573 pages, published in 2019
Chapter 1, Vital Python – Math, Strings, Conditionals, and Loops, explains how to write basic Python programs, and outlines the fundamentals of the Python language.
Chapter 2, Python Structures, covers the essential elements that are used to store and retrieve data in all programming languages.
Chapter 3, Executing Python – Programs, Algorithms, and Functions, explains how to write more powerful and concise code through an increased appreciation of well- written algorithms, and an understanding of functions.
Chapter 4, Extending Python, Files, Errors, and Graphs, covers the basic I/O (input- output) operations for Python and covers using the matplotlib and seaborn libraries to create visualizations.
Chapter 5, Constructing Python – Classes and Methods, introduces the most central concepts in object-oriented programming, and it will help you write code using classes, which will make your life easier.
Chapter 6, The Standard Library, covers the importance of the Python standard library. It explains how to navigate in the standard Python libraries and overviews some of the most commonly used modules.
Chapter 7, Becoming Pythonic, covers the Python programming language, with which you will enjoy writing succinct, meaningful code. It also demonstrates some techniques for expressing yourself in ways that are familiar to other Python programmers.
Chapter 8, Software Development, covers how to debug and troubleshoot our applications, how to write tests to validate our code and the documentation for other developers and users.
Chapter 9, Practical Python – Advanced Topics, explains how to take advantage of parallel programming, how to parse command-line arguments, how to encode and decode Unicode, and how to profile Python to discover and fix performance problems.
Chapter 10, Data Analytics with pandas and NumPy, covers data science, which is the core application of Python. We will be covering NumPy and pandas in this chapter.
Chapter 11, Machine Learning, covers the concept of machine learning and the steps involved in building a machine learning algorithm.
Andrew Bird, Dr Lau Cher Han, Mario Corchero Jiménez Graham Lee, Corey Wade