Skip to main content

Fundamental package for array computing in Python

Project description


Powered by NumFOCUS PyPI Downloads Conda Downloads Stack Overflow Nature Paper OpenSSF Scorecard

NumPy is the fundamental package for scientific computing with Python.

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, sys; sys.exit(numpy.test() is False)"

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


Release history Release notifications | RSS feed

Download files

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

Source Distribution

numpy-1.25.1.tar.gz (10.4 MB view details)

Uploaded Source

Built Distributions

numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl (14.9 MB view details)

Uploaded PyPy Windows x86-64

numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.0 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

numpy-1.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl (19.4 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

numpy-1.25.1-cp311-cp311-win_amd64.whl (15.0 MB view details)

Uploaded CPython 3.11 Windows x86-64

numpy-1.25.1-cp311-cp311-win32.whl (12.6 MB view details)

Uploaded CPython 3.11 Windows x86

numpy-1.25.1-cp311-cp311-musllinux_1_1_x86_64.whl (17.5 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

numpy-1.25.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.6 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

numpy-1.25.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.1 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

numpy-1.25.1-cp311-cp311-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

numpy-1.25.1-cp311-cp311-macosx_10_9_x86_64.whl (20.0 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

numpy-1.25.1-cp310-cp310-win_amd64.whl (15.0 MB view details)

Uploaded CPython 3.10 Windows x86-64

numpy-1.25.1-cp310-cp310-win32.whl (12.6 MB view details)

Uploaded CPython 3.10 Windows x86

numpy-1.25.1-cp310-cp310-musllinux_1_1_x86_64.whl (17.5 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

numpy-1.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

numpy-1.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

numpy-1.25.1-cp310-cp310-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl (20.1 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

numpy-1.25.1-cp39-cp39-win_amd64.whl (15.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

numpy-1.25.1-cp39-cp39-win32.whl (12.6 MB view details)

Uploaded CPython 3.9 Windows x86

numpy-1.25.1-cp39-cp39-musllinux_1_1_x86_64.whl (17.5 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

numpy-1.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

numpy-1.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

numpy-1.25.1-cp39-cp39-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

numpy-1.25.1-cp39-cp39-macosx_10_9_x86_64.whl (20.1 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

File details

Details for the file numpy-1.25.1.tar.gz.

File metadata

  • Download URL: numpy-1.25.1.tar.gz
  • Upload date:
  • Size: 10.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1.tar.gz
Algorithm Hash digest
SHA256 9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf
MD5 768d0ebf15e2242f4c7ca7565bb5dd3e
BLAKE2b-256 cf7af68d1d658a0e68084097beb212fa9356fee7eabff8b57231cc4acb555b12

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 012097b5b0d00a11070e8f2e261128c44157a8689f7dedcf35576e525893f4fe
MD5 95e36689e6dd078caf11e7e2a2d5f5f1
BLAKE2b-256 b866d74ebeff4fbd678d19002f7be03c3f9ab57604e4606d67d9b18431250cc2

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0d3fe3dd0506a28493d82dc3cf254be8cd0d26f4008a417385cbf1ae95b54004
MD5 fbccb20254a2dc85bdec549a03b8eb56
BLAKE2b-256 bd8a7eccc0d8b54c85a0b210a72c3f8be71eecc7db1bf62dcd9275a911282a5a

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 35a9527c977b924042170a0887de727cd84ff179e478481404c5dc66b4170009
MD5 9ba95d8d6004d9659d7728fe93f67be9
BLAKE2b-256 83b876d402c0c09aa08befa84f7a55c714ba4e2d9974134e8881aa42ac46abd5

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: numpy-1.25.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 15.0 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 c40571fe966393b212689aa17e32ed905924120737194b5d5c1b20b9ed0fb171
MD5 87bb1633b2e8029dbfa1e59f7ab22625
BLAKE2b-256 2f2a34fe0b64e78347f4ea128868df0034a97e0f92b476f62947b0976caba820

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-win32.whl.

File metadata

  • Download URL: numpy-1.25.1-cp311-cp311-win32.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 791f409064d0a69dd20579345d852c59822c6aa087f23b07b1b4e28ff5880fcb
MD5 5e84e797866c68ba65fa89a4bf4ba8ce
BLAKE2b-256 286546e9c57b034c63f67d4a76fe335c0204e80d4722e94939de684af0456ef0

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 38eb6548bb91c421261b4805dc44def9ca1a6eef6444ce35ad1669c0f1a3fc5d
MD5 4eb459c3d9479c4da2fdf20e4c4085d0
BLAKE2b-256 55eea9cb689bab52cb617094148a2db7f87f89fb390682392545e8751a9cf06b

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d5154b1a25ec796b1aee12ac1b22f414f94752c5f94832f14d8d6c9ac40bcca6
MD5 61dfd7c00638e83a7af59b86615ee9d2
BLAKE2b-256 f05a9b7b7bae29f9f5f8a976607cd30139c1fec9076c0e65ea918d3400924acf

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 41a56b70e8139884eccb2f733c2f7378af06c82304959e174f8e7370af112e09
MD5 20d04dccd2bfca5cfd88780d1dc9a3f8
BLAKE2b-256 7cbe76ae69de42c60e9d14252df2191db298e955223853dff6a77c9d1e01cd71

