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

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

This version

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-1.6.0.zip (3.4 MB view details)

Uploaded Source

numpy-1.6.0.tar.gz (2.6 MB view details)

Uploaded Source

Built Distributions

numpy-1.6.0.win32-py3.2.exe (2.6 MB view details)

Uploaded Source

numpy-1.6.0.win32-py3.1.exe (2.6 MB view details)

Uploaded Source

numpy-1.6.0.win32-py2.7.exe (2.6 MB view details)

Uploaded Source

numpy-1.6.0.win32-py2.6.exe (2.6 MB view details)

Uploaded Source

numpy-1.6.0.win32-py2.5.exe (2.5 MB view details)

Uploaded Source

numpy-1.6.0-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.whl (11.6 MB view details)

Uploaded CPython 2.7 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

numpy-1.6.0-cp27-cp27mu-manylinux1_x86_64.whl (13.6 MB view details)

Uploaded CPython 2.7mu

numpy-1.6.0-cp27-cp27m-manylinux1_x86_64.whl (13.6 MB view details)

Uploaded CPython 2.7m

numpy-1.6.0-cp26-cp26mu-manylinux1_x86_64.whl (13.6 MB view details)

Uploaded CPython 2.6mu

numpy-1.6.0-cp26-cp26m-manylinux1_x86_64.whl (13.6 MB view details)

Uploaded CPython 2.6m

File details

Details for the file numpy-1.6.0.zip.

File metadata

  • Download URL: numpy-1.6.0.zip
  • Upload date:
  • Size: 3.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.6.0.zip
Algorithm Hash digest
SHA256 2e2eb61f97168165a0a8aa650c5f206f5db6e7d05bb6bb996065b8e2c1bf541b
MD5 f0ce7ea1a12b3b3480571980af243e48
BLAKE2b-256 1e03ada8e8080de8a4d488709ba721396e9eb103906fcb62f6128edec56b09ed

See more details on using hashes here.

File details

Details for the file numpy-1.6.0.tar.gz.

File metadata

  • Download URL: numpy-1.6.0.tar.gz
  • Upload date:
  • Size: 2.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.6.0.tar.gz
Algorithm Hash digest
SHA256 687dcfb5f6a51f2107ba1dc8bb324fd1a0146ce8b0e2bd01a3ee7bcc453ee3d8
MD5 e0993c74cb8e83292e560eac1a9be8e9
BLAKE2b-256 5a6f85439fe9c58d16c477f8bb767da9b55c0721adfb940ace2e4240793af70d

See more details on using hashes here.

File details

Details for the file numpy-1.6.0.win32-py3.2.exe.

File metadata

File hashes

Hashes for numpy-1.6.0.win32-py3.2.exe
Algorithm Hash digest
SHA256 334d963c82498c6591f2a392f468111a3cab5bd786595a3b47b6f8a1497d221a
MD5 7863302af8928fb345c420c6af136197
BLAKE2b-256 5df05df2f4ea6837fecae474768d87afcf225353c3c9ea7bb9128f6d2144655a

See more details on using hashes here.

File details

Details for the file numpy-1.6.0.win32-py3.1.exe.

File metadata

File hashes

Hashes for numpy-1.6.0.win32-py3.1.exe
Algorithm Hash digest
SHA256 a080464d897e80e17c9c878831fe149ab435c37cc2060a229921c43226dc08c7
MD5 917c6b217b3867fe2cbdb788e4e6bb32
BLAKE2b-256 c2945ff07d1d8dd7bea8289785f31428fdf157a6aa6de47f5a99a4a9bca64785

See more details on using hashes here.

File details

Details for the file numpy-1.6.0.win32-py2.7.exe.

File metadata

File hashes

Hashes for numpy-1.6.0.win32-py2.7.exe
Algorithm Hash digest
SHA256 2a3e164b3c4659e2652ca6237b2fb9b40cc59c551187ebff49d8b94d22bfd554
MD5 6f5266d348e5f4d1471a6ae66c26438d
BLAKE2b-256 046c737e537c83e9345cafd4d56b3a6902ce24df95d35156d774d5eb17142ccd

See more details on using hashes here.

File details

Details for the file numpy-1.6.0.win32-py2.6.exe.

File metadata

File hashes

Hashes for numpy-1.6.0.win32-py2.6.exe
Algorithm Hash digest
SHA256 6a0154532e1b0b4b4e480de4b481e44d65d4d2ed88eff11e33d76f13fed20149
MD5 e09cd07ba120ed9c84b85c7a188b3bce
BLAKE2b-256 8aff2da6ab3812c1f9bc70f093f0993ac6d1a8fb594c43ed3fb70156a92b4d6c

See more details on using hashes here.

File details

Details for the file numpy-1.6.0.win32-py2.5.exe.

File metadata

File hashes

Hashes for numpy-1.6.0.win32-py2.5.exe
Algorithm Hash digest
SHA256 915d0d963f788ef3916949e956f0baee6ec3de16d3bbf286c6ddfcb9d6e51c7a
MD5 539782c7311d4a3379f66a964159ef11
BLAKE2b-256 b0a5ba8a8f45c0f0f22ce6d27e5b0b2ec1cd8ed71d960821a65fcb2adc6c6695

See more details on using hashes here.

File details

Details for the file numpy-1.6.0-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.6.0-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c03cdf0b7f6faa9a70672c7d16939c99634d170c7d74b4043dd50c69253f3285
MD5 90102d5dc2e20b079200999a1f95f613
BLAKE2b-256 1c57ef71f2426816c2303b9a64c60485f341d5b146e39b5cb77fdbac0d6f8603

See more details on using hashes here.

File details

Details for the file numpy-1.6.0-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.6.0-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 f90111b8bd18672e63bdcf3d4e2988e2d51a2c393d65a28417cbd423062a8c5e
MD5 9ced3a92c46a1419cd45cf29fcdec14d
BLAKE2b-256 51e1646c30422256ef8d350c70d72499bba1c5aa19b56939601816757b4934a7

See more details on using hashes here.

File details

Details for the file numpy-1.6.0-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.6.0-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 3b6ccb9cad2106b0d543314f9e297d10d92994b21342d4bae2e9c7c1640bb172
MD5 9ade5b0ed2c0ed57150d4422ebafceab
BLAKE2b-256 3616de492dda22c95137ca4cef59e8ce354c4c1b8e752326b0eb6dedad2cb8ba

See more details on using hashes here.

File details

Details for the file numpy-1.6.0-cp26-cp26mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.6.0-cp26-cp26mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 23f974e57eafb60580289642ec5e598d6b5abfbcd0f1873c49293cf090a99fdb
MD5 90e2211889b4e553f17bfe18003670fc
BLAKE2b-256 3bddd1b1fdd0082ac250b0c54033c8ca49501209dd43b8d4f6fe5ec7b60825db

See more details on using hashes here.

File details

Details for the file numpy-1.6.0-cp26-cp26m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.6.0-cp26-cp26m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 fa8a1ff52f04fdafbc1313539bf2550fccc81753857160725a8df5f1ee7ce313
MD5 85ae66ba2b6fcb5f362384131c62810d
BLAKE2b-256 2217606397a90ea8ec590b92c793c1f130b41a3871ac0f867bfc81734c53d381

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