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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.

All numpy wheels distributed from pypi are BSD licensed.

Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted to SSE2 instructions, so may not give optimal linear algebra performance for your machine. See http://docs.scipy.org/doc/numpy/user/install.html for alternatives.

Release History

This version
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1.14.1

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1.14.0

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1.14.0rc1

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1.13.3

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1.13.1

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1.13.0

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1.13.0rc2

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1.13.0rc1

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1.12.1

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1.12.1rc1

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1.12.0

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1.12.0rc2

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1.12.0rc1

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1.12.0b1

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1.11.3

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1.11.2

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1.11.2rc1

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1.11.1

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1.11.1rc1

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1.11.0

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1.11.0rc2

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1.11.0rc1

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1.11.0b3

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1.10.4

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1.10.3

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1.10.2

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1.10.1

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1.10.0

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1.9.3

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1.9.2

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1.9.1

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1.9.0

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1.8.2

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1.8.1

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1.8.0

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1.7.2

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1.7.1

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1.7.0

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1.6.2

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1.6.1

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1.6.0

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1.5.1

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1.5.0

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1.4.1

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1.4.0

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1.3.0

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1.2.1

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1.2.0

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1.1.1

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1.0.4

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1.0.3

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1.0

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1.0rc3

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1.0rc2

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1.0rc1

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1.0b5

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1.0b4

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1.0b1

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0.9.8

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0.9.6

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Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
numpy-1.14.1-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
(4.7 MB) Copy SHA256 Hash SHA256
Wheel cp27 Feb 21, 2018
numpy-1.14.1-cp27-cp27m-manylinux1_i686.whl
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Wheel cp27 Feb 21, 2018
numpy-1.14.1-cp27-cp27m-manylinux1_x86_64.whl
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Wheel cp27 Feb 21, 2018
numpy-1.14.1-cp27-cp27mu-manylinux1_i686.whl
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Wheel cp27 Feb 21, 2018
numpy-1.14.1-cp27-cp27mu-manylinux1_x86_64.whl
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Wheel cp27 Feb 21, 2018
numpy-1.14.1-cp27-none-win32.whl
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Wheel cp27 Feb 21, 2018
numpy-1.14.1-cp27-none-win_amd64.whl
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Wheel cp27 Feb 21, 2018
numpy-1.14.1-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
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Wheel cp34 Feb 21, 2018
numpy-1.14.1-cp34-cp34m-manylinux1_i686.whl
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Wheel cp34 Feb 21, 2018
numpy-1.14.1-cp34-cp34m-manylinux1_x86_64.whl
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Wheel cp34 Feb 21, 2018
numpy-1.14.1-cp34-none-win32.whl
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Wheel cp34 Feb 21, 2018
numpy-1.14.1-cp34-none-win_amd64.whl
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Wheel cp34 Feb 21, 2018
numpy-1.14.1-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
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Wheel cp35 Feb 21, 2018
numpy-1.14.1-cp35-cp35m-manylinux1_i686.whl
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Wheel cp35 Feb 21, 2018
numpy-1.14.1-cp35-cp35m-manylinux1_x86_64.whl
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Wheel cp35 Feb 21, 2018
numpy-1.14.1-cp35-none-win32.whl
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Wheel cp35 Feb 21, 2018
numpy-1.14.1-cp35-none-win_amd64.whl
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Wheel cp35 Feb 21, 2018
numpy-1.14.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
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Wheel cp36 Feb 21, 2018
numpy-1.14.1-cp36-cp36m-manylinux1_i686.whl
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Wheel cp36 Feb 21, 2018
numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl
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Wheel cp36 Feb 21, 2018
numpy-1.14.1-cp36-none-win32.whl
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numpy-1.14.1-cp36-none-win_amd64.whl
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Wheel cp36 Feb 21, 2018
numpy-1.14.1.zip
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Source None Feb 21, 2018

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