numpy

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

1.20.0rc2 pre-release

Dec 25, 2020

1.20.0rc1 pre-release

Dec 3, 2020

1.19.5

Jan 5, 2021

1.19.4

Nov 2, 2020

1.19.3

Oct 29, 2020

1.19.2

Sep 10, 2020

1.19.1

Jul 21, 2020

1.19.0

Jun 20, 2020

1.19.0rc2 pre-release

May 31, 2020

1.19.0rc1 pre-release

May 18, 2020

1.18.5

Jun 4, 2020

1.18.4

May 3, 2020

1.18.3

Apr 19, 2020

1.18.2

Mar 17, 2020

1.18.1

Jan 6, 2020

1.18.0

Dec 22, 2019

1.18.0rc1 pre-release

Dec 6, 2019

1.17.5

Jan 1, 2020

1.17.4

Nov 11, 2019

1.17.3

Oct 17, 2019

1.17.2

Sep 7, 2019

1.17.1

Aug 27, 2019

1.17.0

Jul 26, 2019

1.17.0rc2 pre-release

Jul 16, 2019

1.17.0rc1 pre-release

Jun 30, 2019

1.16.6

Dec 29, 2019

1.16.5

Aug 28, 2019

1.16.4

May 28, 2019

1.16.3

Apr 22, 2019

1.16.2

Feb 26, 2019

1.16.1

Jan 31, 2019

1.16.0

Jan 14, 2019

1.16.0rc2 pre-release

Jan 5, 2019

1.16.0rc1 pre-release

Dec 20, 2018

1.15.4

Nov 4, 2018

1.15.3

Oct 22, 2018

1.15.2

Sep 23, 2018

1.15.1

Aug 21, 2018

1.15.0

Jul 23, 2018

1.15.0rc2 pre-release

Jul 9, 2018

1.15.0rc1 pre-release

Jun 21, 2018

1.14.6

Sep 23, 2018

1.14.5

Jun 12, 2018

1.14.4

Jun 6, 2018

1.14.3

Apr 28, 2018

1.14.2

Mar 12, 2018

1.14.1

Feb 21, 2018

1.14.0

Jan 6, 2018

1.14.0rc1 pre-release

Dec 13, 2017

1.13.3

Sep 29, 2017

1.13.1

Jul 7, 2017

1.13.0

Jun 7, 2017

1.13.0rc2 pre-release

May 18, 2017

1.13.0rc1 pre-release

May 11, 2017

This version

1.12.1

Mar 18, 2017

1.12.1rc1 pre-release

Mar 7, 2017

1.12.0

Jan 15, 2017

1.12.0rc2 pre-release

Jan 1, 2017

1.12.0rc1 pre-release

Dec 20, 2016

1.12.0b1 pre-release

Nov 17, 2016

1.11.3

Dec 19, 2016

1.11.2

Oct 4, 2016

1.11.2rc1 pre-release

Sep 12, 2016

1.11.1

Jun 26, 2016

1.11.1rc1 pre-release

May 27, 2016

1.11.0

Mar 27, 2016

1.11.0rc2 pre-release

Mar 19, 2016

1.11.0rc1 pre-release

Feb 23, 2016

1.11.0b3 pre-release

Feb 10, 2016

1.10.4

Jan 7, 2016

1.10.3

Jan 7, 2016

1.10.2

Dec 14, 2015

1.10.1

Oct 12, 2015

1.10.0

Oct 6, 2015

1.9.3

Sep 21, 2015

1.9.2

Mar 1, 2015

1.9.1

Nov 2, 2014

1.9.0

Sep 7, 2014

1.8.2

Aug 9, 2014

1.8.1

Mar 25, 2014

1.8.0

Oct 30, 2013

1.7.2

Dec 31, 2013

1.7.1

Apr 7, 2013

1.7.0

Feb 12, 2013

1.6.2

May 20, 2012

1.6.1

Jul 24, 2011

1.6.0

May 14, 2011

1.5.1

Nov 18, 2010

1.5.0

Aug 31, 2010

1.4.1

Apr 24, 2010

1.4.0

Dec 28, 2009

1.3.0

Apr 5, 2009

1.2.1

Oct 29, 2008

1.2.0

Oct 3, 2008

1.1.1

Aug 6, 2008

1.0.4

Nov 7, 2007

1.0.3

May 23, 2007

1.0

Oct 25, 2006

1.0rc3 pre-release

Oct 19, 2006

1.0rc2 pre-release

Oct 10, 2006

1.0rc1 pre-release

Sep 20, 2006

1.0b5 pre-release

Sep 5, 2006

1.0b4 pre-release

Aug 26, 2006

1.0b1 pre-release

Jul 21, 2006

0.9.8

May 18, 2006

0.9.6

Mar 14, 2006

Download files

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

Files for numpy, version 1.12.1
Filename, size	File type	Python version	Upload date	Hashes
Filename, size numpy-1.12.1-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 (4.4 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp27-cp27m-manylinux1_i686.whl (12.4 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp27-cp27m-manylinux1_x86_64.whl (16.5 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp27-cp27mu-manylinux1_i686.whl (12.4 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp27-cp27mu-manylinux1_x86_64.whl (16.5 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp27-none-win32.whl (6.6 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp27-none-win_amd64.whl (7.5 MB)	File type Wheel	Python version cp27	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-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 (4.4 MB)	File type Wheel	Python version cp34	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp34-cp34m-manylinux1_i686.whl (12.7 MB)	File type Wheel	Python version cp34	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp34-cp34m-manylinux1_x86_64.whl (16.8 MB)	File type Wheel	Python version cp34	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp34-none-win32.whl (6.6 MB)	File type Wheel	Python version cp34	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp34-none-win_amd64.whl (7.5 MB)	File type Wheel	Python version cp34	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-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 (4.4 MB)	File type Wheel	Python version cp35	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp35-cp35m-manylinux1_i686.whl (12.6 MB)	File type Wheel	Python version cp35	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp35-cp35m-manylinux1_x86_64.whl (16.8 MB)	File type Wheel	Python version cp35	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp35-none-win32.whl (6.7 MB)	File type Wheel	Python version cp35	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp35-none-win_amd64.whl (7.7 MB)	File type Wheel	Python version cp35	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-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 (4.4 MB)	File type Wheel	Python version cp36	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp36-cp36m-manylinux1_i686.whl (12.7 MB)	File type Wheel	Python version cp36	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp36-cp36m-manylinux1_x86_64.whl (16.8 MB)	File type Wheel	Python version cp36	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp36-none-win32.whl (6.7 MB)	File type Wheel	Python version cp36	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1-cp36-none-win_amd64.whl (7.7 MB)	File type Wheel	Python version cp36	Upload date Mar 18, 2017	Hashes View
Filename, size numpy-1.12.1.zip (4.8 MB)	File type Source	Python version None	Upload date Mar 18, 2017	Hashes View

Hashes for numpy-1.12.1-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

Hashes for numpy-1.12.1-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	`3b21dc40fa1e2450dee8cf54991b0f95c415ac508d5db1227338efcf03c162cd`
MD5	`ca6c4a370f76bb461f7c3e254c45db02`
BLAKE2-256	`0505c9d5ef7c85ce92a95eb449523c8b3baf8890bf04e1ebcb119980915c5488`

Hashes for numpy-1.12.1-cp27-cp27m-manylinux1_i686.whl

Hashes for numpy-1.12.1-cp27-cp27m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`405ce136edb18c6f1f8c5acc75d7d8fbb875cc8b5015562251b93435099233d3`
MD5	`71c887adb4cf6a374ff4a83115c8860b`
BLAKE2-256	`1eea6b12b5ae7f879fdda936f0a407ecb1b8f1b65f1b57c696f6a36632c55250`

Hashes for numpy-1.12.1-cp27-cp27m-manylinux1_x86_64.whl

Hashes for numpy-1.12.1-cp27-cp27m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`ca917155b35b3bcc68ef1ad82570a29414f5088495ea8f68c65b071c50e64340`
MD5	`614755c8ee8408b83bd1ba837b6034b2`
BLAKE2-256	`d8f997aa0903ae39ed4ab6df1c9c22902f3c71f4330a54cf5a81b2bea585544d`

Hashes for numpy-1.12.1-cp27-cp27mu-manylinux1_i686.whl

Hashes for numpy-1.12.1-cp27-cp27mu-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`7e9015bc5de54c8bd73ca750ccedfda25d34a25a767caf802740d35a692ec3ab`
MD5	`3ec80a7e027146d4fad10f85426af256`
BLAKE2-256	`f5f983189c429515422673e226ea49805c7c7c5260f0dc5cd2e7baf70d892cc1`

