numpy

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

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

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

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

2.1.3

Nov 2, 2024

2.1.2

Oct 5, 2024

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

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

This version

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-1.12.0.zip (4.8 MB view details)

Uploaded Jan 15, 2017 Source

Built Distributions

numpy-1.12.0-cp36-none-win_amd64.whl (7.7 MB view details)

Uploaded Jan 15, 2017 CPython 3.6 Windows x86-64

numpy-1.12.0-cp36-none-win32.whl (6.7 MB view details)

Uploaded Jan 15, 2017 CPython 3.6 Windows x86

numpy-1.12.0-cp36-cp36m-manylinux1_x86_64.whl (16.8 MB view details)

Uploaded Jan 15, 2017 CPython 3.6m

numpy-1.12.0-cp36-cp36m-manylinux1_i686.whl (12.7 MB view details)

Uploaded Jan 15, 2017 CPython 3.6m

numpy-1.12.0-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 (4.4 MB view details)

Uploaded Jan 15, 2017 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-1.12.0-cp35-none-win_amd64.whl (7.7 MB view details)

Uploaded Jan 15, 2017 CPython 3.5 Windows x86-64

numpy-1.12.0-cp35-none-win32.whl (6.7 MB view details)

Uploaded Jan 15, 2017 CPython 3.5 Windows x86

numpy-1.12.0-cp35-cp35m-manylinux1_x86_64.whl (16.8 MB view details)

Uploaded Jan 15, 2017 CPython 3.5m

numpy-1.12.0-cp35-cp35m-manylinux1_i686.whl (12.6 MB view details)

Uploaded Jan 15, 2017 CPython 3.5m

numpy-1.12.0-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 (4.4 MB view details)

Uploaded Jan 15, 2017 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-1.12.0-cp34-none-win_amd64.whl (7.5 MB view details)

Uploaded Jan 15, 2017 CPython 3.4 Windows x86-64

numpy-1.12.0-cp34-none-win32.whl (6.6 MB view details)

Uploaded Jan 15, 2017 CPython 3.4 Windows x86

numpy-1.12.0-cp34-cp34m-manylinux1_x86_64.whl (16.8 MB view details)

Uploaded Jan 15, 2017 CPython 3.4m

numpy-1.12.0-cp34-cp34m-manylinux1_i686.whl (12.7 MB view details)

Uploaded Jan 15, 2017 CPython 3.4m

numpy-1.12.0-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 (4.4 MB view details)

Uploaded Jan 15, 2017 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-1.12.0-cp27-none-win_amd64.whl (7.5 MB view details)

Uploaded Jan 15, 2017 CPython 2.7 Windows x86-64

numpy-1.12.0-cp27-none-win32.whl (6.6 MB view details)

Uploaded Jan 15, 2017 CPython 2.7 Windows x86

numpy-1.12.0-cp27-cp27mu-manylinux1_x86_64.whl (16.5 MB view details)

Uploaded Jan 15, 2017 CPython 2.7mu

numpy-1.12.0-cp27-cp27mu-manylinux1_i686.whl (12.4 MB view details)

Uploaded Jan 15, 2017 CPython 2.7mu

numpy-1.12.0-cp27-cp27m-manylinux1_x86_64.whl (16.5 MB view details)

Uploaded Jan 15, 2017 CPython 2.7m

numpy-1.12.0-cp27-cp27m-manylinux1_i686.whl (12.4 MB view details)

Uploaded Jan 15, 2017 CPython 2.7m

numpy-1.12.0-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 (4.4 MB view details)

Uploaded Jan 15, 2017 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-1.12.0.zip.

File metadata

Download URL: numpy-1.12.0.zip
Upload date: Jan 15, 2017
Size: 4.8 MB
Tags: Source
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0.zip
Algorithm	Hash digest
SHA256	`ff320ecfe41c6581c8981dce892fe6d7e69806459a899e294e4bf8229737b154`
MD5	`33e5a84579f31829bbbba084fe0a4300`
BLAKE2b-256	`b79d8209e555ea5eb8209855b6c9e60ea80119dab5eff5564330b35aa5dc4b2c`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp36-none-win_amd64.whl.

