numpy

Fundamental package for array computing in 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

NumPy is the fundamental package for scientific computing with Python.

Website: https://www.numpy.org
Documentation: https://numpy.org/doc
Mailing list: https://mail.python.org/mailman/listinfo/numpy-discussion
Source code: https://github.com/numpy/numpy
Contributing: https://www.numpy.org/devdocs/dev/index.html
Bug reports: https://github.com/numpy/numpy/issues
Report a security vulnerability: https://tidelift.com/docs/security

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

Testing:

NumPy requires pytest and hypothesis. Tests can then be run after installation with:

python -c 'import numpy; numpy.test()'

Code of Conduct

NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes our community thrive.

Call for Contributions

The NumPy project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through the mailing list first.

Writing code isn’t the only way to contribute to NumPy. You can also:

review pull requests
help us stay on top of new and old issues
develop tutorials, presentations, and other educational materials
maintain and improve our website
develop graphic design for our brand assets and promotional materials
translate website content
help with outreach and onboard new contributors
write grant proposals and help with other fundraising efforts

For more information about the ways you can contribute to NumPy, visit our website. If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or here, on GitHub, by opening a new issue or leaving a comment on a relevant issue that is already open.

Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact our community coordinators at numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for an invitation).

We also have a biweekly community call, details of which are announced on the mailing list. You are very welcome to join.

If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.

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

This version

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

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-1.24.1.tar.gz (10.9 MB view details)

Uploaded Dec 26, 2022 Source

Built Distributions

numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl (14.7 MB view details)

Uploaded Dec 26, 2022 PyPy Windows x86-64

numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.7 MB view details)

Uploaded Dec 26, 2022 PyPy manylinux: glibc 2.17+ x86-64

numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (19.2 MB view details)

Uploaded Dec 26, 2022 PyPy macOS 10.9+ x86-64

numpy-1.24.1-cp311-cp311-win_amd64.whl (14.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.11 Windows x86-64

numpy-1.24.1-cp311-cp311-win32.whl (12.4 MB view details)

Uploaded Dec 26, 2022 CPython 3.11 Windows x86

numpy-1.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded Dec 26, 2022 CPython 3.11 manylinux: glibc 2.17+ x86-64

numpy-1.24.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded Dec 26, 2022 CPython 3.11 manylinux: glibc 2.17+ ARM64

numpy-1.24.1-cp311-cp311-macosx_11_0_arm64.whl (13.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.11 macOS 11.0+ ARM64

numpy-1.24.1-cp311-cp311-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.11 macOS 10.9+ x86-64

numpy-1.24.1-cp310-cp310-win_amd64.whl (14.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.10 Windows x86-64

numpy-1.24.1-cp310-cp310-win32.whl (12.4 MB view details)

Uploaded Dec 26, 2022 CPython 3.10 Windows x86

numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded Dec 26, 2022 CPython 3.10 manylinux: glibc 2.17+ x86-64

numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded Dec 26, 2022 CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded Dec 26, 2022 CPython 3.10 macOS 11.0+ ARM64

numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.10 macOS 10.9+ x86-64

numpy-1.24.1-cp39-cp39-win_amd64.whl (14.9 MB view details)

Uploaded Dec 26, 2022 CPython 3.9 Windows x86-64

numpy-1.24.1-cp39-cp39-win32.whl (12.4 MB view details)

Uploaded Dec 26, 2022 CPython 3.9 Windows x86

numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded Dec 26, 2022 CPython 3.9 manylinux: glibc 2.17+ x86-64

numpy-1.24.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded Dec 26, 2022 CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.24.1-cp39-cp39-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded Dec 26, 2022 CPython 3.9 macOS 11.0+ ARM64

numpy-1.24.1-cp39-cp39-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.9 macOS 10.9+ x86-64

numpy-1.24.1-cp38-cp38-win_amd64.whl (14.9 MB view details)

