Type Hinting & Static Typing

Type Hinting & Static Typing

Python’s dynamic nature makes it easy to write quick scripts, but large code‑bases benefit from type annotations – they improve readability, help IDEs, and enable static analysis tools like mypy.

1️⃣ Basic Syntax (PEP 484)

def greet(name: str) -> str:
    return f"Hello, {name}!"

age: int = 27
items: list[int] = [1, 2, 3]

• The part after : is the type hint for a variable.
• The part after -> is the return type.

2️⃣ The typing Module

from typing import List, Tuple, Dict, Optional, Union, Any, Callable

def process(data: List[int]) -> Tuple[int, float]:
    total = sum(data)
    avg = total / len(data) if data else 0.0
    return total, avg

Frequently used types

Hint	Meaning
List[int]	List containing only integers
Dict[str, Any]	Mapping from strings to any value
Optional[str]	str or None
Union[int, float]	Either int or float
Callable[[int, int], int]	Function taking two ints and returning an int

3️⃣ Runtime vs. Static Checking

• Runtime: Python ignores hints – they are just metadata.
• Static: Tools like mypy, pyright or IDEs read hints and warn about mismatches.

# Install mypy
pip install mypy

# Run a type‑check on a file
mypy myscript.py

Typical output:

myscript.py:5: error: Incompatible types in assignment (expression has type "int", variable has type "str")

4️⃣ Gradual Adoption

1. Add hints to public APIs first (functions, classes).
2. Run mypy --strict locally; fix the most critical errors.
3. Keep the code working – you can always add more hints later.

5️⃣ Advanced Topics (quick glance)

• TypedDict – typed dictionaries for JSON‑like data.
• Protocol – structural subtyping (duck‑typing with static checks).
• New‑type – create distinct types from existing ones (e.g., UserId = NewType('UserId', int)).
• Annotated – attach extra metadata (e.g., for validation libraries).

When you combine type hints with good test coverage, your Python projects become far easier to maintain and refactor.