Robust Python

Chapter 3. Type Annotations

Python is a dynamically typed language; types aren’t known until runtime. This is an obstacle when trying to write robust code. Since types are embedded in the value itself, developers have a very tough time knowing what type they are working with. Sure, that name looks likes a str today, but what happens if someone makes it bytes? Assumptions about types are built on shaky ground with dynamically typed languages. Hope is not lost, though. In Python 3.5, a brand-new feature was introduced: type annotations.

Type annotations bring your ability to write robust code to a whole new level. Guido van Rossum, creator of Python, says it best:

I’ve learned a painful lesson that for small programs dynamic typing is great. For large programs you have to have a more disciplined approach and it helps if the language actually gives you that discipline, rather than telling you “Well, you can do whatever you want.”¹

Type annotations are the more disciplined approach, the extra bit of care you need to wrangle larger codebases. In this chapter, you’ll learn how to use type annotations, why they are so important, and how to utilize a tool called a typechecker to enforce your intentions throughout your codebase.

What Are Type Annotations?

In Chapter 2, you got your first glance at a type annotation:

def close_kitchen_if_past_close(point_in_time: datetime.datetime):