Robust Python

Part I. Annotating Your Code with Types

Welcome to Part I, where I will focus on types in Python. Types model the behavior of your program. Beginner programmers understand that there are different types in Python, such as float or str. But what is a type? How does mastering types make your codebase stronger? Types are a fundamental underpinning of any programming language, but, unfortunately, most introductory texts gloss over just how types benefit your codebase (or if misused, those same types increase complexity).

Tell me if you’ve seen this before:

>>>type(3.14)
<class 'float'>

>>>type("This is another boring example")
<class 'str'>

>>> type(["Even", "more", "boring", "examples"])
<class 'list'>

This could be pulled from almost any beginner’s guide to Python. You will learn about the int, str, float, and bool data types, and all sorts of other things the language offers. Then, boom, you move on, because let’s face it, this Python is not flashy. You want to dive into the cool stuff, like functions and loops and dictionaries, and I don’t blame you. But it’s a shame that many tutorials never revisit types and give them their proper due. As users dig deeper, they may discover type annotations (which I cover in the next chapter) or start writing classes, but often miss out on the fundamental discussion about when to use types appropriately.

That’s where I’ll start.