Python Dataclasses & Pydantic
Also Known As
TL;DR
Explanation
@dataclass (Python 3.7+) generates boilerplate from type-annotated fields: __init__ with defaults, __repr__, __eq__ (and optionally __hash__, __lt__ for ordering). @dataclass(frozen=True) makes instances immutable — Python's equivalent of PHP 8.1 readonly classes. field() allows customisation: default_factory for mutable defaults, compare=False, repr=False. Pydantic v2 goes further: validates types at runtime (coercing '42' to int or rejecting invalid data), generates JSON Schema, and serialises to dict/JSON with model.model_dump(). Widely used in FastAPI for request/response models. Compared to PHP DTOs backed by readonly classes + PHPStan: Python dataclasses give the same structural benefits; Pydantic adds the runtime validation that PHP's type declarations provide natively.
Common Misconception
Why It Matters
Common Mistakes
- Mutable default arguments: field(default=[]) is required — bare list defaults are shared across instances.
- Not using frozen=True for value objects that should be immutable.
- Using dataclasses for classes with significant behaviour — they are for data containers, not services.
- Not using __post_init__ for validation — the generated __init__ provides no validation hook otherwise.
Code Examples
# Without dataclass — repetitive boilerplate:
class Point:
def __init__(self, x, y): self.x = x; self.y = y
def __repr__(self): return f'Point({self.x}, {self.y})'
def __eq__(self, other): return self.x == other.x and self.y == other.y
# With dataclass:
from dataclasses import dataclass
@dataclass(frozen=True)
class Point:
x: float
y: floatfrom dataclasses import dataclass, field
@dataclass(frozen=True)
class Money:
amount: int
currency: str
tags: list[str] = field(default_factory=list)
def __post_init__(self):
if self.amount < 0:
raise ValueError('Amount cannot be negative')