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

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for matthew.brett from gravatar.com matthew.brett Avatar for mattip from gravatar.com mattip Avatar for rgommers from gravatar.com rgommers Avatar for teoliphant from gravatar.com teoliphant

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: OSI Approved (BSD)
  • Author: NumPy Developers
  • Requires: Python >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Classifiers

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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for matthew.brett from gravatar.com matthew.brett Avatar for mattip from gravatar.com mattip Avatar for rgommers from gravatar.com rgommers Avatar for teoliphant from gravatar.com teoliphant

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: OSI Approved (BSD)
  • Author: NumPy Developers
  • Requires: Python >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Classifiers

Release history Release notifications | RSS feed

2.1.3

2.1.2

2.1.1

2.1.0

2.1.0rc1 pre-release

2.0.2

2.0.1

2.0.0

1.26.4

1.26.3

1.26.2

1.26.1

1.26.0

1.25.2

1.25.1

1.25.0

1.24.4

1.24.3

1.24.2

1.24.1

1.24.0

1.23.5

1.23.4

1.23.3

1.23.2

1.23.1

1.23.0

1.22.4

1.22.3

1.22.2

1.22.1

1.22.0

1.21.6

1.21.5

1.21.4

1.21.3

1.21.2

1.21.1

1.21.0

1.20.3

1.20.2

1.20.1

1.20.0

1.19.5

1.19.4

1.19.3

1.19.2

1.19.1

1.19.0

1.18.5

1.18.4

1.18.3

1.18.2

1.18.1

1.18.0

1.17.5

1.17.4

1.17.3

1.17.2

1.17.1

1.17.0

1.16.6

1.16.5

1.16.4

1.16.3

1.16.2

1.16.1

1.16.0

1.15.4

1.15.3

1.15.2

1.15.1

1.15.0

1.14.6

1.14.5

1.14.4

1.14.3

This version

1.14.2

1.14.1

1.14.0

1.13.3

1.13.1

1.13.0

1.12.1

1.12.0

1.11.3

1.11.2

1.11.1

1.11.0

1.10.4

1.10.3

1.10.2

1.10.1

1.10.0

1.9.3

1.9.2

1.9.1

1.9.0

1.8.2

1.8.1

1.8.0

1.7.2

1.7.1

1.7.0

1.6.2

1.6.1

1.6.0

1.5.1

1.5.0

1.4.1

1.4.0

1.3.0

1.2.1

1.2.0

1.1.1

1.0.4

1.0.3

1.0

1.0rc3 pre-release

1.0rc2 pre-release

1.0rc1 pre-release

1.0b5 pre-release

1.0b4 pre-release

1.0b1 pre-release

0.9.8

0.9.6

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

numpy-1.14.2.zip (4.9 MB view details)

Uploaded Source

Built Distributions

numpy-1.14.2-cp36-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.6 Windows x86-64

numpy-1.14.2-cp36-none-win32.whl (9.8 MB view details)

Uploaded CPython 3.6 Windows x86

numpy-1.14.2-cp36-cp36m-manylinux1_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.6m

numpy-1.14.2-cp36-cp36m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 3.6m

numpy-1.14.2-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-1.14.2-cp35-none-win_amd64.whl (13.4 MB view details)

Uploaded CPython 3.5 Windows x86-64

numpy-1.14.2-cp35-none-win32.whl (9.8 MB view details)

Uploaded CPython 3.5 Windows x86

numpy-1.14.2-cp35-cp35m-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 3.5m

numpy-1.14.2-cp35-cp35m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 3.5m

numpy-1.14.2-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-1.14.2-cp34-none-win_amd64.whl (13.3 MB view details)

Uploaded CPython 3.4 Windows x86-64

numpy-1.14.2-cp34-none-win32.whl (9.8 MB view details)

Uploaded CPython 3.4 Windows x86

numpy-1.14.2-cp34-cp34m-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 3.4m

numpy-1.14.2-cp34-cp34m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 3.4m

numpy-1.14.2-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-1.14.2-cp27-none-win_amd64.whl (13.3 MB view details)

Uploaded CPython 2.7 Windows x86-64

numpy-1.14.2-cp27-none-win32.whl (9.8 MB view details)

Uploaded CPython 2.7 Windows x86

numpy-1.14.2-cp27-cp27mu-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 2.7mu

numpy-1.14.2-cp27-cp27mu-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 2.7mu

numpy-1.14.2-cp27-cp27m-manylinux1_x86_64.whl (12.1 MB view details)

Uploaded CPython 2.7m

numpy-1.14.2-cp27-cp27m-manylinux1_i686.whl (8.7 MB view details)

Uploaded CPython 2.7m

numpy-1.14.2-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-1.14.2.zip.