File metadata

Download URL: numpy-1.12.0-cp36-none-win_amd64.whl
Upload date: Jan 15, 2017
Size: 7.7 MB
Tags: CPython 3.6, Windows x86-64
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp36-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`0bcf40e7717bc03d3af614a5bd8b8448f549abc922445e576198de9885547e69`
MD5	`3d29bf5c852b4eb81b127bbad001610e`
BLAKE2b-256	`454029bc3ccf91887e2e38b18d81adf847b062408a3fa8bdf7c4479c564e6275`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp36-none-win32.whl.

File metadata

Download URL: numpy-1.12.0-cp36-none-win32.whl
Upload date: Jan 15, 2017
Size: 6.7 MB
Tags: CPython 3.6, Windows x86
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp36-none-win32.whl
Algorithm	Hash digest
SHA256	`ca9ec2610751628f485a2fc8ca3f31980cb539d7f9477b72273f066640816ca2`
MD5	`8b660d969a04678ad21726580811e55d`
BLAKE2b-256	`d4a655f85fa298a26b89d516833d8f83feb270ee9cfd8c6cd047b2170d725ee6`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

Download URL: numpy-1.12.0-cp36-cp36m-manylinux1_x86_64.whl
Upload date: Jan 15, 2017
Size: 16.8 MB
Tags: CPython 3.6m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`818a09855c5a257aea637c73755806dfec130ecf190130a34bf1546ef69b77e3`
MD5	`397600ce67594eaab38f0d3accc181ce`
BLAKE2b-256	`7ddb04b13cd69a66657e27dd8af68650b5c8c511501f108358653fca8e52bf86`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp36-cp36m-manylinux1_i686.whl.

File metadata

Download URL: numpy-1.12.0-cp36-cp36m-manylinux1_i686.whl
Upload date: Jan 15, 2017
Size: 12.7 MB
Tags: CPython 3.6m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp36-cp36m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`1595d07b935aac12aa62c27c59ad9e7b7f3072d9576fad18730efabe322ccc3f`
MD5	`56ce905c77bae20026cf15f5b303748a`
BLAKE2b-256	`70ca2a3c552d10491c6fd63780a23b6cd400e02a30ca9bc1acadc89fcd8ce4f4`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-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

Download URL: numpy-1.12.0-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
Upload date: Jan 15, 2017
Size: 4.4 MB
Tags: 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
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-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	`204ffa15185d4b94eaef06d3395bd6ae032fb9db3985a2e3fd758121062021a0`
MD5	`b8e5150a12bbf13ca4ab847839906e5f`
BLAKE2b-256	`7ac6035158a65ad8fb8ed606900f0af0f4dd7c4462f9e3d752ad9303cb367771`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp35-none-win_amd64.whl.

File metadata

Download URL: numpy-1.12.0-cp35-none-win_amd64.whl
Upload date: Jan 15, 2017
Size: 7.7 MB
Tags: CPython 3.5, Windows x86-64
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp35-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`84cec2549e94dd147a384d60d8ab342309f80c8e099b2325505a202cd3a764a5`
MD5	`ce2c1303b7be932e216388ba2480f581`
BLAKE2b-256	`2b89c1e5b28880468870c7f395fcd383aac3893f7bdcc237266498633b20ed61`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp35-none-win32.whl.

File metadata

Download URL: numpy-1.12.0-cp35-none-win32.whl
Upload date: Jan 15, 2017
Size: 6.7 MB
Tags: CPython 3.5, Windows x86
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp35-none-win32.whl
Algorithm	Hash digest
SHA256	`778c8c5f734c47c0292c4df17cc7d5acb4957dd8972793b9c3a6b37a333f4be0`
MD5	`edb061429a0ed84787205a5f18266bcb`
BLAKE2b-256	`ac7d125fbc2bd94d446b3e84a8a02b252b75032d7385552e8cda11db879dece4`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

Download URL: numpy-1.12.0-cp35-cp35m-manylinux1_x86_64.whl
Upload date: Jan 15, 2017
Size: 16.8 MB
Tags: CPython 3.5m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`de35d0b8ff0c0499ff143c7b408fdb592c5e2e56801e30f992be1d8540ac8b12`
MD5	`1938e565fe049351342fb6800f14b0d8`
BLAKE2b-256	`101aca086475ab930049c174d98519b570bffe97a39b5b6944202b910ba300fa`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp35-cp35m-manylinux1_i686.whl.

