Robust Python

Chapter 5. Collection Types

You can’t go very far in Python without encountering collection types. Collection types store a grouping of data, such as a list of users or a lookup between restaurant or address. Whereas other types (e.g., int, float, bool, etc.) may focus on a single value, collections may store any arbitrary amount of data. In Python, you will encounter common collection types such as dictionaries, lists, and sets (oh, my!). Even a string is a type of collection; it contains a sequence of characters. However, collections can be difficult to reason about when reading new code. Different collection types have different behaviors.

Back in Chapter 1, I went over some of the differences between the collections, where I talked about mutability, iterability, and indexing requirements. However, picking the right collection is just the first step. You must understand the implications of your collection and ensure that users can reason about it. You also need to recognize when the standard collection types aren’t cutting it and you need to roll your own. But the first step is knowing how to communicate your collection choices to the future. For that, we’ll turn to an old friend: type annotations.

Annotating Collections

I’ve covered type annotations for non–collection types, and now you need to know how to annotate collection types. Fortunately, these annotations don’t differ too much from the annotations you’ve already learned.

To illustrate this, suppose ...