2020 by Yaning Liu, University of Colorado Denver.
Based on “First semester in Numerical Analysis with Julia”, written by Giray Ökten. The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index, a measure of popularity of programming languages, and is the top-ranked interpreted language. Will serve those interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms.
2016 by Charles Severance, University of Michigan.
Based on "Think Python". The book structure has been changed to do data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning.
By Allen B. Downey, Olin College.
An introduction to Python programming for beginners. It starts with basic concepts of programming; it is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. All examples in the book are now Python 3, and the supporting code has been updated to run in both Python 2 and 3.
2021 by Allen B. Downey, Franklin W. Olin College of Engineering.
This book is primarily about complexity science, but studying complexity science gives you a chance to explore topics and ideas you might not encounter otherwise, practice programming in Python, and learn about data structures and algorithms.
This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I try to emphasize fundamental ideas that apply to programming in many languages, but along the way you will learn useful features that are specific to Python.