File metadata

Download URL: numpy-1.12.0-cp35-cp35m-manylinux1_i686.whl
Upload date: Jan 15, 2017
Size: 12.6 MB
Tags: CPython 3.5m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp35-cp35m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`a0644c0f861c96146a641a160011069b7726791b80aa94791dcf24f0abcb8c09`
MD5	`3ddca2ebe93a880ece4e09b4756f3b7e`
BLAKE2b-256	`a6d69bf1fb68f877562d099446ecfef2636548f15349bd512a27e63b4e7959ee`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-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

Download URL: numpy-1.12.0-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
Upload date: Jan 15, 2017
Size: 4.4 MB
Tags: 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
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-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	`f6a56e2278348395279eedba201d344fd8cc1017c6b4fd6c3d9a08a81efe5dfa`
MD5	`21b3dafa7e4644ca27d15f91e02a9503`
BLAKE2b-256	`881020a403370eabccf9e7a092346e079d6a58828ffe2feac15eb5ee2e20e5ea`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp34-none-win_amd64.whl.

File metadata

Download URL: numpy-1.12.0-cp34-none-win_amd64.whl
Upload date: Jan 15, 2017
Size: 7.5 MB
Tags: CPython 3.4, Windows x86-64
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp34-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`f77965ace763464336a2eb84a2ae14cefb58a6a5ceaa5fab4072308623a6d6e3`
MD5	`e65e9cf4864d17f1e490bc5d5b88c587`
BLAKE2b-256	`43eb96b7941c9c993e9e1c4ee5cdfabffc3817988b17969e510e4244ebfd2d48`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp34-none-win32.whl.

File metadata

Download URL: numpy-1.12.0-cp34-none-win32.whl
Upload date: Jan 15, 2017
Size: 6.6 MB
Tags: CPython 3.4, Windows x86
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp34-none-win32.whl
Algorithm	Hash digest
SHA256	`a8a402599b7e3f193ff97a859fde7f165df4d46d20621d05f535f25a437100e0`
MD5	`f5e25075731cedfbf24c7db494003cfd`
BLAKE2b-256	`dfed9efaf13a5d78d3a73eea0cced3becddfa92051bed8dfc4d39464e392feb0`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

Download URL: numpy-1.12.0-cp34-cp34m-manylinux1_x86_64.whl
Upload date: Jan 15, 2017
Size: 16.8 MB
Tags: CPython 3.4m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp34-cp34m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`08ca549ac6e4f86a052e8d2d2eddcb00b61e642ddd3ef55b57c4756a06459604`
MD5	`0197a7f4060e82bf03a450fcac4f56a3`
BLAKE2b-256	`4856fd4698f72aa705a0feddef869fbff26011efdcfcaf1126a48f8a59043843`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp34-cp34m-manylinux1_i686.whl.

File metadata

Download URL: numpy-1.12.0-cp34-cp34m-manylinux1_i686.whl
Upload date: Jan 15, 2017
Size: 12.7 MB
Tags: CPython 3.4m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp34-cp34m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`249841fd5e211e4ccbeac276a7c794c7ac841e798508fb151dc67559c65941df`
MD5	`1b7af502fe2200652608073f6e13c233`
BLAKE2b-256	`3c4e702366b70b30e33a62d50ce4c8620f17c35e010d616c2509462baff70541`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-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

Download URL: numpy-1.12.0-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
Upload date: Jan 15, 2017
Size: 4.4 MB
Tags: 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
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-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	`29e3f343b61d009293070bafc1048a8370d0558cb4b0e26f69e71831922c6b31`
MD5	`e57f9f0ba8fed7cc8d5ef256e4dee3c9`
BLAKE2b-256	`7c8f969defa53c4fe99027658158a69b1b8d0831be1c2705db1eb474fd4536e2`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp27-none-win_amd64.whl.

