Python for Data Science Cheat Sheet: NumPy Basics

This book is the NumPy Basics page from the DataCamp series Python for Data Science Cheat Sheet. In a single page it lists the most common ways to write NumPy, the scientific computing library, so you do not need to start from the beginning; when a specific question comes up, you can quickly look up the corresponding syntax. Contents include: importing the library by the convention `import numpy as np`; creating arrays, such as array literals, `zeros`, `ones`, `arange`, `linspace`, `full`, `eye`, `random.random`, `empty`, and more; saving and loading data (`save`, `savez`, `load`, plus `loadtxt`, `genfromtxt`, `savetxt` for text files); common data types and type conversion; inspecting array attributes (`shape`, `ndim`, `size`, `dtype`, `astype`); arithmetic operations and universal functions (addition, subtraction, multiplication, division, exponents, square roots, trigonometric functions, logarithms, dot products); comparison and aggregate statistics (`sum`, `min`, `max`, `cumsum`, `mean`, `median`, `std`, etc.); array reshaping, joining, and splitting (`reshape`, `ravel`, `transpose`, `concatenate`, `vstack`, `hstack`, `split`, etc.); and diagrams of one- to three-dimensional arrays that help explain array shapes and the meaning of axes. Each code snippet comes with a short explanation; this is a reference sheet, not a tutorial.