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e8f6049c4878cb16960fbbfb22105e49d13d752d4d8371b55110941fb3b17800
MD5 099f74d654888869704469c321af845d
BLAKE2b-256 4873df07644e8fa1127a7985db70cf1d07123004e2dd7a3cf33e8b83297a775b

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6b82655dd8efeea69dbf85d00fca40013d7f503212bc5259056244961268b66e
MD5 f31b059256ae09b7b83df63f52d8371e
BLAKE2b-256 e37e0b072c21f4feefb2d89600956af307db29fb7df695cbe6e145de91643155

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpy-1.25.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 15.0 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c1516db588987450b85595586605742879e50dcce923e8973f79529651545b57
MD5 5466bebeaafcc3d6e1b98858d77ff945
BLAKE2b-256 8d07cab4129005dba3f170dd1a2cfa312fc71100f0e26b91f7fc659b6b5abbdc

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-win32.whl.

File metadata

  • Download URL: numpy-1.25.1-cp310-cp310-win32.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 fd67b306320dcadea700a8f79b9e671e607f8696e98ec255915c0c6d6b818503
MD5 ab8ecd125ca86eac0b3ada67ab66dad6
BLAKE2b-256 2868a4594268ecddf860de11fa541ee789e9c2d23b3854f9f04d308960768c60

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 0def91f8af6ec4bb94c370e38c575855bf1d0be8a8fbfba42ef9c073faf2cf19
MD5 e81f6264aecfa2269c5d29d10c362cbc
BLAKE2b-256 e9ce27f1961d50170ab38fd739263465437fb193404dc8fac7dd0b3bb146c95a

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6c6c9261d21e617c6dc5eacba35cb68ec36bb72adcff0dee63f8fbc899362588
MD5 6a62d7a6cee310b41dc872aa7f3d7e8b
BLAKE2b-256 d055559e6f455a066e12058330377259a106b7fefa41c15dbdb1b71070cec429

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4a90725800caeaa160732d6b31f3f843ebd45d6b5f3eec9e8cc287e30f2805bf
MD5 1007893b1a8bfd97d445a63d29d33642
BLAKE2b-256 3c8190d13a812268943226a9ca8d4967343f9e273a5d9a1063f8a99736816eba

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d736b75c3f2cb96843a5c7f8d8ccc414768d34b0a75f466c05f3a739b406f10b
MD5 d5b8d3b0424e2af41018f35a087c4500
BLAKE2b-256 1bcd9e8313ffd849626c836fffd7881296a74f53a7739bd9ce7a6e22b1fc843b

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 77d339465dff3eb33c701430bcb9c325b60354698340229e1dff97745e6b3efa
MD5 d09d98643db31e892fad11b8c2b7af22
BLAKE2b-256 fa07c6980120967a9fc76138eddd583d6ac47dd072922d6f66d7d502f6f9a964

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy-1.25.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 15.1 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 1d5d3c68e443c90b38fdf8ef40e60e2538a27548b39b12b73132456847f4b631
MD5 838e97b751bebadf47e2196b2c88ffa2
BLAKE2b-256 86c7f92afdefa2bccdf0bc357321a931adafb1a999bc84f8877a6ed786a69ccc

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy-1.25.1-cp39-cp39-win32.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for numpy-1.25.1-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 247d3ffdd7775bdf191f848be8d49100495114c82c2bd134e8d5d075fb386a1c
MD5 7e5bed491b85f0d7c718d6809f9b3ed2
BLAKE2b-256 94832473c27e3f52339d1278d7b02245211d1ecf9c5eb2d97cc17370bf59ef10

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 f76aebc3358ade9eacf9bc2bb8ae589863a4f911611694103af05346637df1b7
MD5 7d9d1ae23cf5420652088bfe8e048d89
BLAKE2b-256 99087a793f0ad404a0d9f03debeb82cf9039b8ce60d5a6631b35f4991f6fc93a

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 20e1266411120a4f16fad8efa8e0454d21d00b8c7cee5b5ccad7565d95eb42dd
MD5 5cbb4c2f2892fafdf6f34fcb37c9e743
BLAKE2b-256 219c1a7d658112aed97be36dfd93cc7362ca6c33598615988e6d836fa0a80f89

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d412c1697c3853c6fc3cb9751b4915859c7afe6a277c2bf00acf287d56c4e625
MD5 5b457e10834c991bca84aae7eaa49f34
BLAKE2b-256 0a54370ec143539de0dc09c3024b4fea99e76e3051bd6ad199ccf1e5b6bc05b7

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1a180429394f81c7933634ae49b37b472d343cccb5bb0c4a575ac8bbc433722f
MD5 d71e1cbe18fe05944219e5a5be1796bf
BLAKE2b-256 dd68ee92adfe1cadfc36f5bb7301eae5c8ad8138ec4dae83d68014f2a7f3709f

See more details on using hashes here.

File details

Details for the file numpy-1.25.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.25.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3d7abcdd85aea3e6cdddb59af2350c7ab1ed764397f8eec97a038ad244d2d105
MD5 3fcf2eb5970d848a26abdff1b10228e7
BLAKE2b-256 0623883340d4a8ff93ab24beb5a25c53f5999e0016c20ed2171853bbd2cdf9f4

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