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.

Lots of tips and tricks available on the NumPY Web site, which is well worth a look, especially as you start out. This short introduction should get you started in thinking of Python as a viable ...

The cache is also intelligently optimized for large objects like NumPy arrays. Regions of data can be shared in-memory between processes on the same system by using numpy.memmap.

[Zoltán] sends in his very interesting implementation of a NumPy-like library for micropython called ulab. He had a project in MicroPython that needed a very fast FFT on a micro controller, and ...

This post shows you how to use arrays in Python and why this data structure is so useful. A foundational skill for data science, coding, and more!