How to Use TypedDict and Dataclasses in Python

Create typed data structures with TypedDict and dataclasses, then use them as helper functions for describing objects in a type-safe way.

Easy Python 3.11+ Aug 9, 2026 Testing & modern typing 12 views 0 copies

typing typdict dataclass type-hints python-311

Python code

33 lines

Python 3.11+

from typing import TypedDict, NotRequired, Optional
from dataclasses import dataclass


class User(TypedDict):
    name: str
    age: NotRequired[int]
    email: Optional[str]


@dataclass
class Product:
    id: int
    title: str
    price: float = 0.0


def describe_user(user: User) -> str:
    age = user.get("age", "unknown")
    email = user.get("email", "not provided")
    return f"{user['name']} (age: {age}, email: {email})"


def describe_product(product: Product) -> str:
    return f"Product #{product.id}: {product.title} — ${product.price:.2f}"


if __name__ == "__main__":
    sample_user: User = {"name": "Alice", "email": None}
    sample_product = Product(id=101, title="Keyboard", price=49.99)

    print(describe_user(sample_user))
    print(describe_product(sample_product))

Output

stdout

Alice (age: unknown, email: not provided)
Product #101: Keyboard — $49.99

How it works

TypedDict defines a dictionary schema with optional keys (NotRequired) and nullable fields (Optional), enabling type checking on dict-like structures. The dataclass decorator auto-generates __init__, __repr__, and equality methods, reducing boilerplate for class-based data. Helper functions like describe_user and describe_product provide single-responsibility formatting, leveraging type hints for better IDE support and static analysis. Optional[str] means the value can be None or a string, while get() with defaults makes handling missing keys safe at runtime.

Common mistakes

Using `Optional[int]` instead of `NotRequired[int]` when the key may be absent entirely
Forgetting that `TypedDict` is checked statically, not at runtime — invalid keys won't raise errors
Omitting `from __future__ import annotations` when mixing dataclasses and TypedDicts in older Python versions

Variations

Use `typing.Protocol` for structural subtyping instead of `TypedDict`
Define dataclass fields with `field(default_factory=...)` for mutable defaults

Real-world use cases

Typing API payloads in a client library so endpoint responses are validated by the type checker.
Defining immutable config objects with dataclasses and defaults for application settings.
Building helper formatters that display user or product data consistently across a web UI.

How to Use TypedDict and Dataclasses in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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