Uploaded Dec 26, 2022 CPython 3.8 Windows x86-64

numpy-1.24.1-cp38-cp38-win32.whl (12.4 MB view details)

Uploaded Dec 26, 2022 CPython 3.8 Windows x86

numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB view details)

Uploaded Dec 26, 2022 CPython 3.8 manylinux: glibc 2.17+ x86-64

numpy-1.24.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.0 MB view details)

Uploaded Dec 26, 2022 CPython 3.8 manylinux: glibc 2.17+ ARM64

numpy-1.24.1-cp38-cp38-macosx_11_0_arm64.whl (13.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.8 macOS 11.0+ ARM64

numpy-1.24.1-cp38-cp38-macosx_10_9_x86_64.whl (19.8 MB view details)

Uploaded Dec 26, 2022 CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file numpy-1.24.1.tar.gz.

File metadata

Download URL: numpy-1.24.1.tar.gz
Upload date: Dec 26, 2022
Size: 10.9 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1.tar.gz
Algorithm	Hash digest
SHA256	`2386da9a471cc00a1f47845e27d916d5ec5346ae9696e01a8a34760858fe9dd2`
MD5	`dd3aaeeada8e95cc2edf9a3a4aa8b5af`
BLAKE2b-256	`ceb8c170db50ec49d5845bd771bc5549fe734ee73083c5c52791915f95d8e2bc`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl.

File metadata

Download URL: numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl
Upload date: Dec 26, 2022
Size: 14.7 MB
Tags: PyPy, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl
Algorithm	Hash digest
SHA256	`cfa1161c6ac8f92dea03d625c2d0c05e084668f4a06568b77a25a89111621566`
MD5	`e85b245c57a10891b3025579bf0cf298`
BLAKE2b-256	`f90394ee2d37561d77538e9f2c933a8b22ff234f15404420517b3f51cc3a0749`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: Dec 26, 2022
Size: 16.7 MB
Tags: PyPy, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`ad2925567f43643f51255220424c23d204024ed428afc5aad0f86f3ffc080086`
MD5	`46f19b4b147f8836c2bd34262fabfffa`
BLAKE2b-256	`494712ef5c22217e16afdf1ba1e7cbf6bc36b5df2e0ddee3f5557bc1e41c9e41`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Upload date: Dec 26, 2022
Size: 19.2 MB
Tags: PyPy, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`ed5fb71d79e771ec930566fae9c02626b939e37271ec285e9efaf1b5d4370e7d`
MD5	`619af9cd4f33b668822ae2350f446a15`
BLAKE2b-256	`01a8de4f84ccbbe0b616b4c36bd74dd21ddcac9f0d69466b91a60e3b8647d5ca`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp311-cp311-win_amd64.whl.

File metadata

Download URL: numpy-1.24.1-cp311-cp311-win_amd64.whl
Upload date: Dec 26, 2022
Size: 14.8 MB
Tags: CPython 3.11, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp311-cp311-win_amd64.whl
Algorithm	Hash digest
SHA256	`de92efa737875329b052982e37bd4371d52cabf469f83e7b8be9bb7752d67e51`
MD5	`ab7caa2c6c20e1fab977e1a94dede976`
BLAKE2b-256	`7339f104eb30cc3da44d1e10622418c5e6eb5ac224f0f20c97dba44cf2de2af9`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp311-cp311-win32.whl.

