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:
https://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.
The repository has been migrated to https://github.com/pydata/numexpr. All new tickets and PR should be directed there. Also, a conj() function for computing the conjugate of complex arrays has been added. Thanks to David Menéndez. See PR #125.
Finallly, we fixed a DeprecationWarning derived of using oa_ndim ==
0 and op_axes == NULL when using NpyIter_AdvancedNew() and
NumPy 1.8. Thanks to Mark Wiebe for advise on how to fix this
properly.
Many thanks to Christoph Gohlke and Ilan Schnell for his help during the testing of this release in all kinds of possible combinations of platforms and MKL.
In case you want to know more in detail what has changed in this version, see:
https://github.com/pydata/numexpr/wiki/Release-Notes
or have a look at RELEASE_NOTES.txt in the tarball.
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
Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.
Enjoy data!