========================= 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: 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. 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