Learning Python, 5th Edition

Chapter 20. Comprehensions and Generations

This chapter continues the advanced function topics theme, with a reprisal of the comprehension and iteration concepts previewed in Chapter 4 and introduced in Chapter 14. Because comprehensions are as much related to the prior chapter’s functional tools (e.g., map and filter) as they are to for loops, we’ll revisit them in this context here. We’ll also take a second look at iterables in order to study generator functions and their generator expression relatives—user-defined ways to produce results on demand.

Iteration in Python also encompasses user-defined classes, but we’ll defer that final part of this story until Part VI, when we study operator overloading. As this is the last pass we’ll make over built-in iteration tools, though, we will summarize the various tools we’ve met thus far. The next chapter continues this thread by timing the relative performance of these tools as a larger case study. Before that, though, let’s continue the comprehensions and iterations story, and extend it to include value generators.

List Comprehensions and Functional Tools

As mentioned early in this book, Python supports the procedural, object-oriented, and function programming paradigms. In fact, Python has a host of tools that most would consider functional in nature, which we enumerated in the preceding chapter—closures, generators, lambdas, comprehensions, maps, decorators, function objects, and more. These tools allow us to apply and combine functions ...