Skip to main content

Fundamental algorithms for scientific computing in Python

Project description

https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue

SciPy (pronounced “Sigh Pie”) is an open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.

SciPy is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines, such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!

For the installation instructions, see our install guide.

Call for Contributions

We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as “good first issue” may be a good starting point. Have a look at our contributing guide.

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

  • review pull requests

  • triage issues

  • develop tutorials, presentations, and other educational materials

  • maintain and improve our website

  • develop graphic design for our brand assets and promotional materials

  • help with outreach and onboard new contributors

  • write grant proposals and help with other fundraising efforts

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 leaving a comment on a relevant issue that is already open.

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

scipy-1.11.4.tar.gz (56.3 MB view details)

Uploaded Source

Built Distributions

scipy-1.11.4-cp312-cp312-win_amd64.whl (43.7 MB view details)

Uploaded CPython 3.12 Windows x86-64

scipy-1.11.4-cp312-cp312-musllinux_1_1_x86_64.whl (36.0 MB view details)

Uploaded CPython 3.12 musllinux: musl 1.1+ x86-64

scipy-1.11.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

scipy-1.11.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (32.3 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ ARM64

scipy-1.11.4-cp312-cp312-macosx_12_0_arm64.whl (29.6 MB view details)

Uploaded CPython 3.12 macOS 12.0+ ARM64

scipy-1.11.4-cp312-cp312-macosx_10_9_x86_64.whl (37.1 MB view details)

Uploaded CPython 3.12 macOS 10.9+ x86-64

scipy-1.11.4-cp311-cp311-win_amd64.whl (44.1 MB view details)

Uploaded CPython 3.11 Windows x86-64

scipy-1.11.4-cp311-cp311-musllinux_1_1_x86_64.whl (36.6 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

scipy-1.11.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

scipy-1.11.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (32.9 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

scipy-1.11.4-cp311-cp311-macosx_12_0_arm64.whl (29.7 MB view details)

Uploaded CPython 3.11 macOS 12.0+ ARM64

scipy-1.11.4-cp311-cp311-macosx_10_9_x86_64.whl (37.2 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

scipy-1.11.4-cp310-cp310-win_amd64.whl (44.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

scipy-1.11.4-cp310-cp310-musllinux_1_1_x86_64.whl (36.6 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

scipy-1.11.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

scipy-1.11.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (32.9 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

scipy-1.11.4-cp310-cp310-macosx_12_0_arm64.whl (29.8 MB view details)

Uploaded CPython 3.10 macOS 12.0+ ARM64

scipy-1.11.4-cp310-cp310-macosx_10_9_x86_64.whl (37.3 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

scipy-1.11.4-cp39-cp39-win_amd64.whl (44.3 MB view details)

Uploaded CPython 3.9 Windows x86-64

scipy-1.11.4-cp39-cp39-musllinux_1_1_x86_64.whl (36.8 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

scipy-1.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

scipy-1.11.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (33.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

scipy-1.11.4-cp39-cp39-macosx_12_0_arm64.whl (29.7 MB view details)

Uploaded CPython 3.9 macOS 12.0+ ARM64

scipy-1.11.4-cp39-cp39-macosx_10_9_x86_64.whl (37.3 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

File details

Details for the file scipy-1.11.4.tar.gz.

File metadata

  • Download URL: scipy-1.11.4.tar.gz
  • Upload date:
  • Size: 56.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for scipy-1.11.4.tar.gz
Algorithm Hash digest
SHA256 90a2b78e7f5733b9de748f589f09225013685f9b218275257f8a8168ededaeaa
MD5 0e8eea0f7c4fe7d8102366230c96d66d
BLAKE2b-256 6e1f91144ba78dccea567a6466262922786ffc97be1e9b06ed9574ef0edc11e1

