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-1.14.3.zip (4.9 MB view details)

Uploaded Source

Built Distributions

numpy-1.14.3-cp36-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.6 Windows x86-64

numpy-1.14.3-cp36-none-win32.whl (9.8 MB view details)

Uploaded CPython 3.6 Windows x86

numpy-1.14.3-cp36-cp36m-manylinux1_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.6m

numpy-1.14.3-cp36-cp36m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 3.6m

numpy-1.14.3-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 (4.7 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-1.14.3-cp35-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.5 Windows x86-64

numpy-1.14.3-cp35-none-win32.whl (9.8 MB view details)

Uploaded CPython 3.5 Windows x86

numpy-1.14.3-cp35-cp35m-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 3.5m

numpy-1.14.3-cp35-cp35m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 3.5m

numpy-1.14.3-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 (4.7 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-1.14.3-cp34-none-win_amd64.whl (13.3 MB view details)

Uploaded CPython 3.4 Windows x86-64

numpy-1.14.3-cp34-none-win32.whl (9.8 MB view details)

Uploaded CPython 3.4 Windows x86

numpy-1.14.3-cp34-cp34m-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 3.4m

numpy-1.14.3-cp34-cp34m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 3.4m

numpy-1.14.3-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 (4.7 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-1.14.3-cp27-none-win_amd64.whl (13.3 MB view details)

Uploaded CPython 2.7 Windows x86-64

numpy-1.14.3-cp27-none-win32.whl (9.8 MB view details)

Uploaded CPython 2.7 Windows x86

numpy-1.14.3-cp27-cp27mu-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 2.7mu

numpy-1.14.3-cp27-cp27mu-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 2.7mu

numpy-1.14.3-cp27-cp27m-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 2.7m

numpy-1.14.3-cp27-cp27m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 2.7m

numpy-1.14.3-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 (4.7 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-1.14.3.zip.

File metadata

  • Download URL: numpy-1.14.3.zip
  • Upload date:
  • Size: 4.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.14.3.zip
Algorithm Hash digest
SHA256 9016692c7d390f9d378fc88b7a799dc9caa7eb938163dda5276d3f3d6f75debf
MD5 97416212c0a172db4bc6b905e9c4634b
BLAKE2b-256 b02b497c2bb7c660b2606d4a96e2035e92554429e139c6c71cdff67af66b58d2

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp36-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 560e23a12e7599be8e8b67621396c5bc687fd54b48b890adbc71bc5a67333f86
MD5 955959dbc1a743308bfcafb4d867da29
BLAKE2b-256 afe47d7107bdfb5c33f6cf33cdafea8c27d1209cf0068a6e3e3d3342be6f3578

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp36-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp36-none-win32.whl
Algorithm Hash digest
SHA256 9ccf4d5c9139b1e985db915039baa0610a7e4a45090580065f8d8cb801b7422f
MD5 a376953ac6bfca04371899d70126ebd4
BLAKE2b-256 c6dd9dce3596b9ed768cc7e3037d8d1729a87fb963317e2e280d4f95d39f3f81

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 46ce8323ca9384814c7645298b8b627b7d04ce97d6948ef02da357b2389d6972
MD5 04428f5a071531dd463504250c194de3
BLAKE2b-256 7190ca61e203e0080a8cef7ac21eca199829fa8d997f7c4da3e985b49d0a107d

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp36-cp36m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 57dc6c22d59054542600fce6fae2d1189b9c50bafc1aab32e55f7efcc84a6c46
MD5 0f8ed907b7c37d7e8c0508ee30ac5e0b
BLAKE2b-256 0a62d57ccb289af83c85bdf6a2394e208bd13fec83ca796354e73266dce7dcbc

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-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-1.14.3-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 b8987e30d9a0eb6635df9705a75cf8c4a2835590244baecf210163343bc65176
MD5 d728ee343c54c8b9b1186747bae6800b
BLAKE2b-256 8e757a8b7e3c073562563473f2a61bd53e75d0a1f5e2047e576ee61d44113c22

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp35-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 c5eb7254cfc4bd7a4330ad7e1f65b98343836865338c57b0e25c661e41d5cfd9
MD5 b7cd0a630d24ef8ed245cde71e50c46e
BLAKE2b-256 20096f302aba4a08ffcd34b20a6ee94f34a76207105f59acd83462b81469c06e

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp35-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp35-none-win32.whl
Algorithm Hash digest
SHA256 510863d606c932b41d2209e4de6157ab3fdf52001d3e4ad351103176d33c4b8b
MD5 42000f9cfef06906e25c0020a9c92366
BLAKE2b-256 ba3ab5dd45c0942ceb39cb2999405df5218e40bbb88603f48f8ddcd6a0ed2a88