File metadata

Download URL: numpy-1.24.1-cp311-cp311-win32.whl
Upload date: Dec 26, 2022
Size: 12.4 MB
Tags: CPython 3.11, Windows x86
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp311-cp311-win32.whl
Algorithm	Hash digest
SHA256	`442feb5e5bada8408e8fcd43f3360b78683ff12a4444670a7d9e9824c1817d36`
MD5	`915dfb89054e1631574a22a9b53a2b25`
BLAKE2b-256	`d7184491cefc090909c3615315722fd09864b791c34a1f174845d41716278d23`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: Dec 26, 2022
Size: 17.3 MB
Tags: CPython 3.11, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`0044f7d944ee882400890f9ae955220d29b33d809a038923d88e4e01d652acd9`
MD5	`9e86658a414272f9749bde39344f9b76`
BLAKE2b-256	`db245343241cabd04224e4fc4f2cf12b35146a90a83f53bef9b541c439a7dada`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

Download URL: numpy-1.24.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Upload date: Dec 26, 2022
Size: 14.0 MB
Tags: CPython 3.11, manylinux: glibc 2.17+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm	Hash digest
SHA256	`e274f0f6c7efd0d577744f52032fdd24344f11c5ae668fe8d01aac0422611df1`
MD5	`2a76bd9da8a78b44eb816bd70fa3aee3`
BLAKE2b-256	`85924a280c9d31ec4950b0de759722b9feb9cc9d680726da3578f6b993ae6236`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

Download URL: numpy-1.24.1-cp311-cp311-macosx_11_0_arm64.whl
Upload date: Dec 26, 2022
Size: 13.8 MB
Tags: CPython 3.11, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`28e418681372520c992805bb723e29d69d6b7aa411065f48216d8329d02ba032`
MD5	`50bddb05acd54b4396100a70522496dd`
BLAKE2b-256	`b8a9993477a7d6a3fdb1b7bb2287333d027303b9af7643d90088a4c74a15dc1d`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp311-cp311-macosx_10_9_x86_64.whl
Upload date: Dec 26, 2022
Size: 19.8 MB
Tags: CPython 3.11, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`7094891dcf79ccc6bc2a1f30428fa5edb1e6fb955411ffff3401fb4ea93780a8`
MD5	`8eedcacd6b096a568e4cb393d43b3ae5`
BLAKE2b-256	`cd9b0398b0638ccdda7167d407f50494406560d6e4b7f4e23c33588704e2928b`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp310-cp310-win_amd64.whl.

File metadata

Download URL: numpy-1.24.1-cp310-cp310-win_amd64.whl
Upload date: Dec 26, 2022
Size: 14.8 MB
Tags: CPython 3.10, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp310-cp310-win_amd64.whl
Algorithm	Hash digest
SHA256	`b07b40f5fb4fa034120a5796288f24c1fe0e0580bbfff99897ba6267af42def2`
MD5	`1f3823999fce821a28dee10ac6fdd721`
BLAKE2b-256	`ee70c9055fe381e9e5103222e2f5efeb0cfb4524ab3c7d75b4eedc330380f9f5`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp310-cp310-win32.whl.

File metadata

Download URL: numpy-1.24.1-cp310-cp310-win32.whl
Upload date: Dec 26, 2022
Size: 12.4 MB
Tags: CPython 3.10, Windows x86
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp310-cp310-win32.whl
Algorithm	Hash digest
SHA256	`b31da69ed0c18be8b77bfce48d234e55d040793cebb25398e2a7d84199fbc7e2`
MD5	`b246beb773689d97307f7b4c2970f061`
BLAKE2b-256	`24c144f013eba432b5f18a044b587f96aa76964ea4eacbf512bd6c947a9f78c9`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: Dec 26, 2022
Size: 17.3 MB
Tags: CPython 3.10, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`0e3463e6ac25313462e04aea3fb8a0a30fb906d5d300f58b3bc2c23da6a15398`
MD5	`0bddb527345449df624d3cb9aa0e1b75`
BLAKE2b-256	`3d172cc40e1ed44f37b0bab7d62e0c6ba88362da23f48e52833ffdd1b9dfc220`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