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: scipy-1.11.4-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 43.7 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for scipy-1.11.4-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 36750b7733d960d7994888f0d148d31ea3017ac15eef664194b4ef68d36a4a97
MD5 eae94466eb0c36b61db723b5cd835fec
BLAKE2b-256 c6a1357e4cd43af2748e1e0407ae0e9a5ea8aaaa6b702833c81be11670dcbad8

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp312-cp312-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp312-cp312-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 ce7fff2e23ab2cc81ff452a9444c215c28e6305f396b2ba88343a567feec9660
MD5 30e84671d615659f0041a47f6aee5105
BLAKE2b-256 00deb9f6938090c37b5092969ba1c67118e9114e8e6ef9d197251671444e839c

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ad669df80528aeca5f557712102538f4f37e503f0c5b9541655016dd0932ca79
MD5 13794337c432999fa37f8cf0280b08f8
BLAKE2b-256 0877f90f7306d755ac68bd159c50bb86fffe38400e533e8c609dd8484bd0f172

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b030c6674b9230d37c5c60ab456e2cf12f6784596d15ce8da9365e70896effc4
MD5 b8526ce9d78ee7629fa1c00733233a01
BLAKE2b-256 479b62d0ec086dd2871009da8769c504bec6e39b80f4c182c6ead0fcebd8b323

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp312-cp312-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp312-cp312-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 2c6ff6ef9cc27f9b3db93a6f8b38f97387e6e0591600369a297a50a8e96e835d
MD5 f61c07733fcd8747fa3212295bf639e7
BLAKE2b-256 5e43abf331745a7e5f4af51f13d40e2a72f516048db41ecbcf3ac6f86ada54a3

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 028eccd22e654b3ea01ee63705681ee79933652b2d8f873e7949898dda6d11b6
MD5 6b2eeebed81a8aba66d703d785b562e1
BLAKE2b-256 df648a690570485b636da614acff35fd725fcbc487f8b1fa9bdb12871b77412f

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: scipy-1.11.4-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 44.1 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for scipy-1.11.4-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 acf8ed278cc03f5aff035e69cb511741e0418681d25fbbb86ca65429c4f4d9cd
MD5 fc3aade39fda56dbcd07106bd4b26c84
BLAKE2b-256 43d0f3cd75b62e1b90f48dbf091261b2fc7ceec14a700e308c50f6a69c83d337

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 5e347b14fe01003d3b78e196e84bd3f48ffe4c8a7b8a1afbcb8f5505cb710993
MD5 340b520348315d183455e7b79fc0c1d3
BLAKE2b-256 8886827b56aea1ed04adbb044a675672a73c84d81076a350092bbfcfc1ae723b

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 530f9ad26440e85766509dbf78edcfe13ffd0ab7fec2560ee5c36ff74d6269ff
MD5 00fe7507f91e11730ed6f48d5881fd74
BLAKE2b-256 6bd4d62ce38ba00dc67d7ec4ec5cc19d36958d8ed70e63778715ad626bcbc796

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 00150c5eae7b610c32589dda259eacc7c4f1665aedf25d921907f4d08a951b1c
MD5 617c587f9442fa26fb36876efefdb311
BLAKE2b-256 752ea781862190d0e7e76afa74752ef363488a9a9d6ea86e46d5e5506cee8df6

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp311-cp311-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp311-cp311-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 1b7c3dca977f30a739e0409fb001056484661cb2541a01aba0bb0029f7b68db8
MD5 c6d0bfc7b2e57822de9249f468e234e6
BLAKE2b-256 4b4820e77ddb1f473d4717a7d4d3fc8d15557f406f7708496054c59f635b7734

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f313b39a7e94f296025e3cffc2c567618174c0b1dde173960cf23808f9fae4be
MD5 955a224e94e29e03d0c3cfda7147d756
BLAKE2b-256 b8f21aefbd5e54ebd8c6163ccf7f73e5d17bc8cb38738d312befc524fce84bb4

