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Numpy for Windows 1.11.2 freeware

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NumPy is the fundamental package needed for scientific computing with Python. It contains among other things:
* a powerful N-dimensional array object
* sophisticated (broadcasting) functions
* tools for integrating C/C++ and Fortran code
* useful linear algebra, Fourier transform, and random number capabilities. ...

Author Jarrod Millman
Released 2016-10-04
Filesize 4.70 MB
Downloads 381
OS Windows 2000, Windows 2003, Windows XP, Windows Vista, Windows Vista x64, Windows 7, Windows 7 x64, Windows 8, Windows 8 x64, Windows 10, Windows 10 x64
Installation Instal And Uninstall
Keywords Numerical Python, Python Component, Python Language, Python, Numerical, Component
Users' rating
(6 rating)
Numpy for WindowsOtherWindows 2000, Windows 2003, Windows XP, Windows Vista, Windows Vista x64, Windows 7, Windows 7 x64, Windows 8, Windows 8 x64, Windows 10, Windows 10 x64
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Numpy for Windows freeware - The Latest User Reviews
Numpy for Windows freeware - The Latest Versions History
1.11.2 Oct 4, 2016 New Release Numpy 1.11.2 supports Python 2.6 - 2.7 and 3.2 - 3.5. It fixes bugs and regressions found in Numpy 1.11.1 and includes several build related improvements. Wheels for Linux, Windows, and OS X can be found on PyPI.
1.6.2 Aug 25, 2012 New Release New features:
· Reduction UFuncs Generalize axis= Parameter
· Any ufunc.reduce function call, as well as other reductions like sum, prod, any, all, max and min support the ability to choose a subset of the axes to reduce over. Previously, one could say axis=None to mean all the axes or axis=# to pick a single axis. Now, one can also say axis=(#,#) to pick a list of axes for reduction.

Reduction UFuncs New keepdims= Parameter :
· There is a new keepdims= parameter, which if set to True, doesn't throw away the reduction axes but instead sets them to have size one. When this option is set, the reduction result will broadcast correctly to the original operand which was reduced.

Datetime support:
.. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes in future versions of NumPy. There have been a lot of fixes and enhancements to datetime64 compared to NumPy 1.6:
· the parser is quite strict about only accepting ISO 8601 dates, with a few convenience extensions
1.6.1 Jul 21, 2011 New Release · BUG: Fix regression in printing polynomials.
· BUG: defer numpy.distutils import in ctypeslib.

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