Introducing NumPy, Half 2: Indexing Arrays | by Lee Vaughan | Sep, 2024
Fast Success Knowledge Science
Slicing and dicing like a professional
NumPy is Python’s foundational library for numerical calculations. With NumPy, the heavy lifting is dealt with by arrays, basically tables of components of the identical information sort. Arrays are optimized for efficiency, allowing sooner mathematical and logical operations than conventional Python information sorts, like lists.
In Part 1, we lined the best way to create arrays, describe them, and entry their attributes utilizing dot notation. On this article, we’ll look at the best way to entry the weather in arrays utilizing indexes and slices, so you may extract the worth of components and alter them utilizing project statements. Array indexing makes use of sq. brackets [], identical to Python lists.
As a refresher from Half 1, here’s a graphical illustration of a 1D, 2D, and 3D array, with the axes annotated. You’ll want to know the axes’ instructions to index correctly.
