NumPy, the Python package for scientific computing, is an adolescent with prospects for a prolific maturity.

Want to get better performance with Python? Here's how to use NumPy to toe the 'invisible line' of data and memory transfers and optimize efficiency.

Learn how this popular Python library accelerates math at scale, especially when paired with tools like Cython and Numba.

The demo program is a bit too long to present in its entirety in this article, but the complete source code is available in the accompanying file download. I wrote the demo using the 3.6.5 version of ...