Python / Core Python Fundamentals Interview Questions
What are Python dataclasses and when should you use them instead of regular classes?
@dataclass (introduced in Python 3.7, PEP 557) is a class decorator that auto-generates boilerplate methods — __init__, __repr__, and __eq__ — from class-level field annotations. It removes the tedium of writing identical initialisation code for data-holding classes.
from dataclasses import dataclass, field @dataclass class Product: name: str price: float tags: list = field(default_factory=list) # mutable default in_stock: bool = True p = Product('Widget', 9.99, ['sale', 'new']) print(p) # Product(name='Widget', price=9.99, tags=['sale', 'new'], in_stock=True) print(p == Product('Widget', 9.99, ['sale', 'new'])) # True â __eq__ generated # Frozen (immutable) dataclass â useful as dict key @dataclass(frozen=True) class Point: x: float y: float pt = Point(1.0, 2.0) print(hash(pt)) # hashable because frozen
Use field(default_factory=list) for mutable defaults — the same reason you use None in regular functions; if you wrote tags: list = [] in a dataclass the annotation system handles it safely (unlike regular class attributes), but field(default_factory=list) is the explicit, recommended way.
Dataclasses are the right choice for plain data containers: API response models, configuration objects, records. For complex logic with many methods, regular classes are cleaner. For fully immutable value objects, frozen=True is the quick path. For validation and serialisation, libraries like Pydantic build on the dataclass concept and add runtime type checking.
More Related questions...