Hashes for numpy-1.12.1-cp27-cp27mu-manylinux1_x86_64.whl

Hashes for numpy-1.12.1-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`cd7892f7d644d1b4ed2ead254d4851616c07ecf82618e3203e2a81747ffb6069`
MD5	`471f740f61f7fba1a1a1e526bf710c49`
BLAKE2-256	`f9d5f24f86b51298f171826a398efdd64b5214b687a28a2f05ff736b1505b1b2`

Hashes for numpy-1.12.1-cp27-none-win32.whl

Hashes for numpy-1.12.1-cp27-none-win32.whl
Algorithm	Hash digest
SHA256	`56e68de63ae738f40669b6a5f0601f9453940a0470a1e9bea16448e5b53f5f28`
MD5	`906d8d8e1cb6a5056e0405d5b54d6440`
BLAKE2-256	`32155dac23340abe95eae4e819f1575fb9be6b87ea92bf31ca808b41119d0346`

Hashes for numpy-1.12.1-cp27-none-win_amd64.whl

Hashes for numpy-1.12.1-cp27-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`95e52d1077abeead6d205c1fc644f075228813859bb625960c1ae1248c4189ba`
MD5	`7cd640cdcb6b80fa501d377bf883ec61`
BLAKE2-256	`a06102bbd10971adb84ba13cf642b7b2702c9d0e20a4432f6cbf0866274bac98`

Hashes for numpy-1.12.1-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

Hashes for numpy-1.12.1-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	`bcbce5ef18dc826ef67756a0d3669baca815c8d44b26867c6865f714a23d9262`
MD5	`2d89d21806408befdc20b5c9e8bfd354`
BLAKE2-256	`69e1c8b40d1c9e1130996881ad3d42ecec7b31e24307a48c3b23c69ab9a0cafb`

Hashes for numpy-1.12.1-cp34-cp34m-manylinux1_i686.whl

Hashes for numpy-1.12.1-cp34-cp34m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`d8dbd7e35e4819e059a044c7545d5602937d6b666dbd9b6eb8ff40037ab0980c`
MD5	`e5e9c27564bd41d88df001c2cc0ace7b`
BLAKE2-256	`889210cb30d909aa55003096ad23bab761bc305c05b7c20c406760c76e0c72e0`

Hashes for numpy-1.12.1-cp34-cp34m-manylinux1_x86_64.whl

Hashes for numpy-1.12.1-cp34-cp34m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`4eac5f2f624c5e7eecbdb51395ff39a099c48cab607a158f16f288c6fe39a2b3`
MD5	`6288d4e9cfea859e03dc82879539d029`
BLAKE2-256	`0264c6c1c24ff4dbcd789fcfdb782e343ac23c074f6b8b03e818ff60eb0f937f`

Hashes for numpy-1.12.1-cp34-none-win32.whl

Hashes for numpy-1.12.1-cp34-none-win32.whl
Algorithm	Hash digest
SHA256	`9cd16915a815c2f04633d14e7640083c1b72e82b325439c91370adfd376c9975`
MD5	`7e08d4f57dc51c7916042670753c0462`
BLAKE2-256	`adb05fcd4319cb4f66db0ebd8facb030981e5a34172a27d35fdeafbf2cfafaaf`

Hashes for numpy-1.12.1-cp34-none-win_amd64.whl

Hashes for numpy-1.12.1-cp34-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`4c64d9c389827f310c7f4e7887b741c34c6b2c337ff63a12f66ef0197fdf5366`
MD5	`cac2b18bde8a76537762e8acfb25c89d`
BLAKE2-256	`139dc08e7977921729d5655aebdd2e2170641e310e789aa6944fb43a406c0282`

Hashes for numpy-1.12.1-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

Hashes for numpy-1.12.1-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	`9ce673bb7b6240b94b60b52186f5c0825f4b31e8191c8bc7412a7d0348fca2cd`
MD5	`ebd51c3549ee44a57af0f35a9f5b2b02`
BLAKE2-256	`7e7919053aebeb6e0ddf160ee1776a842d580e69757790ea1af8719d31595485`

Hashes for numpy-1.12.1-cp35-cp35m-manylinux1_i686.whl

Hashes for numpy-1.12.1-cp35-cp35m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`130105bfc0b03245115da67b441c48597bf1ed7f5385f8388ce4f75cdf2f91d2`
MD5	`5bb0426593f74b922f1e549cde412f4b`
BLAKE2-256	`a01cfc043d84afe1157c0ba52ed55d19fbfd51aaacabc81036c74155862aa8d6`

