strict-dataclass

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A Python standard library dataclasses enhancement implementation that provides strict type checking.

This is an experimental library. Please do not use it in production environments. You might want to use pydantic or attrs instead.

Features

• Advantages:
  • Compatible with standard library dataclasses usage
  • Strict type checking during initialization and attribute modification
  • Supports validation of nested complex data types
  • Unlike pydantic, it doesn't deep copy input values
  • Performs deep checking of nested complex data types (pydantic's BaseModel / dataclass doesn't perform deep checking)
• Disadvantages:
  • Poor performance, object creation and attribute modification takes about 3 times longer than pydantic. However, it seems acceptable considering the cost of deep checking.

Installation

pip install strict-dataclass
# Python >= 3.11

Usage

Basic Usage

from strict_dataclasses import strict_dataclass

@strict_dataclass
class User:
    name: str
    age: int
    is_active: bool

# Correct usage - passes type checking
user = User(name="Alice", age=25, is_active=True)

# Incorrect usage - raises TypeError
user = User(name=123, age="25", is_active=1)  # TypeError: Field 'name' must be of type str

Complex Types

from typing import Optional
from strict_dataclasses import strict_dataclass

@strict_dataclass
class Student:
    name: str
    scores: dict[str, list[int]]
    tags: list[str]
    note: Optional[str] = None

# Correct usage
student = Student(
    name="Bob",
    scores={"math": [90], "english": [85]},
    tags=["good", "active"],
    note="Excellent student"
)

# Type errors
student = Student(
    name="Bob",
    scores={"math": ["90"]},  # TypeError: Value must be a list[int]
    tags="good",  # TypeError: Must be a list
    note=123  # TypeError: Must be a string or None
)