numpy

NumPy is the fundamental package for array computing with Python.

These details have been verified by PyPI

Maintainers

charlesr.harris matthew.brett mattip rgommers teoliphant

These details have not been verified by PyPI

Project links

Project description

It provides:

a powerful N-dimensional array object
sophisticated (broadcasting) functions
tools for integrating C/C++ and Fortran code
useful linear algebra, Fourier transform, and random number capabilities
and much more

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

All NumPy wheels distributed on PyPI are BSD licensed.

Project details

These details have been verified by PyPI

Maintainers

charlesr.harris matthew.brett mattip rgommers teoliphant

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

2.1.1

Sep 3, 2024

2.1.0

Aug 18, 2024

2.1.0rc1 pre-release

Aug 11, 2024

2.0.2

Aug 26, 2024

2.0.1

Jul 21, 2024

2.0.0

Jun 16, 2024

1.26.4

Feb 5, 2024

1.26.3

Jan 2, 2024

1.26.2

Nov 12, 2023

1.26.1

Oct 14, 2023

1.26.0

Sep 16, 2023

1.25.2

Jul 31, 2023

1.25.1

Jul 8, 2023

1.25.0

Jun 17, 2023

1.24.4

Jun 26, 2023

1.24.3

Apr 22, 2023

1.24.2

Feb 5, 2023

1.24.1

Dec 26, 2022

1.24.0

Dec 18, 2022

1.23.5

Nov 20, 2022

1.23.4

Oct 12, 2022

1.23.3

Sep 9, 2022

1.23.2

Aug 14, 2022

1.23.1

Jul 9, 2022

1.23.0

Jun 22, 2022

1.22.4

May 20, 2022

1.22.3

Mar 7, 2022

1.22.2

Feb 4, 2022

1.22.1

Jan 14, 2022

1.22.0

Dec 31, 2021

1.21.6

Apr 12, 2022

1.21.5

Dec 19, 2021

1.21.4

Nov 5, 2021

1.21.3

Oct 20, 2021

1.21.2

Aug 15, 2021

1.21.1

Jul 18, 2021

1.21.0

Jun 22, 2021

1.20.3

May 10, 2021

1.20.2

Mar 27, 2021

1.20.1

Feb 7, 2021

1.20.0

Jan 30, 2021

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

This version

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

1.17.5

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

1.16.6

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

1.15.4

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

1.14.6

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

1.13.3

Sep 29, 2017

1.13.1

Jul 7, 2017

1.13.0

Jun 7, 2017

1.12.1

Mar 18, 2017

1.12.0

Jan 15, 2017

1.11.3

Dec 19, 2016

1.11.2

Oct 4, 2016

1.11.1

Jun 26, 2016

1.11.0

Mar 27, 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.

Source Distribution

numpy-1.18.5.zip (5.4 MB view hashes)

Uploaded Jun 4, 2020 Source

Built Distributions

numpy-1.18.5-cp38-cp38-win_amd64.whl (12.8 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.8 Windows x86-64

numpy-1.18.5-cp38-cp38-win32.whl (10.8 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.8 Windows x86

numpy-1.18.5-cp38-cp38-manylinux1_x86_64.whl (20.6 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.8

numpy-1.18.5-cp38-cp38-manylinux1_i686.whl (17.8 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.8

numpy-1.18.5-cp38-cp38-macosx_10_9_x86_64.whl (15.1 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.8 macOS 10.9+ x86-64

numpy-1.18.5-cp37-cp37m-win_amd64.whl (12.7 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.7m Windows x86-64

numpy-1.18.5-cp37-cp37m-win32.whl (10.8 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.7m Windows x86

numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl (20.1 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.7m

numpy-1.18.5-cp37-cp37m-manylinux1_i686.whl (17.4 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.7m

numpy-1.18.5-cp37-cp37m-macosx_10_9_x86_64.whl (15.1 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.7m macOS 10.9+ x86-64

numpy-1.18.5-cp36-cp36m-win_amd64.whl (12.7 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.6m Windows x86-64

numpy-1.18.5-cp36-cp36m-win32.whl (10.8 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.6m Windows x86

numpy-1.18.5-cp36-cp36m-manylinux1_x86_64.whl (20.1 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.6m

numpy-1.18.5-cp36-cp36m-manylinux1_i686.whl (17.4 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.6m

numpy-1.18.5-cp36-cp36m-macosx_10_9_x86_64.whl (15.1 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.6m macOS 10.9+ x86-64

numpy-1.18.5-cp35-cp35m-win_amd64.whl (12.7 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.5m Windows x86-64

numpy-1.18.5-cp35-cp35m-win32.whl (10.7 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.5m Windows x86

numpy-1.18.5-cp35-cp35m-manylinux1_x86_64.whl (19.9 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.5m

numpy-1.18.5-cp35-cp35m-manylinux1_i686.whl (17.3 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.5m

numpy-1.18.5-cp35-cp35m-macosx_10_9_intel.whl (14.8 MB view hashes)

