In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low.
One of the problems I saw was the books. They were too big, with too much unnecessary detail about Java, and not enough high-level guidance about how to program. And they all suffered from the trap door effect: they would start out easy, proceed gradually, and then somewhere around Chapter 5 the bottom would fall out. The students would get too much new material, too fast, and I would spend the rest of the semester picking up the pieces.
Two weeks before the first day of classes, I decided to write my own book. My goals were:
- Keep it short. It is better for students to read 10 pages than not read 50 pages.
- Be careful with vocabulary. I tried to minimize the jargon and define each term at first use.
- Build gradually. To avoid trap doors, I took the most difficult topics and split them into a series of small steps.
- Focus on programming, not the programming language. I included the minimum useful subset of Java and left out the rest.
I needed a title, so on a whim I chose How to Think Like a Computer Scientist.
My first version was rough, but it worked. Students did the reading, and they understood enough that I could spend class time on the hard topics, the interesting topics and (most important) letting the students practice.
I released the book under the GNU Free Documentation License, which allows users to copy, modify, and distribute the book.
What happened next is the cool part. Jeff Elkner, a high school teacher in Virginia, adopted my book and translated it into Python. He sent me a copy of his translation, and I had the unusual experience of learning Python by reading my own book. As Green Tea Press, I published the first Python version in 2001.
In 2003 I started teaching at Olin College and I got to teach Python for the first time. The contrast with Java was striking. Students struggled less, learned more, worked on more interesting projects, and generally had a lot more fun.
Over the last nine years I continued to develop the book, correcting errors, improving some of the examples and adding material, especially exercises.
The result is this book, now with the less grandiose title Think Python. Some of the changes are:
- I added a section about debugging at the end of each chapter. These sections present general techniques for finding and avoiding bugs, and warnings about Python pitfalls.
- I added more exercises, ranging from short tests of understanding to a few substantial projects. And I wrote solutions for most of them.
- I added a series of case studies—longer examples with exercises, solutions, and discussion. Some are based on Swampy, a suite of Python programs I wrote for use in my classes. Swampy, code examples, and some solutions are available from http://thinkpython.com.
- I expanded the discussion of program development plans and basic design patterns.
- I added appendices about debugging, analysis of algorithms, and UML diagrams with Lumpy.
I hope you enjoy working with this book, and that it helps you learn to program and think, at least a little bit, like a computer scientist.
Allen B. Downey
Allen Downey is a Professor of Computer Science at the Franklin W. Olin College of Engineering.