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c80fcf9b38c7f4df666150069b04abbd2fe42ae640703a6e1f128cda83b552b7
MD5 6d7ced18705cdd82030472b7a0b106c9
BLAKE2b-256 7b6111b05cc37ccdaabad89f04dbdc2a02905cf6de6f9b05816dba843beed328

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp35-cp35m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 f22b3206f1c561dd9110b93d144c6aaa4a9a354e3b07ad36030df3ea92c5bb5b
MD5 e93edc38b9e31d774af60b45ad25d3d7
BLAKE2b-256 0dc1226c632c85bfe34021043212fde27d448a3c7d188f483a3ce4fc8d068704

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-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-1.14.3-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 1864d005b2eb7598063e35c320787d87730d864f40d6410f768fe4ea20672016
MD5 faee14118dea28c6e2be5aadaa1613ca
BLAKE2b-256 a8074bf580a7576f3ae5c4dcbff8b40e2144eab161118f136b64f26bbb1ecfc7

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 c3fe23df6fe0898e788581753da453f877350058c5982e85a8972feeecb15309
MD5 fc74d7d13da26e2ffc8bf39d5c24d171
BLAKE2b-256 bd30f784951f5a2a7c454d0f57f4ae87f7ccd434976d243bc2c6a6e06f8c52ff

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp34-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp34-none-win32.whl
Algorithm Hash digest
SHA256 0074d42e2cc333800bd09996223d40ec52e3b1ec0a5cab05dacc09b662c4c1ae
MD5 13fa200925025289dbd120078c54377f
BLAKE2b-256 a46774adac101716ebf5835b45cebcac73423bb17f7e398a83ec13b81afbf6b9

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 e33baf50f2f6b7153ddb973601a11df852697fba4c08b34a5e0f39f66f8120e1
MD5 1a0fc864b3b1aea403b426eb2e83276c
BLAKE2b-256 10cefc69d9ab5b375104b651745bedc72b8645ae038705ea25626db17dc75b14

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp34-cp34m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 e8578a62a8eaf552b95d62f630bb5dd071243ba1302bbff3e55ac48588508736
MD5 0450e19513ff2406055bdffcdfef8d82
BLAKE2b-256 0e267d95543fb63137715db3d1d8ba111dfff6d44adc972d4ca915038d7067ef

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-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-1.14.3-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 760550fdf9d8ec7da9c4402a4afe6e25c0f184ae132011676298a6b636660b45
MD5 711dd188cf3269e092adb4240742731b
BLAKE2b-256 c31f78cda78434a61450a1441169fa7354e35c1de03f25538732772061015037

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 aaef1bea636b6e552bbc5dae0ada87d4f6046359daaa97a05a013b0169620f27
MD5 fa3f732464bc83eb08fc6748aeb01ba0
BLAKE2b-256 22be6865fecd80834a242b32f722e4fde27335f60de6b6b1430626fe7a84ce40

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp27-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp27-none-win32.whl
Algorithm Hash digest
SHA256 98ff275f1b5907490d26b30b6ff111ecf2de0254f0ab08833d8fe61aa2068a00
MD5 9c616eb6134c92ca42cca5883e7861b7
BLAKE2b-256 1c98b03970f7e080bccc1118c5ee0f9168ecc735e6c0e618f7de26d4ec163799

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 0db6301324d0568089663ef2701ad90ebac0e975742c97460e89366692bd0563
MD5 c8243f0d6a77c88acf48235aaedf1497
BLAKE2b-256 c0e708f059a00367fd613e4f2875a16c70b6237268a1d6d166c6d36acada8301

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp27-cp27mu-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 8670067685051b49d1f2f66e396488064299fefca199c7c80b6ba0c639fedc98
MD5 37bfe26b655464a77356ee053deafad2
BLAKE2b-256 4fafa6b848bacda7b4ed60fc187cc040ca9b443c8b682e03bcdd5e508001390a

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 f39afab5769b3aaa786634b94b4a23ef3c150bdda044e8a32a3fc16ddafe803b
MD5 51f3c8de7bac77ce864a8a28dc0c3f10
BLAKE2b-256 ea7d9f99896cc3f4834871619a36da2a833c71a2178a5bdadd4fff40c261c119

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-cp27-cp27m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.3-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 034717bfef517858abc79324820a702dc6cd063effb9baab86533e8a78670689
MD5 501b9237037beee4c1262180c317f527
BLAKE2b-256 6047b62c5b0ec89d53e6347ec2914fa250d0d6bef31dac113a74b819ef7ac324

See more details on using hashes here.

File details

Details for the file numpy-1.14.3-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-1.14.3-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 a8dbab311d4259de5eeaa5b4e83f5f8545e4808f9144e84c0f424a6ee55a7b98
MD5 14b675b1f5c0e33dea22735df8ecf5d1
BLAKE2b-256 b897ecff917542e3a8a33bc8e88c031ed50c90577fd205eab362b29f3e57c09e

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