Hashes for numpy-1.12.1-cp35-cp35m-manylinux1_x86_64.whl

Hashes for numpy-1.12.1-cp35-cp35m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`92dce120e385767cbe433719b5e3fdb1ac81907140d3984b3187208f79aff19f`
MD5	`c372561ab420e6e18eb8f2e7da24f1fd`
BLAKE2-256	`715c945047c185332bbaf57c400dc4c9bffa13c97486df3cd99e25a641f1cbbb`

Hashes for numpy-1.12.1-cp35-none-win32.whl

Hashes for numpy-1.12.1-cp35-none-win32.whl
Algorithm	Hash digest
SHA256	`e97cecd783e8e7e70d18a42f6df7f18be14cbcc82fb9b837b03d072d1401ae53`
MD5	`9d2d3a0d9af306c51255ced96244213f`
BLAKE2-256	`6e06b83acf0caf285cc9fdcbb45202c1f0609fc06ad7555449a1c6e4d9572a0e`

Hashes for numpy-1.12.1-cp35-none-win_amd64.whl

Hashes for numpy-1.12.1-cp35-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`818d5a1d5752d09929ce1ba1735366d5acc769a1839386dc91f3ac30cf9faf19`
MD5	`4b32dcd1c59804f53cb9473d99673ea5`
BLAKE2-256	`92e27d9c6894511337b012735c0c149a7b4e49db0b934798b3ae05a3b46f31f0`

Hashes for numpy-1.12.1-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

Hashes for numpy-1.12.1-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	`43ccfed0092def52b924e004780517c762f8fce3ececbd3f8e2580ac0538bb5e`
MD5	`a1d17430e3688e962feac3ec0d2f12c2`
BLAKE2-256	`e957a204d3eaefc5ba2bb3d93ac1b93d1ab755d3874ab3d9c33a897e9f79b035`

Hashes for numpy-1.12.1-cp36-cp36m-manylinux1_i686.whl

Hashes for numpy-1.12.1-cp36-cp36m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`5cb6341fc885b101978328d3c8d51a069a97a00699a30891106ef7dda56a0d30`
MD5	`c1f1c64b9d421c8715e476ae8a9d274e`
BLAKE2-256	`ed4159f8fb0197e66377db8407df3ca465011267e988b094c4df0879688efe92`

Hashes for numpy-1.12.1-cp36-cp36m-manylinux1_x86_64.whl

Hashes for numpy-1.12.1-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`5dd60892710df0ef654bbf4d1e3cb53ac79845e55a96e4a26dd47218e06d819a`
MD5	`fbebdc68b7698e00c07bf4ddae0fb717`
BLAKE2-256	`b1e2884cfbfd4f21b2313210d1d2ea72ecc381b98826d1b7e6606929ac6c0a08`

Hashes for numpy-1.12.1-cp36-none-win32.whl

Hashes for numpy-1.12.1-cp36-none-win32.whl
Algorithm	Hash digest
SHA256	`727d6373355b00b96d9320254a676b878d6cd43ae409186bec27eec3e5e4e6e7`
MD5	`3e3110a79b3ce9feb8af31aaf3b47003`
BLAKE2-256	`1f472b4201be09432abfb52a449f58005d2f014b9d51acceafe54c8192980ccb`

Hashes for numpy-1.12.1-cp36-none-win_amd64.whl

Hashes for numpy-1.12.1-cp36-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`47b4c4da2fe0618b65fd70987a414fdc24c09e1ffdff77f7147a3c6627b07596`
MD5	`0c753fec7a10e3778215eb9f7c6f43f4`
BLAKE2-256	`581a473632103d3ef36f20cb578c33bda0fcd2dfd442845e3fedb94b59baf13f`

Hashes for numpy-1.12.1.zip

Hashes for numpy-1.12.1.zip
Algorithm	Hash digest
SHA256	`a65266a4ad6ec8936a1bc85ce51f8600634a31a258b722c9274a80ff189d9542`
MD5	`c75b072a984028ac746a6a332c209a91`
BLAKE2-256	`a5168a678404411842fe02d780b5f0a676ff4d79cd58f0f22acddab1b392e230`