File metadata

Download URL: numpy-1.12.0-cp27-none-win_amd64.whl
Upload date: Jan 15, 2017
Size: 7.5 MB
Tags: CPython 2.7, Windows x86-64
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp27-none-win_amd64.whl
Algorithm	Hash digest
SHA256	`e307978ab4a5f7f3c88c0eadab482249b01bc6dc943e9fc32d6b696c1cd45ea0`
MD5	`a0e2cf28701964ce32b27ce3d2d670d5`
BLAKE2b-256	`90e8b5dde871d6ca99ef23dc68457bb7572e74d447fdd12f8c11f996120e8828`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp27-none-win32.whl.

File metadata

Download URL: numpy-1.12.0-cp27-none-win32.whl
Upload date: Jan 15, 2017
Size: 6.6 MB
Tags: CPython 2.7, Windows x86
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp27-none-win32.whl
Algorithm	Hash digest
SHA256	`70dfe7b936f7953f6366fa5bf03f127ec322b737469e97a41b7096af4673051c`
MD5	`5ea683e61094e0f5297f527d75d35f8d`
BLAKE2b-256	`9c35e503033f81c4627fc6ba6f92185563e5e95988b5e4df50b83c6af5720bd5`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

Download URL: numpy-1.12.0-cp27-cp27mu-manylinux1_x86_64.whl
Upload date: Jan 15, 2017
Size: 16.5 MB
Tags: CPython 2.7mu
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`160a3168325fa4ef04cddff168ab9cffd0e579a911fdf8d888dbf06be49d2253`
MD5	`9f9bc53d2e281831e1a75be0c09a9548`
BLAKE2b-256	`cb4719e96945ee6012459e85f87728633f05b1e8791677ae64370d16ac4c849e`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp27-cp27mu-manylinux1_i686.whl.

File metadata

Download URL: numpy-1.12.0-cp27-cp27mu-manylinux1_i686.whl
Upload date: Jan 15, 2017
Size: 12.4 MB
Tags: CPython 2.7mu
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp27-cp27mu-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`1ac518945ca78fc54b63bdd30f790ac4d509448d2d26d20e6c1c063b8d9d05fb`
MD5	`40688215dc3020bece11f186df88c254`
BLAKE2b-256	`de20f2424af5c7075b39809297d23da29719b5d3cbcb726df737922ef29d9a5f`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

Download URL: numpy-1.12.0-cp27-cp27m-manylinux1_x86_64.whl
Upload date: Jan 15, 2017
Size: 16.5 MB
Tags: CPython 2.7m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp27-cp27m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`13acbf9ed3e7eb3691cd4e842804d06bfc5ba48b68b35b7d54addd0246def197`
MD5	`e6ac7b379bc53e3220fc9c0d8c85624d`
BLAKE2b-256	`5ba4761dd4596da94d3ce438d93673fcd8053eb368400526223ab7e981547592`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-cp27-cp27m-manylinux1_i686.whl.

File metadata

Download URL: numpy-1.12.0-cp27-cp27m-manylinux1_i686.whl
Upload date: Jan 15, 2017
Size: 12.4 MB
Tags: CPython 2.7m
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-cp27-cp27m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`ea8115bb72a0c5d46f2a32dce490dcc27f9bea6c6ab6934df30420c2293e3411`
MD5	`cae3611aa666eef0597866dfc59a6671`
BLAKE2b-256	`bb7d4a1c08dca6b162ad5b33d50767e0c1a50a7d04695a5354bab580a9f6fea1`

See more details on using hashes here.

File details

Details for the file numpy-1.12.0-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

Download URL: numpy-1.12.0-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
Upload date: Jan 15, 2017
Size: 4.4 MB
Tags: 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
Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.12.0-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	`bf5df5c600b3d798382bb7d050d2ac4345d9f5c1277283a695e36928945f3cfd`
MD5	`3d870f571fbc1dad2fd81515de689abf`
BLAKE2b-256	`4bfb99346d8d7d2460337f9e1772072d35e1274ca81ce9ef64f821d4686233b4`

See more details on using hashes here.

numpy 1.12.0

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes