Speed up large arrays with PNumPy

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Speed up large arrays with PNumPy

mattip
Administrator
I am pleased to announce the first release of PNumPy: a project to
seamlessly speed up NumPy for large arrays (64K+ elements) with no
change required to your existing NumPy code. PNumPy supports Linux,
Windows, and MacOS on top of NumPy >= 1.18 for python 3.6, 3.7, 3.8, and
3.9. This first release speeds up NumPy binary and unary ufuncs such as
add, multiply, isnan, abs, sin, log, sum, min and many more. PNumPy also
speeds up functions sort, argsort, lexsort, arange, boolean indexing,
and fancy indexing. In the near future it will speed up: astype, where,
putmask, and searchsorted. Other packages that use NumPy, such as
scikit-learn or pandas, will also be sped up for large arrays. Once
installed via "pip install pnumpy", you can trigger it by "import
pnumpy". This will import and modify NumPy by replacing functionality
under-the-hood. More information at
https://quansight.github.io/pnumpy/stable/index.html 
<https://quansight.github.io/pnumpy/stable/index.html>.

This project is a collaboration between RTOS Holdings and Quansight.
Thanks to those companies for their support, and to everyone who
contributed to this release. Thanks also to the original holder of the
pnumpy pypi project, who agreed to allow us to adopt the name. Their
project is still available as pnumpy<2.0.

Matti
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