Download URL: numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Upload date: Dec 26, 2022
Size: 14.0 MB
Tags: CPython 3.10, manylinux: glibc 2.17+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm	Hash digest
SHA256	`f1b739841821968798947d3afcefd386fa56da0caf97722a5de53e07c4ccedc7`
MD5	`0c0a3012b438bb455a6c2fadfb1be76a`
BLAKE2b-256	`a0a644d97c9d6ec619f0ff3a5a8471e5a1283a0ff492348214d512a79f32e9e4`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

Download URL: numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl
Upload date: Dec 26, 2022
Size: 13.9 MB
Tags: CPython 3.10, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`b09804ff570b907da323b3d762e74432fb07955701b17b08ff1b5ebaa8cfe6a9`
MD5	`4ebd7af622bf617b4876087e500d7586`
BLAKE2b-256	`395d21ea2da2aa6f419a7e48a582b7f5c99ba62822dcd173a6e5a58b22748a36`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl
Upload date: Dec 26, 2022
Size: 19.8 MB
Tags: CPython 3.10, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`179a7ef0889ab769cc03573b6217f54c8bd8e16cef80aad369e1e8185f994cd7`
MD5	`9e543db90493d6a00939bd54c2012085`
BLAKE2b-256	`6e777b69133bf0f3a6b0000cdb6133ff5292734182ca0cd107ad7ff4c46e7bc1`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp39-cp39-win_amd64.whl.

File metadata

Download URL: numpy-1.24.1-cp39-cp39-win_amd64.whl
Upload date: Dec 26, 2022
Size: 14.9 MB
Tags: CPython 3.9, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp39-cp39-win_amd64.whl
Algorithm	Hash digest
SHA256	`ddc7ab52b322eb1e40521eb422c4e0a20716c271a306860979d450decbb51b8e`
MD5	`fec91d4c85066ad8a93816d71b627701`
BLAKE2b-256	`37155667b269bf2c3473133823733fc0cd8fa44850e4c1d61b45bccc798a3e5a`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp39-cp39-win32.whl.

File metadata

Download URL: numpy-1.24.1-cp39-cp39-win32.whl
Upload date: Dec 26, 2022
Size: 12.4 MB
Tags: CPython 3.9, Windows x86
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp39-cp39-win32.whl
Algorithm	Hash digest
SHA256	`87a118968fba001b248aac90e502c0b13606721b1343cdaddbc6e552e8dfb56f`
MD5	`db339ec0b2693cac2d7cf9ca75c334b1`
BLAKE2b-256	`065cd43e4b9eefc95bed55128cc08c535dfb0047cbeac5b7b3cd835a7a531974`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: Dec 26, 2022
Size: 17.3 MB
Tags: CPython 3.9, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`ef85cf1f693c88c1fd229ccd1055570cb41cdf4875873b7728b6301f12cd05bf`
MD5	`864b159e644848bc25f881907dbcf062`
BLAKE2b-256	`4355fea3342371187dea4044521c0ba82b90fb5a42fb92446be019b316dd3320`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

Download URL: numpy-1.24.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Upload date: Dec 26, 2022
Size: 14.0 MB
Tags: CPython 3.9, manylinux: glibc 2.17+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm	Hash digest
SHA256	`8e669fbdcdd1e945691079c2cae335f3e3a56554e06bbd45d7609a6cf568c700`
MD5	`848ad020bba075ed8f19072c64dcd153`
BLAKE2b-256	`ad9a98490aee9ca665cd04291658dd76e19c9b9d17680404aa9a122d5ef6ff79`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

Download URL: numpy-1.24.1-cp39-cp39-macosx_11_0_arm64.whl
Upload date: Dec 26, 2022
Size: 13.9 MB
Tags: CPython 3.9, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`84e789a085aabef2f36c0515f45e459f02f570c4b4c4c108ac1179c34d475ed7`
MD5	`987f22c49b2be084b5d72f88f347d31e`
BLAKE2b-256	`af74070f80c41427f41a48bd4c873768f4989aacac7b8c0a3060566402339ce9`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp39-cp39-macosx_10_9_x86_64.whl
Upload date: Dec 26, 2022
Size: 19.8 MB
Tags: CPython 3.9, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`28bc9750ae1f75264ee0f10561709b1462d450a4808cd97c013046073ae64ab6`
MD5	`4383c1137f0287df67c364fbdba2bc72`
BLAKE2b-256	`813afaa8aa531ec3001ff3b215892de791142e01516105da4c5e40a5686edca2`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp38-cp38-win_amd64.whl.