Uploaded Jun 4, 2020 CPython 3.5m macOS 10.9+ intel

Hashes for numpy-1.18.5.zip

Hashes for numpy-1.18.5.zip
Algorithm	Hash digest
SHA256	`34e96e9dae65c4839bd80012023aadd6ee2ccb73ce7fdf3074c62f301e63120b`
MD5	`0d426af04e17cd480ecf3cd70743eaf4`
BLAKE2b-256	`011bd3ddcabd5817be02df0e6ee20d64f77ff6d0d97f83b77f65e98c8a651981`

Hashes for numpy-1.18.5-cp38-cp38-win_amd64.whl

Hashes for numpy-1.18.5-cp38-cp38-win_amd64.whl
Algorithm	Hash digest
SHA256	`3dd6823d3e04b5f223e3e265b4a1eae15f104f4366edd409e5a5e413a98f911f`
MD5	`81c9e86442602529b3c52d4af7a515b7`
BLAKE2b-256	`22cb21a148329591931d8764e7ef49cb19586dd5b5e002a184988cb5ec1ccba9`

Hashes for numpy-1.18.5-cp38-cp38-win32.whl

Hashes for numpy-1.18.5-cp38-cp38-win32.whl
Algorithm	Hash digest
SHA256	`9c9d6531bc1886454f44aa8f809268bc481295cf9740827254f53c30104f074a`
MD5	`b66c03695208dd843b78acb32557a765`
BLAKE2b-256	`8a52daf6f4b7fd1499c153cb25ff84f87421598d95e5bb5b760585d2c0263773`

Hashes for numpy-1.18.5-cp38-cp38-manylinux1_x86_64.whl

Hashes for numpy-1.18.5-cp38-cp38-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`4674f7d27a6c1c52a4d1aa5f0881f1eff840d2206989bae6acb1c7668c02ebfb`
MD5	`2347f759a1b8bc27423bb5ece6ae1c79`
BLAKE2b-256	`01c687592f924246da1e58673cf708a2748754517c5cf050726238d6cfbd8df4`

Hashes for numpy-1.18.5-cp38-cp38-manylinux1_i686.whl

Hashes for numpy-1.18.5-cp38-cp38-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`3676abe3d621fc467c4c1469ee11e395c82b2d6b5463a9454e37fe9da07cd0d7`
MD5	`1715c674b3070ccd90f56fa2cd48cce1`
BLAKE2b-256	`ae324382f0ed2723ee04a73e23d6ba9a03c4d09cc1189908756084ccef3305a4`

Hashes for numpy-1.18.5-cp38-cp38-macosx_10_9_x86_64.whl

Hashes for numpy-1.18.5-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`e15b382603c58f24265c9c931c9a45eebf44fe2e6b4eaedbb0d025ab3255228b`
MD5	`2b9153362bf0e53574abc2df048a1578`
BLAKE2b-256	`6d779492f79d58c9abb8a3a93a40ab661ca7f64169a6010ea1365a51294bd64a`

Hashes for numpy-1.18.5-cp37-cp37m-win_amd64.whl

Hashes for numpy-1.18.5-cp37-cp37m-win_amd64.whl
Algorithm	Hash digest
SHA256	`0172304e7d8d40e9e49553901903dc5f5a49a703363ed756796f5808a06fc233`
MD5	`8b793d97dae258d06e63c452a2684b16`
BLAKE2b-256	`e4017a26148f7de9eb6c27f95b29eba16b7e820827cb9ebaae182d7483e44711`

Hashes for numpy-1.18.5-cp37-cp37m-win32.whl

Hashes for numpy-1.18.5-cp37-cp37m-win32.whl
Algorithm	Hash digest
SHA256	`cae14a01a159b1ed91a324722d746523ec757357260c6804d11d6147a9e53e3f`
MD5	`08bdf2289600c5c728a2668b585fdd02`
BLAKE2b-256	`232e41a977865a8ad0ac98b3f9ae421415cd2cd3f195b179a6b5f3aafb7922d7`

Hashes for numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl

Hashes for numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`b39321f1a74d1f9183bf1638a745b4fd6fe80efbb1f6b32b932a588b4bc7695f`
MD5	`f261237ab3d47b9b6e859bf240014a48`
BLAKE2b-256	`d6c658e517e8b1fb192725cfa23c01c2e60e4e6699314ee9684a1c5f5c9b27e1`

Hashes for numpy-1.18.5-cp37-cp37m-manylinux1_i686.whl

Hashes for numpy-1.18.5-cp37-cp37m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`cd49930af1d1e49a812d987c2620ee63965b619257bd76eaaa95870ca08837cf`
MD5	`97f27a6e2e6951cf8107132e7c628004`
BLAKE2b-256	`0ce10a99a7f1f7ade384ec1ac647534f2cccf480a863dafbc96097488fda004b`

Hashes for numpy-1.18.5-cp37-cp37m-macosx_10_9_x86_64.whl

Hashes for numpy-1.18.5-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`a7acefddf994af1aeba05bbbafe4ba983a187079f125146dc5859e6d817df824`
MD5	`bc1ebaa1ecf20f22b72cbb824c9cbc21`
BLAKE2b-256	`3e000266fefaafb839760d5b25b884375b2ab0f842ebe138ee6c1ef807af44bb`

Hashes for numpy-1.18.5-cp36-cp36m-win_amd64.whl

Hashes for numpy-1.18.5-cp36-cp36m-win_amd64.whl
Algorithm	Hash digest
SHA256	`b03b2c0badeb606d1232e5f78852c102c0a7989d3a534b3129e7856a52f3d161`
MD5	`acfa82d4e66601386dad19ad3a3983a5`
BLAKE2b-256	`dc18e69ef84530360c2d39db51acb4cc0012990f27fb1fa7542dac45ad30e7ab`

Hashes for numpy-1.18.5-cp36-cp36m-win32.whl

Hashes for numpy-1.18.5-cp36-cp36m-win32.whl
Algorithm	Hash digest
SHA256	`4064f53d4cce69e9ac613256dc2162e56f20a4e2d2086b1956dd2fcf77b7fac5`
MD5	`9188a301a9640836322f2dc926640515`
BLAKE2b-256	`6ff26b9074c4e4d08196d8b373702c16f84978196dc627298e710af7b03c09e4`

Hashes for numpy-1.18.5-cp36-cp36m-manylinux1_x86_64.whl

Hashes for numpy-1.18.5-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`f718a7949d1c4f622ff548c572e0c03440b49b9531ff00e4ed5738b459f011e8`
MD5	`259dbb8694209921d56ffb091ae42b5b`
BLAKE2b-256	`b3a9b1bc4c935ed063766bce7d3e8c7b20bd52e515ff1c732b02caacf7918e5a`

Hashes for numpy-1.18.5-cp36-cp36m-manylinux1_i686.whl

Hashes for numpy-1.18.5-cp36-cp36m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`ef627986941b5edd1ed74ba89ca43196ed197f1a206a3f18cc9faf2fb84fd675`
MD5	`402be8c771c2541c7ee936ef63c9ebc0`
BLAKE2b-256	`50f45db156e455543c184115e13b28c8c080e4bbc8af9e7f4dfeaae3ebbf74c1`

Hashes for numpy-1.18.5-cp36-cp36m-macosx_10_9_x86_64.whl

Hashes for numpy-1.18.5-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`ac792b385d81151bae2a5a8adb2b88261ceb4976dbfaaad9ce3a200e036753dc`
MD5	`caef5b4785e5deb6891f118a49d48ccc`
BLAKE2b-256	`ae47fc66812fec2cdbdac2cdbc7788ce55fb2072bae5326279079fb00b765b50`

Hashes for numpy-1.18.5-cp35-cp35m-win_amd64.whl

Hashes for numpy-1.18.5-cp35-cp35m-win_amd64.whl
Algorithm	Hash digest
SHA256	`965df25449305092b23d5145b9bdaeb0149b6e41a77a7d728b1644b3c99277c1`
MD5	`5a93e72e30c56462492a29315e19c0cc`
BLAKE2b-256	`ed09ff8f529a5548ff788765f66a81ef751130f26f8c7d517e94d3dbf3ba1ed5`

Hashes for numpy-1.18.5-cp35-cp35m-win32.whl

Hashes for numpy-1.18.5-cp35-cp35m-win32.whl
Algorithm	Hash digest
SHA256	`a87f59508c2b7ceb8631c20630118cc546f1f815e034193dc72390db038a5cb3`
MD5	`2cc7cc1b1640d6b50c50d96a35624698`
BLAKE2b-256	`46044ceccf9b04f89f32bfc2976ebf6c35488c1d6a4b5d375dc4d3137c967386`

Hashes for numpy-1.18.5-cp35-cp35m-manylinux1_x86_64.whl

Hashes for numpy-1.18.5-cp35-cp35m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`a78e438db8ec26d5d9d0e584b27ef25c7afa5a182d1bf4d05e313d2d6d515271`
MD5	`d5bf77d6caf4f83ed871ab9e4f9d1f72`
BLAKE2b-256	`b53688723426b4ff576809fec7d73594fe17a35c27f8d01f93637637a29ae25b`

Hashes for numpy-1.18.5-cp35-cp35m-manylinux1_i686.whl

Hashes for numpy-1.18.5-cp35-cp35m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`7d42ab8cedd175b5ebcb39b5208b25ba104842489ed59fbb29356f671ac93583`
MD5	`79990253bda9ffa2db75152e77c318e9`
BLAKE2b-256	`e70d2f062df1cf227372d0bfc22edfe2451cda972a38185a5748028326c517c3`

Hashes for numpy-1.18.5-cp35-cp35m-macosx_10_9_intel.whl

Hashes for numpy-1.18.5-cp35-cp35m-macosx_10_9_intel.whl
Algorithm	Hash digest
SHA256	`e91d31b34fc7c2c8f756b4e902f901f856ae53a93399368d9a0dc7be17ed2ca0`
MD5	`f923519347ba9f6bca59dce0583bdbd5`
BLAKE2b-256	`85932d19421e0f70b2fd4a1b6f86ff739dd2ce3cea6cab50b2b5f792045388b4`