======================== Announcing Numexpr 2.4 ======================== 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. What's new ========== A new `contains()` function has been added for detecting substrings in strings. Only plain strings (bytes) are supported for now (see ticket #142). Thanks to Marcin Krol. You can have a glimpse on how `contains()` works in this notebook: http://nbviewer.ipython.org/gist/FrancescAlted/10595974 where it can be seen that this can make substring searches more than 10x faster than with regular Python. You can find the source for the notebook here: https://github.com/FrancescAlted/ngrams Also, there is a new version of setup.py that allows better management of the NumPy dependency during pip installs. Thanks to Aleks Bunin. Windows related bugs have been addressed and (hopefully) squashed. Thanks to Christoph Gohlke. 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. 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