File metadata

  • Download URL: numpy-1.14.2.zip
  • Upload date:
  • Size: 4.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.14.2.zip
Algorithm Hash digest
SHA256 facc6f925c3099ac01a1f03758100772560a0b020fb9d70f210404be08006bcb
MD5 080f01a19707cf467393e426382c7619
BLAKE2b-256 0b6686185402ee2d55865c675c06a5cfef742e39f4635a4ce1b1aefd20711c13

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp36-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 6be6b0ca705321c178c9858e5ad5611af664bbdfae1df1541f938a840a103888
MD5 9d78ceef101313f49fd0b8fed25d889c
BLAKE2b-256 3070cd94a1655d082b8f024b21af1eb13dd0f3035ffe78ff43d4ff9bb97baa5f

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp36-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp36-none-win32.whl
Algorithm Hash digest
SHA256 e6120d63b50e2248219f53302af7ec6fa2a42ed1f37e9cda2c76dbaca65036a7
MD5 8f9986b323d4215925d6cfa1cd1bc14d
BLAKE2b-256 053f39ec9e88b0a14930c70722f832861c2ef7bd4bbee9ed8d586c0c1dcb531b

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 07e21f14490324cc1160db101e9b6c1233c33985af4cb1d301dd02650fea1d7f
MD5 65c3802c0f25f2d26aa784433643f655
BLAKE2b-256 6edc92c0f670e7b986829fc92c4c0208edb9d72908149da38ecda50d816ea057

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp36-cp36m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 0739146eaf4985962f07c62f7133aca89f3a600faac891ce6c7f3a1e2afe5272
MD5 b11c80344b84853b7a24acc51bbe4945
BLAKE2b-256 8a23dd4bef28fe2ee3c8a195e28e5a41792e14a1fae007c7013ecef4a0ab9727

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-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-1.14.2-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 8c18ee4dddd5c6a811930c0a7c7947bf16387da3b394725f6063f1366311187d
MD5 1cdb6cf8d60dfbe99f60639dac38471e
BLAKE2b-256 a0dffa637677800e6702a57ef09e1d62e42aec3f598fb235f897155d146f2f59

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp35-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 0fd65cbbfdbf76bbf80c445d923b3accefea0fe2c2082049e0ce947c81fe1d3f
MD5 2652e9660be5d074224d14436504f008
BLAKE2b-256 46eb846d92fed0ef6dbc1906c198e3e5475f1d9f7954ce9648c05c0dfddc36b9

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp35-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp35-none-win32.whl
Algorithm Hash digest
SHA256 a7157c9ac6bddd2908c35ef099e4b643bc0e0ebb4d653deb54891d29258dd329
MD5 8c98ab081112832e3a7faca624598119
BLAKE2b-256 13655681e722cb455c8f5aea63197becd8f37c9afae05f0bd499996384e60640

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 7f76d406c6b998d6410198dcb82688dcdaec7d846aa87e263ccf52efdcfeba30
MD5 ef57856bf6dade82922ab58922756dd0
BLAKE2b-256 ea31991207e6234b46a1228be970735ead9d6f06a298917d6f718c5e32e835bb

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp35-cp35m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 a1413d06abfa942ca0553bf3bccaff5fdb36d55b84f2248e36228db871147dab
MD5 c1231d7e7fc52c09dff9a529ad228818
BLAKE2b-256 7bb7ad7d216dbeafa35e9a8daf9f502db70f56e5bba6e275228197e2b9eff1db

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-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-1.14.2-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 f2b1378b63bdb581d5d7af2ec0373c8d40d651941d283a2afd7fc71184b3f570
MD5 8a74bb1f94ad8c1ad8f37e73f967b850
BLAKE2b-256 5f20fe66080957a74381420c445c76a3e33dc30b6c340bad58eb161f182b42ad

