[Numpy-discussion] Speed up large arrays with PNumPy

[Matti Picus]

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