Robust Python

Chapter 2. Introduction to Python Types

To write maintainable Python, you must be aware of the nature of types and be deliberate about using them. I’ll start by talking about what a type actually is and why that matters. I’ll then move on to how the Python language’s decisions about its type system affects the robustness of your codebase.

What’s in a Type?

I want you to pause and answer a question: without mentioning numbers, strings, text, or Booleans, how would you explain what a type is?

It’s not a simple answer for everyone. It’s even harder to explain what the benefits are, especially in a language like Python where you do not have to explicitly declare types of variables.

I consider a type to have a very simple definition: a communication method. Types convey information. They provide a representation that users and computers can reason about. I break the representation down into two different facets:

Mechanical representation

Types communicate behaviors and constraints to the Python language itself.

Semantic representation

Types communicate behaviors and constraints to other developers.

Let’s go learn a little more about each representation.

Mechanical Representation

At its core, computers are all about binary code. Your processor doesn’t speak Python; all it sees is the presence or absence of electrical current on circuits going through it. Same goes for what’s in your computer memory.

Suppose your memory looked like the following:

0011001010001001000101001001000100100010000010101 ...