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Sun, 29 Jan 2017 05:08:01 -0800

=========================

 Announcing Numexpr 2.6.2

=========================


What's new

==========


This is a maintenance release that fixes several issues, with special

emphasis in keeping compatibility with newer NumPy versions.  Also,

initial support for POWER processors is here.  Thanks to Oleksandr

Pavlyk, Alexander Shadchin, Breno Leitao, Fernando Seiti Furusato and

Antonio Valentino for their nice contributions.


In case you want to know more in detail what has changed in this

version, see:


https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst



What's Numexpr

==============


Numexpr is a fast numerical expression evaluator for NumPy.  With it,

expressions that operate on arrays (like "3*a+4*b") are accelerated

and use less memory than doing the same calculation in Python.


It wears multi-threaded capabilities, as well as support for Intel's

MKL (Math Kernel Library), which allows an extremely fast evaluation

of transcendental functions (sin, cos, tan, exp, log...) while

squeezing the last drop of performance out of your multi-core

processors.  Look here for a some benchmarks of numexpr using MKL:


free online bettinghttps://github.com/pydata/numexpr/wiki/NumexprMKL


Its only dependency is NumPy (MKL is optional), so it works well as an

easy-to-deploy, easy-to-use, computational engine for projects that

don't want to adopt other solutions requiring more heavy dependencies.


Where I can find Numexpr?

=========================


The project is hosted at GitHub in:


https://github.com/pydata/numexpr


You can get the packages from PyPI as well (but not for RC releases):


http://pypi.python.org/pypi/numexpr


Share your experience

=====================


Let us know of any bugs, suggestions, gripes, kudos, etc. you may

have.


Enjoy data!

-- 
Francesc Alted
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