NumPy Illustrated: The Visual Guide to NumPy
Brush up your NumPy or learn it from scratch
NumPy is a fundamental library that most of the widely used Python data processing libraries are built upon (pandas, OpenCV), inspired by (PyTorch), or can efficiently share data with (TensorFlow, Keras, etc). Understanding how NumPy works gives a boost to your skills in those libraries as well. It is also possible to run NumPy code with no or minimal changes on GPU¹.
The central concept of NumPy is an n-dimensional array. The beauty of it is that most operations look just the same, no matter how many dimensions an array has. But 1D and 2D cases are a bit special. The article consists of three parts:
I took a great article, “A Visual Intro to NumPy” by Jay Alammar², as a starting point, significantly expanded its coverage, and amended a pair of nuances.
Numpy Array vs. Python List
At first glance, NumPy arrays are similar to Python lists. They both serve as containers with fast item getting and setting and somewhat slower inserts and removals of elements.
