463547df6fcafa139c567

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp27-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp27-none-win32.whl
Algorithm Hash digest
SHA256 674ea7917f0657ddb6976bd102ac341bc493d072c32a59b98e5b8c6eaa2d5ec0
MD5 73f930c046ac09e518d0b4cf2f8ff642
BLAKE2b-256 d023de3c3f8fdee3245dd61bedc8c8de0ea5ead8adc245321be38993179ef437

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 924f37e66db78464b4b85ed4b6d2e5cda0c0416e657cac7ccbef14b9fa2c40b5
MD5 0bd79da73435161850099bfcacc75fae
BLAKE2b-256 8551ba4564ded90e093dbb6adfc3e21f99ae953d9ad56477e1b0d4a93bacf7d3

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 64c6acf5175745fd1b7b7e17c74fdbfb7191af3b378bc54f44560279f41238d3
MD5 cbdd2291782deb29f41c9b7d121264e0
BLAKE2b-256 0f0c9acf69f0958369fbb101282d097e44f88e095fee917d04e0a18386ce8e7b

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 e2317cf091c2e7f0dacdc2e72c693cc34403ca1f8e3807622d0bb653dc978616
MD5 a6f7aa33d4d1598dc33831a4bb36570d
BLAKE2b-256 2fbc9808bf8887fc0f300a58ad0e1f2a4fcdaa5dbe109843149c9c9951b943e0

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 c3ac359ace241707e5a48fe2922e566ac666aacacf4f8031f2994ac429c31344
MD5 d5ffa73c6a3eeba8cfcab283e7db3c2f
BLAKE2b-256 9e302d8d610f1cd807bda697dd814409521e10a5bd22101c967c8edd14797705

See more details on using hashes here.

File details

Details for the file numpy-1.15.0-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-1.15.0-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 a17a8fd5df4fec5b56b4d11c9ba8b9ebfb883c90ec361628d07be00aaa4f009a
MD5 4957a50c1125fdecb4cb51829f5feba1
BLAKE2b-256 3cbfe36756c562f7386be78c6942f0a8a647ee4eb374cdf219bece7054832b14

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