Based on a "massive open online course" (MOOC) from MIT, this book introduces students with little or no prior programming experience to the art of solving computational problems using Python and various Python libraries, such as numpy, matplotlib, random, pandas, and sklearn.

In this third edition, the core explanatory material is expanded with additional programming examples and many more "practice exercises," making it a relatively smooth introduction to programming for beginners. A new chapter demonstrates the use of the pandas package for time series data analysis. All the code has been rewritten to comply with PEP 8 standards.

Beyond traditional topics, the book also addresses a wide range of subjects that are not typically included in introductory texts, such as visual representation of information, simulations for modeling randomness, computational techniques for understanding data, statistical techniques that inform (and misinform), and optimization problems and dynamic programming.

The book also includes a concise reference guide to Python 3.

John V. Guttag is a professor of computer science and electrical engineering in the Dugald C. Jackson chair at MIT.

Wrote about previous editions: "There is no one-size-fits-all computer science book that meets every need. But if you had to choose just one, this would be an excellent choice." — Hal Abelson, author (with Gerald Jay Sussman) of Structure and Interpretation of Computer Programs.

"It is the book on 'computational thinking' that we have all been waiting for! With humor and historical anecdotes, John Guttag conveys the breadth and joy of computer science without incomprehensible technical details." — Jeannette M. Wing, Vice President of Microsoft Research and Professor of Computer Science and former Department Head at Carnegie Mellon University.