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

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for matthew.brett from gravatar.com matthew.brett Avatar for mattip from gravatar.com mattip Avatar for rgommers from gravatar.com rgommers Avatar for teoliphant from gravatar.com teoliphant

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

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.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for charlesr.harris from gravatar.com charlesr.harris Avatar for matthew.brett from gravatar.com matthew.brett Avatar for mattip from gravatar.com mattip Avatar for rgommers from gravatar.com rgommers Avatar for teoliphant from gravatar.com teoliphant

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

2.1.3

2.1.2

2.1.1

2.1.0

2.1.0rc1 pre-release

2.0.2

2.0.1

2.0.0

1.26.4

1.26.3

1.26.2

1.26.1

1.26.0

1.25.2

1.25.1

1.25.0

1.24.4

1.24.3

1.24.2

1.24.1

1.24.0

1.23.5

1.23.4

1.23.3

1.23.2

1.23.1

1.23.0

1.22.4

1.22.3

1.22.2

1.22.1

1.22.0

1.21.6

1.21.5

1.21.4

1.21.3

1.21.2

1.21.1

1.21.0

1.20.3

1.20.2

1.20.1

1.20.0

1.19.5

1.19.4

1.19.3

1.19.2

1.19.1

1.19.0

1.18.5

1.18.4

1.18.3

1.18.2

1.18.1

1.18.0

1.17.5

1.17.4

1.17.3

1.17.2

1.17.1

1.17.0

1.16.6

1.16.5

1.16.4

1.16.3

1.16.2

1.16.1

1.16.0

1.15.4

1.15.3

1.15.2

1.15.1

1.15.0

1.14.6

1.14.5

1.14.4

1.14.3

1.14.2

1.14.1

1.14.0

1.13.3

1.13.1

1.13.0

1.12.1

1.12.0

1.11.3

This version

1.11.2

1.11.1

1.11.0

1.10.4

1.10.3

1.10.2

1.10.1

1.10.0

1.9.3

1.9.2

1.9.1

1.9.0

1.8.2

1.8.1

1.8.0

1.7.2

1.7.1

1.7.0

1.6.2

1.6.1

1.6.0

1.5.1

1.5.0

1.4.1

1.4.0

1.3.0

1.2.1

1.2.0

1.1.1

1.0.4

1.0.3

1.0

1.0rc3 pre-release

1.0rc2 pre-release

1.0rc1 pre-release

1.0b5 pre-release

1.0b4 pre-release

1.0b1 pre-release

0.9.8

0.9.6

Download files

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

Source Distributions

numpy-1.11.2.zip (4.7 MB view details)

Uploaded Source

numpy-1.11.2.tar.gz (4.2 MB view details)

Uploaded Source

Built Distributions

numpy-1.11.2-cp35-none-win_amd64.whl (7.6 MB view details)

Uploaded CPython 3.5 Windows x86-64

numpy-1.11.2-cp35-none-win32.whl (6.6 MB view details)

Uploaded CPython 3.5 Windows x86

numpy-1.11.2-cp35-cp35m-manylinux1_x86_64.whl (15.6 MB view details)

Uploaded CPython 3.5m

numpy-1.11.2-cp35-cp35m-manylinux1_i686.whl (11.7 MB view details)

Uploaded CPython 3.5m

numpy-1.11.2-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 (3.9 MB view details)

Uploaded 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-1.11.2-cp34-none-win_amd64.whl (7.4 MB view details)

Uploaded CPython 3.4 Windows x86-64

numpy-1.11.2-cp34-none-win32.whl (6.5 MB view details)

Uploaded CPython 3.4 Windows x86

numpy-1.11.2-cp34-cp34m-manylinux1_x86_64.whl (15.6 MB view details)

Uploaded CPython 3.4m

numpy-1.11.2-cp34-cp34m-manylinux1_i686.whl (11.7 MB view details)

Uploaded CPython 3.4m

numpy-1.11.2-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 (3.9 MB view details)

