Quick Success Data Science
NumPy is Python’s foundational library for numerical calculations. With NumPy, the heavy lifting is handled by arrays, essentially tables of elements of the identical data type. Arrays are optimized for performance, permitting faster mathematical and logical operations than traditional Python data types, like lists.
In Part 1, we covered learn how to create arrays, describe them, and access their attributes using dot notation. In this text, we’ll examine learn how to access the weather in arrays using indexes and slices, so you may extract the worth of elements and alter them using task statements. Array indexing uses square brackets [], similar to Python lists.
As a refresher from Part 1, here’s a graphical representation of a 1D, 2D, and 3D array, with the axes annotated. You’ll need to grasp the axes’ directions to index properly.
