Skip to main content

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

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.

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 Distributions

numpy-1.11.0.zip (4.7 MB view details)

Uploaded Source

numpy-1.11.0.tar.gz (4.2 MB view details)

Uploaded Source

Built Distributions

numpy-1.11.0-cp35-none-win_amd64.whl (7.6 MB view details)

Uploaded CPython 3.5 Windows x86-64

numpy-1.11.0-cp35-none-win32.whl (6.6 MB view details)

Uploaded CPython 3.5 Windows x86

numpy-1.11.0-cp35-cp35m-manylinux1_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.5m

numpy-1.11.0-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 (3.8 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-1.11.0-cp34-none-win_amd64.whl (7.4 MB view details)

Uploaded CPython 3.4 Windows x86-64

numpy-1.11.0-cp34-none-win32.whl (6.5 MB view details)

Uploaded CPython 3.4 Windows x86

numpy-1.11.0-cp34-cp34m-manylinux1_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.4m

numpy-1.11.0-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 (3.8 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-1.11.0-cp33-cp33m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.3m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

numpy-1.11.0-cp27-none-win_amd64.whl (7.4 MB view details)

Uploaded CPython 2.7 Windows x86-64

numpy-1.11.0-cp27-none-win32.whl (6.5 MB view details)

Uploaded CPython 2.7 Windows x86

numpy-1.11.0-cp27-cp27mu-manylinux1_x86_64.whl (15.3 MB view details)

Uploaded CPython 2.7mu

numpy-1.11.0-cp27-cp27m-manylinux1_x86_64.whl (15.3 MB view details)

Uploaded CPython 2.7m

numpy-1.11.0-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-1.11.0.zip.

File metadata

  • Download URL: numpy-1.11.0.zip
  • Upload date:
  • Size: 4.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.11.0.zip
Algorithm Hash digest
SHA256 9109f260850627e4b83a3c4bcef4f2f99357eb4a5eaae75dec51c32f3c197aa3
MD5 19ce5c4eb16d663a0713daf0018a3021
BLAKE2b-256 26a4795f8fe937283cbdb363205bdcbf91c3e0667294545577b7e9d2251b4f67

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0.tar.gz.

File metadata

  • Download URL: numpy-1.11.0.tar.gz
  • Upload date:
  • Size: 4.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for numpy-1.11.0.tar.gz
Algorithm Hash digest
SHA256 a1d1268d200816bfb9727a7a27b78d8e37ecec2e4d5ebd33eb64e2789e0db43e
MD5 bc56fb9fc2895aa4961802ffbdb31d0b
BLAKE2b-256 1a5c57c6920bf4a1b1c11645b625e5483d778cedb3823ba21a017112730f0a12

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp35-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 7fde25785a99d22cda00ee900772e7ba2dbbd6c9384413afd1b6ebd66e90b2a4
MD5 721e8d4224c9f0c98060571a687a03d2
BLAKE2b-256 4b463526dcd37d4e4bf6fb718c6637041711653b3068d1529761a8211c151361

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp35-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp35-none-win32.whl
Algorithm Hash digest
SHA256 81160e1f01fa21c2ec7a8a26ff16ec47336ee28c61540feaf5b3014d13dbdaed
MD5 8012b634dc8c98bd9703ddde289dd2e6
BLAKE2b-256 1d5d34a1014a7d8cd25a32d95d6102f8ff69eb9f461980b7944e03021cc9a20f

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 da21856583a1f6ed0131e2094d6e6ef1881b09860777510f979e33d8361f7379
MD5 64321358a959d9a570a13eeae7203648
BLAKE2b-256 adaca8154670deedc6d482c4e43cdca61f645967a734575105a602d3ecb84bf7

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-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-1.11.0-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 0b1d6a4758a0f98ce7f8d1f429d97eb721dc2f4dea2586d61d1bbc604bd803a9
MD5 424b1cfaf9957cfc1c12cccbd6e65727
BLAKE2b-256 193fe851c42d43c65cbca23c2a3323e950d013d648bdbe341eff1faf0b15570d

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 e87c90be5719901ac19df861f40d8f16a9368cc68d3ede2b615c4c1524c305c8
MD5 e277cad82c223e3c25481d893edf9a83
BLAKE2b-256 854db421b7a81deb345ef2235887b1370ba05be5888c97ccceb6e21a2e8f7b8e

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp34-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp34-none-win32.whl
Algorithm Hash digest
SHA256 0f87221d407beb3a656f94a9706829b80f2864d010ca8ddebba6080893b18813
MD5 fb554348b619b32713b6518b5bc8bc2d
BLAKE2b-256 654cd787fcd02ede34c00d5c699cd51c3d331e9feee406d99272e31f0e319457

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b8f4afab6b8438036a99cf3384eccc7da6286c6e6077cfedb5651fd5fdcaeda0
MD5 08a002aeffa20354aa5045eadb549361
BLAKE2b-256 eaca5e48a68be496e6f79c3c8d90f7c03ea09bbb154ea4511f5b3d6c825cefe5

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-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-1.11.0-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 da76d2b0e49e7c392192e7c695eabbb0309ed031980197fe3e7c74dcfcede005
MD5 75131aadb668c6c8a0dc9975239c91cc
BLAKE2b-256 e3cac4a32d01ab1a0d966294f266812c9231ae64f1a9d9743b6ea95ba5e0b762

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp33-cp33m-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-1.11.0-cp33-cp33m-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 07bd13135c267bc5c449c196c428935b0880cb43579af1d0da883e9dadc0ef64
MD5 84991be7dc1b215aa2beedde9089e673
BLAKE2b-256 26ac38141d8c0ca181031f8335d6febf9615407c02edf577b5946374da9bf377

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 eb503c83ca75d309a1c4a7ac62a8a6f465fbb5128d1a56a1c97c8fb1f999a22a
MD5 93b3374290cb3394e6fbb6b248fe08cd
BLAKE2b-256 a911199290f6576041c44aa4d46ece046fb06fe865cc96f69445fcf02c61855b

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp27-none-win32.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp27-none-win32.whl
Algorithm Hash digest
SHA256 579fda18697cf978fd4f8eef573b16285887411782fd029d7d6b09bd61658279
MD5 2dbf0efde52ef8d6202e0ac2cc98436c
BLAKE2b-256 6029b7ccc96ac304b760f1bf814e97a26b371f854438eb1a2db8d0952c93d3b8

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 cf49c2451905b170a32e043a2a4c323ac96c5d729713fe52c615dfcfb6816022
MD5 6ffb66ff78c28c55bfa09a2ceee487df
BLAKE2b-256 06923c786303889e6246971ad4c48ac2b4e37a1b1c67c0dc2106dc85cb15c18e

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy-1.11.0-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 611cd19f4ae1dc8a0388d89b2b074f14d13ab19f66881d9ba669c90aa6c874fb
MD5 5e8d2b0d9d2b8608e6c35017c27085f0
BLAKE2b-256 5e38a4d219021d6b35265d3a75b8be52444856f9bb733edc117335bc3c8bf5ae

See more details on using hashes here.

Provenance

File details

Details for the file numpy-1.11.0-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-1.11.0-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 dd514e914929b2ffda2626567b48e27a1da715f8fe0c868c41c928f99fa91305
MD5 b8e0f30e08178144caa93109aa31b6f6
BLAKE2b-256 f82ca72cd7b8b448e3b130eb665abb95fa1b3ab8a00db65db60293dc5f1319c5

See more details on using hashes here.

Provenance

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