Allen Downey’s Think Python: How to Think Like a Computer Scientist

Many textbooks are bloated, poorly structured, and badly written. Most seem to be quite useless without an accompanying college course, but if the course is well-taught, you can often skip the textbook altogether. Programming is no exception to this rule. As Python got more popular, the publishing industry started churning out one tome after another, and from what I’ve seen, they are often dreadful.

For a particularly bad example, look at Mark Lutz’s Learning Python, now in its 5th edition. It’s a staggering 1600 pages thick, and full of downright absurd examples that seem to consist of little more than manipulation of strings like “spam” and “egg”, and if you’re lucky, he will throw in the integer “42” as well. Mark Lutz’ book on Python is quite possibly the worst technical book I have ever encountered, but the other books I’ve sampled were not much better.

One thing they all seem to have in common is their inflated page count. I think this is simply a tactic of publishers to justify selling their books for a higher price. Adding another 500 pages costs very little with a decent print run. Yet, all that dead weight allows them to increase their retail price by 100 %. Apparently consumers have been misled to believe that a higher page count means that you’ll get a better bang for your buck, but the opposite is true.

On the other hand, Think Python: How to Think Like a Computer Scientist, in version 2.0.10, hardly exceeds 200 pages. Yet, Allen Downey manages to cover all basic programming constructs, recursion, data structures, and much more. He even details helpful debugging tips, and added a useful glossary for quick lookup of terms you may be unfamiliar with. File I/O got its own chapter. That’s not all. Towards the end of the book, he invites you to explore a GUI toolkit (Tkinter), object-oriented programming, and explains the basics of the analysis of algorithms. The amount of content in this book is quite staggering, especially when compared to its peers. Downey managed to organize his material very well, which resulted in a book that is slim, yet still feels complete.

What I particularly liked about Think Python is that the material is presented in a clear, logical order. Consequently object-orientation shows up very late. In fact, it is introduced as an optional feature. This is how it should be done, if you want to include OOP. For a beginner, “modern” OOP adds unnecessary complexity and makes it harder to form a clear mental model of the problem you are going to solve. On the other hand, competent experienced programmers tend to be highly critical of OOP. You’re probably better off if you never encounter it. That’s not (yet?) a mainstream opinion, though, so you may have to learn OOP eventually.

In a book like Liang’s Introduction to Programming Using Python, you get dozens of exercises at the end of each chapter. Most are a drudgery, focussing on minute details of the language. Often, they are just mere variations of one another, not unlike the mechanical “drills” that are popular in high school mathematics education. But this isn’t even the worst part of it. Nowadays, you can’t just pick up an old edition of a textbook and get basically the same product plus a few typos here and there. No, instead there are changes all over the place, not all of them particularly well-thought out, and the exercises get modified as well. Of course, compulsive updating and rushing a book to release makes it easy for new errors to find their way into the book, as evinced by long lists of errata of every new edition. On a side note, Liang’s books are particularly aggravating since you don’t even get the full book anymore. Instead, a good chunk of the content consists as “bonus chapters” that have to be downloaded by using a code that was printed in the book, presumably as an attempt to make buying used books less attractive.

Compared to that despicable strategy, you can get Downey’s book not only free of charge, but complete too. His exercise are often surprisingly engaging for a beginner’s text. Writing a simple recursive function that checks whether a word is an anagram is not very exciting. On the other hand, processing a text file that contains over 100,000 words, and finding the five longest anagrams in the English language is more involved, and to successfully solve that exercise, you have to draw from previous chapters. This makes Think Python particularly interesting for autodidacts since you can effectively use the exercises to check whether you have gained a firm grasp of the material. There is a reasonable number of exercises in the book, and they are well-chosen. It’s common that they systematically build upon each other. This should be normal for textbooks, but it’s an exception.

Think Python: How to Think Like a Computer Scientist can be freely downloaded at the homepage of the author. The book has been released under a free license. As a consequence, there are editions for different programming languages. Think Python itself is based on an earlier book that covered Java. Being freely available also led to a large audience for this book. Downey acknowledges dozens of people who have made suggestions or pointed out errors in the text. After about a decade, this now leads to Think Python being a very polished book. Of all the introductory Python textbooks I’ve had a look at, it is the only one I feel comfortable recommending. It’s great for complete beginners and also for people who have experience in another language and quickly want to familiarize themselves with Python.