python-data-classes

Use dataclasses, attrs, or Pydantic to define structured data models with type annotations, validation rules, and automatic serialization. Dataclasses are built-in; Pydantic adds runtime validation and conversion; attrs offers flexibility between the two.

Yes. Dataclasses support type annotations, field defaults, factory functions for mutable defaults, and frozen instances. Configure fields individually to enforce immutability, control slots, or customize serialization behavior.

Dataclasses convert to dicts via `asdict()`; Pydantic models use `.model_dump()` or `.model_dump_json()` for JSON output. Both support nested structures and custom serialization for interoperability with APIs and data pipelines.

Dataclasses are lightweight and built-in; attrs adds inheritance and flexible defaults; Pydantic excels at runtime validation and coercion. Choose dataclasses for simple models, attrs for advanced field control, Pydantic for strict validation and external data ingestion.

Yes. Dataclasses integrate cleanly with dictionaries and JSON, supporting nested models and field customization. Pydantic adds automatic validation and type coercion during deserialization, making it ideal for API inputs and test fixtures.