Data Classes in Python

dataclasses reduce boilerplate for classes that primarily store data.

Basics

Auto‑generate __init__, __repr__, __eq__, and more.

from dataclasses import dataclass

@dataclass
class User:
    id: int
    name: str

Defaults and factory

Use default values or factories for mutable fields.

from dataclasses import field
from typing import List

@dataclass
class Project:
    name: str
    members: List[str] = field(default_factory=list)  # new list per instance

Ordering and hashing

Enable ordering methods and hashing when appropriate.

@dataclass(order=True, frozen=False)
class Point:
    x: int
    y: int

Immutability

frozen=True makes instances immutable (raises on assignment). Combine with tuples/frozenset for deep immutability.

@dataclass(frozen=True)
class Config:
    host: str
    port: int

Post‑init processing

__post_init__ runs after auto‑init for validation/normalization.

@dataclass
class Temperature:
    celsius: float
    def __post_init__(self):
        if self.celsius < -273.15:
            raise ValueError("below absolute zero")

Slots and performance (3.10+)

Use slots=True to reduce memory and speed attribute access.

@dataclass(slots=True)
class Light:
    on: bool

Asdict/astuple and replace

Serialize or derive modified copies.

from dataclasses import asdict, astuple, replace
asdict(User(1, "Ada"))
replace(Config("localhost", 80), port=443)

Summary

• Use @dataclass for concise, typed records
• Prefer default_factory for mutable fields; frozen and slots for safety/perf