Uploaded 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-1.11.2-cp27-none-win_amd64.whl (7.4 MB view details)

Uploaded CPython 2.7 Windows x86-64

numpy-1.11.2-cp27-none-win32.whl (6.5 MB view details)

Uploaded CPython 2.7 Windows x86

numpy-1.11.2-cp27-cp27mu-manylinux1_x86_64.whl (15.3 MB view details)

Uploaded CPython 2.7mu

numpy-1.11.2-cp27-cp27mu-manylinux1_i686.whl (11.5 MB view details)

Uploaded CPython 2.7mu

numpy-1.11.2-cp27-cp27m-manylinux1_x86_64.whl (15.3 MB view details)

Uploaded CPython 2.7m

numpy-1.11.2-cp27-cp27m-manylinux1_i686.whl (11.5 MB view details)

Uploaded CPython 2.7m

numpy-1.11.2-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 (3.9 MB view details)

Uploaded 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-1.11.2.zip.

File metadata

  • Download URL: numpy-1.11.2.zip
  • Upload date:
  • Size: 4.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.11.2.zip
Algorithm Hash digest
SHA256 c1ed4d1d2a795409c7df1eb4bfee65c0e3326cfc7c57875fa39e5c7414116d9a
MD5 8308cc97be154d2f64a2387ea863c2ac
BLAKE2b-256 b6794d453f11184375eb8ce0ffbe47f06fa5de7fa987fe542ad97277487fbdc5

See more details on using hashes here.

File details

Details for the file numpy-1.11.2.tar.gz.

File metadata

  • Download URL: numpy-1.11.2.tar.gz
  • Upload date:
  • Size: 4.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.11.2.tar.gz
Algorithm Hash digest
SHA256 04db2fbd64e2e7c68e740b14402b25af51418fc43a59d9e54172b38b906b0f69
MD5 03bd7927c314c43780271bf1ab795ebc
BLAKE2b-256 16f5b432f028134dd30cfbf6f21b8264a9938e5e0f75204e72453af08d67eb0b

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp35-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 ae5385f4a3255f183ff57dd56b316e3e5294772c2ea49e132df8aaafc9639db5
MD5 3c8ea6c25102de3879f81144c9af738a
BLAKE2b-256 b82ab11d995cc311b292a2d9e606784f7d0b38a7fc6959baf511e1357836f372

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp35-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp35-none-win32.whl
Algorithm Hash digest
SHA256 fb409d73e35f36bb212b4b2f54e7b6783bd4e088b5593f712689b47c72af9d89
MD5 e485e06907826af5e1fc88608d0629a2
BLAKE2b-256 d73cd8b473b517062cc700575889d79e7444c9b54c6072a22189d1831d2fbbce

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 29fe03fb1b68b845d493e3f905650044f2b4ea3d875b9ed36f5c568e7b18bb2f
MD5 8ed38d63da1ca02336048d914518b763
BLAKE2b-256 a8cd78e75f6f81c81d8a2de5b1b2b354ce5495dd99e2ad07cd54875001bf393d

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp35-cp35m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 a9ae4a0aa538c1fb64e25c9f9b2b04464a28425c1f0ebc1305ff98f62ddc7b32
MD5 2373f413ce8f1f07551d6266560c4059
BLAKE2b-256 d3d220a47b28465f6d2388cba511b2d54ed1cea190cd2e06835c9128f93ecab3

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-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

File hashes

Hashes for numpy-1.11.2-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 2b05451d1f1c237db09990eec032990fbee43d9f65503f09b3f5edcf6fe4093d
MD5 60ed84fc70157a4df4c1fa45964c0c5a
BLAKE2b-256 3fc9bc8aa4fd6b5f6e38509a882269357115cac58aae302d853bf3c53c324429

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 420985a52f6660c94f460413b57ab52fb47c8e2e283c473de91443c0ed76ac11
MD5 1364b74372853523650774c508705425
BLAKE2b-256 6e5cfc31c6118735c15c25a22f40d55be4a5591dabe1af8ce0033ace00a183e3