File metadata

Download URL: numpy-1.24.1-cp38-cp38-win_amd64.whl
Upload date: Dec 26, 2022
Size: 14.9 MB
Tags: CPython 3.8, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp38-cp38-win_amd64.whl
Algorithm	Hash digest
SHA256	`6ec0c021cd9fe732e5bab6401adea5a409214ca5592cd92a114f7067febcba0c`
MD5	`9e9f1577f874286a8bdff8dc5551eb9f`
BLAKE2b-256	`0b737db81acb8b9b2dfa24ca51de6b84db878fd216865b7acb75f27e79105680`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp38-cp38-win32.whl.

File metadata

Download URL: numpy-1.24.1-cp38-cp38-win32.whl
Upload date: Dec 26, 2022
Size: 12.4 MB
Tags: CPython 3.8, Windows x86
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp38-cp38-win32.whl
Algorithm	Hash digest
SHA256	`dae46bed2cb79a58d6496ff6d8da1e3b95ba09afeca2e277628171ca99b99db1`
MD5	`09b20949ed21683ad7c9cbdf9ebb2439`
BLAKE2b-256	`141f935ce638d37f8762aafb3962c8b14bf715c3db21a9b30f0cec4b228e7387`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: Dec 26, 2022
Size: 17.3 MB
Tags: CPython 3.8, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`b0677a52f5d896e84414761531947c7a330d1adc07c3a4372262f25d84af7bf7`
MD5	`4c32a43bdb85121614ab3e99929e33c7`
BLAKE2b-256	`3b2b75d7ed116b17202a89e6cf1eba7e91ba83abb79ece7924d5b2c820f59025`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

Download URL: numpy-1.24.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Upload date: Dec 26, 2022
Size: 14.0 MB
Tags: CPython 3.8, manylinux: glibc 2.17+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm	Hash digest
SHA256	`caf65a396c0d1f9809596be2e444e3bd4190d86d5c1ce21f5fc4be60a3bc5b36`
MD5	`a96f29bf106a64f82b9ba412635727d1`
BLAKE2b-256	`c5f7df97e91bf7f4125ce7fa24296f4dfb6f1fc172c08413146b456f5b1299f1`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

Download URL: numpy-1.24.1-cp38-cp38-macosx_11_0_arm64.whl
Upload date: Dec 26, 2022
Size: 13.8 MB
Tags: CPython 3.8, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp38-cp38-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`26089487086f2648944f17adaa1a97ca6aee57f513ba5f1c0b7ebdabbe2b9954`
MD5	`58366b1a559baa0547ce976e416ed76d`
BLAKE2b-256	`145ddf640c8bc151c742d5166aecfc394134bf92bba432472bfa7d606badd0fc`

See more details on using hashes here.

File details

Details for the file numpy-1.24.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

Download URL: numpy-1.24.1-cp38-cp38-macosx_10_9_x86_64.whl
Upload date: Dec 26, 2022
Size: 19.8 MB
Tags: CPython 3.8, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for numpy-1.24.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`b162ac10ca38850510caf8ea33f89edcb7b0bb0dfa5592d59909419986b72407`
MD5	`8246de961f813f5aad89bca3d12f81e7`
BLAKE2b-256	`fac200bed438bc58fd80429b7ea2b28382f99156659ebc6dfa750d1520df59d6`

See more details on using hashes here.

numpy 1.24.1

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Code of Conduct

Call for Contributions

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