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

All of the normal arithmetic operators work on NumPY arrays in an element-wise fashion. So, to get the squares of the elements in an array, it would be as simple as array1 * array1.

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.

We can cast an ordinary python list as a NumPy one-dimensional array. We can also cast a python list of lists to a NumPy two-dimensional array. Usually we will build arrays by using NumPy's ...

[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 ...