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp34-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp34-none-win32.whl
Algorithm Hash digest
SHA256 848a6496069794637f502345c7dba1309688d8d4d9264b3938df19c7f8dd51a3
MD5 532f1b0a76ca4157e897cf823b06cc3b
BLAKE2b-256 73f8d67e90ae8f4f302fe32442567586e45b4d87fed907914af0602f4cc7d2ee

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b133ed34bd14cb3d7e533d42f03c5bd82bfe0ca60908da0d3e5d7906321ffa36
MD5 3e4e1e0622dcf0b47c2d55cd8056444e
BLAKE2b-256 8f73c09caf7e2c95b498943d9fd5c8d93653e3db5bdf54a5aa22cef328870090

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp34-cp34m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 d42a7cbddacf36c83f8ae00f8d7f110e5601ae79062fad2fcce9c8d1c17a3b26
MD5 f02d8a5f4369e1de3d6ba72ccd7d30a1
BLAKE2b-256 65f6c88bd2e9cbecf791c422f2639fd9beee55c29360389d1d2fb06c2d369da1

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-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

File hashes

Hashes for numpy-1.11.2-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 556a3ba02f0484584e6b9ada7b931b3edf9f19fc114da311f88b3123f16587e2
MD5 ac95721011d2f2fa2afb4bf434202ad5
BLAKE2b-256 200e12e6695a25911b6dc6e5bbe1e34d94e4af9f530431b6a36def06b877a5ea

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 0d977fb8b9594dff18759ff901135626c82b8b4a6327ab6dce6a3723b3cb920a
MD5 986864f9c25a405f0f1edb75fea375d5
BLAKE2b-256 5ec3101dec35a5154235d4c13b362417321ab0049ccef53694680f2b811ae6bd

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp27-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp27-none-win32.whl
Algorithm Hash digest
SHA256 a842b8a3c8384aa2f9af724c4c2db18fd4e08139cfd6f77ca8ce189025e90111
MD5 49093e4ce4cca97b19ed2c6254ea1452
BLAKE2b-256 2783ee7b63911b8e9a6aa97685b98d025dc1391835f11831d9820b2a79748867

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 659c49cd031ed50caa9b3aa0eba82942f8fa183da51b50caecdd7fb93f9ca33f
MD5 fa62a11922a9e0776963508fb5254d3d
BLAKE2b-256 5ed53433e015f3e4a1f309dbb110e8557947f68887fe9b8438d50a4b7790a954

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp27-cp27mu-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 9a9bfdd0eadc59335ac7cfc7df955e579220719c1e4751f3f0c9c91b6ab4b2f7
MD5 40b8393c40ce667ea84827bd688a8b86
BLAKE2b-256 7ee3bbe812d3136edacbf19fa8f883c30bdf08e739704d80f70960bb39c5ca7b

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 5c5bca330229ea1e33f9170ffee2a9aa60adf0044e9d5446679f5937767f49c6
MD5 0d07d3aeffb6c28ab1f7c769c5b57e6d
BLAKE2b-256 90fd0343cee0c84f9975bea5020086332dafd88438c73ec281f9185f98c53dd0

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-cp27-cp27m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy-1.11.2-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 b88b880e1dbbc99996bbdb90e2e8ea4800f1273025488a042e549735c7c3eb14
MD5 a132e6fddaab3c90ff7d0af713d1e624
BLAKE2b-256 1f68f4fa60a086cec1f97291f2295f9028b2fb0c46383e46043cd559f4fdcff0

See more details on using hashes here.

File details

Details for the file numpy-1.11.2-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

File hashes

Hashes for numpy-1.11.2-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 a28fae3ce1c174a5ca97ac31046034772848b31933de7fb94aadaa7d45fb7c48
MD5 c79f2631c09ba97175d8c515a8e8e76f
BLAKE2b-256 2acaa6fa2ccd56b16052ba2e73eff4ffe9cf17b2d5d39567a722497d05285736

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