Type Hints

Document expected data shapes and improve tooling without changing Python into a statically typed language.

Why this matters

Type hints make function contracts easier to understand and help editors and static analysis catch mismatches earlier.

Start with the idea

Annotations describe expected types for parameters, return values, and variables. Python still runs dynamically, but tools can use the annotations for checking and navigation.

IdeaUnderstand the purpose

→

CodeRun the smallest example

→

PracticeChange it yourself

SubjectVision learning pattern: understand the idea before memorizing syntax.

Small working example

Run this example first. Do not change several things at once; confirm the basic behavior, then experiment.

def total(values: list[float]) -> float:
    return sum(values)

print(total([1.5, 2.5]))

Expected output

4.0

What to notice

The example is intentionally small. Focus on the chapter’s main idea before adding extra syntax or framework code.

Common beginner mistake

Mistake

Assuming type hints automatically validate runtime input.

Better approach

Use type hints for contracts and tooling; add runtime validation separately when external input must be checked.

Quick recap

Type hints document intent.
Static tools can check them.
Runtime validation is a separate concern.

Try it yourself

These are deliberately small. If you can complete them without copying the example, you are ready to continue.

Add type hints to two earlier functions.
Create a function that returns dict[str, int].