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: scipy-1.11.4-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 44.1 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for scipy-1.11.4-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6550466fbeec7453d7465e74d4f4b19f905642c89a7525571ee91dd7adabb5a3
MD5 9b9fa69812be52fc1da643d4dcab2abf
BLAKE2b-256 f8ecb46756f80e3f4c5f0989f6e4492c2851f156d9c239d554754a3c8cffd4e2

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 8fce70f39076a5aa62e92e69a7f62349f9574d8405c0a5de6ed3ef72de07f446
MD5 517f82f0b581aa34630158591377b127
BLAKE2b-256 696030a9c3fbe5066a3a93eefe3e2d44553df13587e6f792e1bff20dfed3d17e

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 933baf588daa8dc9a92c20a0be32f56d43faf3d1a60ab11b3f08c356430f6e56
MD5 e61c5838aa069eddaa1e71d8d9d85509
BLAKE2b-256 e09e80e2205d138960a49caea391f3710600895dd8292b6868dc9aff7aa593f9

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b9999c008ccf00e8fbcce1236f85ade5c569d13144f77a1946bef8863e8f6eb4
MD5 198cdbc80b37bc4eb262b4478f8bab52
BLAKE2b-256 13e58012be7857db6cbbbdbeea8a154dbacdfae845e95e1e19c028e82236d4a0

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp310-cp310-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp310-cp310-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 cf00bd2b1b0211888d4dc75656c0412213a8b25e80d73898083f402b50f47e41
MD5 67d95164f2366ff0efdff3dd25313e8e
BLAKE2b-256 de0d4fa68303568c70fd56fbf40668b6c6807cfee4cad975f07d80bdd26d013e

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 bc9a714581f561af0848e6b69947fda0614915f072dfd14142ed1bfe1b806710
MD5 b99a5cf10c6a76d2d87ce59d4f87b811
BLAKE2b-256 34c6a32add319475d21f89733c034b99c81b3a7c6c7c19f96f80c7ca3ff1bbd4

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: scipy-1.11.4-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 44.3 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for scipy-1.11.4-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 ee410e6de8f88fd5cf6eadd73c135020bfbbbdfcd0f6162c36a7638a1ea8cc65
MD5 af9246a44e37b2ecfb7370eeb55bd84c
BLAKE2b-256 aca08b8e5495ba759f99ec99d90973d481e8a6682c320fcf875b4f084591f4d8

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 6df1468153a31cf55ed5ed39647279beb9cfb5d3f84369453b49e4b8502394fd
MD5 700b898a5a373cefdc76954282c66bab
BLAKE2b-256 58b5c3fb087664b757be3f5501129f0ece9755c5b4ed77590d6520032d25a96f

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 91af76a68eeae0064887a48e25c4e616fa519fa0d38602eda7e0f97d65d57937
MD5 07695f6f859e93e736008d592cf2080e
BLAKE2b-256 db86bf3f01f003224c00dd94d9443d676023ed65d63ea2e34356888dc7fa8f48

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d10e45a6c50211fe256da61a11c34927c68f277e03138777bdebedd933712fea
MD5 96c002ce95d2cc8e16c81bfe9746198b
BLAKE2b-256 a0c31e498aa3d35ccfdf26c0fe81ebc52c540c454377e2690fc3738aabacaf8d

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp39-cp39-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp39-cp39-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 f3cd9e7b3c2c1ec26364856f9fbe78695fe631150f94cd1c22228456404cf1ec
MD5 750ff6a0490796885a159a0aa338072b
BLAKE2b-256 d13a0ab839bb67043ab35e5dcf8b611ca9e08e5a8933b0bc7506eedcec664aae

See more details on using hashes here.

File details

Details for the file scipy-1.11.4-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scipy-1.11.4-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6e619aba2df228a9b34718efb023966da781e89dd3d21637b27f2e54db0410d7
MD5 616e80dff4d6590aba7096ad1dcccb31
BLAKE2b-256 c5e09872b7923c0ff7a420af8f559d0f5c6831143477b4ce57afe1b2a7c59a63

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