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 bb370120de6d26004358611441e07acda26840e41dfedc259d7f8cc613f96495
MD5 ececd9b8891d801d4a968c2ec5eac7bb
BLAKE2b-256 1c269dd55ab18f78ed333f757ce677cd7ddb6650876ec40b468be4cc9e6915b5

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp34-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp34-none-win32.whl
Algorithm Hash digest
SHA256 a958bf9d4834c72dee4f91a0476e7837b8a2966dc6fcfc42c421405f98d0da51
MD5 350a1e0f0c825ffa1de264108c648482
BLAKE2b-256 7eb47fc423cbbb81e7d51dc5a740dcf9f1bc96946118dddd14a6b287a935bed0

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 675e0f23967ce71067d12b6944add505d5f0a251f819cfb44bdf8ee7072c090d
MD5 cbe383ad27db21767b6ffdd943e3df9c
BLAKE2b-256 7e09c5a2822aa55a9cf89c6398780bbcaa1fede0650cdccd55bc89a8548c0d7a

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp34-cp34m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 768e777cc1ffdbf97c507f65975c8686ebafe0f3dc8925d02ac117acc4669ce9
MD5 7f38fb83008ed4bb8217840ac27aeba4
BLAKE2b-256 acd730fcb52c547e2f1a179b6fd166ee0faa8b5ef49555ae4da4cb4bdbd81819

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-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-1.14.2-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 99051e03b445117b26028623f1a487112ddf61a09a27e2d25e6bc07d37d94f25
MD5 580340cfe4a14f8a9e1d781d7b42955b
BLAKE2b-256 600b82ec3a2f9018e282e1f890950ad1d23c965785f3684a9fbf57b27a85042d

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 56e392b7c738bd70e6f46cf48c8194d3d1dd4c5a59fae4b30c58bb6ef86e5233
MD5 187a94722b84d65cc3a9ecfce27ee3b2
BLAKE2b-256 64f22aa3b3274abe211b773e8a6d8801e19a9451646c213a333359c6a600484a

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp27-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp27-none-win32.whl
Algorithm Hash digest
SHA256 528ce59ded2008f9e8543e0146acb3a98a9890da00adf8904b1e18c82099418b
MD5 6ac633c46c13dd2af93761460d63436e
BLAKE2b-256 2e91504e434d3b95d943caab926f33dee5691768fbb622bc290a0fa6df77e1d8

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 20cac3123d791e4bf8482a580d98d6b5969ba348b9d5364df791ba3a666b660d
MD5 1227a63fcc8ce91a75d2ab006d406df7
BLAKE2b-256 764d418dda252cf92bad00ab82d6b2a856e7843b47a5c2f084aed34b14b67d64

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp27-cp27mu-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 d858423f5ed444d494b15c4cc90a206e1b8c31354c781ac7584da0d21c09c1c3
MD5 ef1065f3ecd08054eca9c6c14a2e3518
BLAKE2b-256 2b0befcec0be075024207e04032961f6b531166d4e63ce3b245ba3cd05d5ffdf

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 d0928076d9bd8a98de44e79b1abe50c1456e7abbb40af7ef58092086f1a6c729
MD5 7733aa702cebb5b0469b820ea9cfc293
BLAKE2b-256 b984513dc190113249244b3027ffebc6bf8ddcce1843cc471620ea179dc5613c

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-cp27-cp27m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.14.2-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 0f6a5ed0cd7ab1da11f5c07a8ecada73fc55a70ef7bb6311a4109891341d7277
MD5 b8a260b915d44475f4385fed4c6a7ec8
BLAKE2b-256 875c61eb6a13bd1d43ee0d445fe6250e26ae696ffe4dabd59711f5e6c9ae6d49

See more details on using hashes here.

File details

Details for the file numpy-1.14.2-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-1.14.2-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 719d914f564f35cce4dc103808f8297c807c9f0297ac183ed81ae8b5650e698e
MD5 9bb06966218d0f3d0a25a6155c7d2439
BLAKE2b-256 554d6fde74ef447202a20e4c1be37475f515d1554d4d677bfe619e408a57c1be

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