Mastering Python Itertools: Boost Your Programming Efficiency

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Itertools in Python 3, By Example

It has been called a “gem” and “pretty much the coolest thing ever,” and if you have not heard of it, then you are missing out on one of the greatest corners of the Python 3 standard library: itertools.

A handful of excellent resources exist for learning what functions are available in the itertools module. The docs themselves are a great place to start. So is this post.

The thing about itertools, though, is that it is not enough to just know the definitions of the functions it contains. The real power lies in composing these functions to create fast, memory-efficient, and good-looking code.

This article takes a different approach. Rather than introducing itertools to you one function at a time, you will construct practical examples designed to encourage you to “think iteratively.” In general, the examples will start simple and gradually increase in complexity.

A word of warning: this article is long and intended for the intermediate-to-advanced Python programmer. Before diving in, you should be confident using iterators and generators in Python 3, multiple assignment, and tuple unpacking. If you aren’t, or if you need to brush up on your knowledge, consider checking out the following before reading on:

All set? Let’s start the way any good journey should—with a question.

What Is Itertools and Why Should You Use It?

According to the itertools docs, it is a “module [that] implements a number of iterator building blocks inspired by constructs from APL, Haskell, and SML… Together, they form an ‘iterator algebra’ making it possible to construct specialized tools succinctly and efficiently in pure Python.”

Loosely speaking, this means that the functions in itertools “operate” on iterators to produce more complex iterators. Consider, for example, the built-in zip() function, which takes any number of iterables as arguments and returns an iterator over tuples of their corresponding elements:

>>> list(zip([1, 2, 3], ['a', 'b', 'c']))
[(1, 'a'), (2, 'b'), (3, 'c')]

How, exactly, does zip() work?

[1, 2, 3] and ['a', 'b', 'c'], like all lists, are iterable, which means they can return their elements one at a time. Technically, any Python object that implements the .__iter__() or .__getitem__() methods is iterable. (See the Python Glossary for a more formal definition.) When zip() is called with these two arguments, it grabs a pair of elements from each list and “zips” them together into a tuple. These tuples are then returned by zip() one at a time, making it an iterator.

The power of itertools lies in its ability to efficiently create complex iterators like zip(). Instead of manually writing a for loop to iterate over the elements of multiple lists and create tuples, you can use zip() to achieve the same result in a more concise and readable manner. And that’s just the tip of the iceberg.

Now that you have a sense of why itertools is useful, let’s dive into some examples that showcase its power.