README.md

Type Hints

Annotations for static analysis — document intent for humans and tools like mypy/pyright.

Files

File	Description
example.py	Functions, dataclasses, generics

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Descriptive Example

Scenario

Type a user model and API functions so mypy catches passing a string where an int is expected.

from dataclasses import dataclass
from typing import Optional, Generic, TypeVar

T = TypeVar("T")

@dataclass
class User:
    id: int
    name: str
    email: Optional[str] = None

def get_users(min_id: int) -> list[User]:
    return [User(id=1, name="Alice")]

def parse_id(value: int | str) -> int:
    if isinstance(value, int):
        return value
    return int(value)

class Box(Generic[T]):
    def __init__(self, value: T):
        self.value = value

Run static check: mypy example.py

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Interview Q&A

Q1: Do type hints affect runtime?
A: No. They're stored in __annotations__ and ignored at runtime unless you use a runtime validator (pydantic, beartype).

Q2: Optional[str] vs str | None?
A: Equivalent. Optional[X] is shorthand for Union[X, None]. Python 3.10+ prefers X | None.

Q3: What is TypeVar for?
A: Generic type variables. Preserves relationships: def first(items: list[T]) -> T — return type matches element type.

Q4: What is a Protocol?
A: Structural subtyping — "if it has these methods, it satisfies the protocol." No inheritance required. Static check only.

Q5: Type hints vs duck typing?
A: Duck typing is runtime behavior. Type hints are optional static checks. Python remains dynamically typed — hints don't enforce at runtime.

Q6: What is TypedDict?
A: Dict with fixed key names and value types. Useful for JSON/API response shapes without full dataclass overhead.

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Run

python3 example.py