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

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.

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 ...

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.

[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!