python-best-practices

To prevent runtime type errors in Python data models, use type-first design with dataclasses, TypedDict, and discriminated unions. This approach enforces explicit data contracts and clear API boundaries before runtime execution.

The best way to structure type-safe Python interfaces is using Protocols and NewType. Protocols define structural subtyping for strong abstractions, while NewType creates distinct types to enforce safer API boundaries.

Use discriminated unions with Literal in Python to support exhaustive state handling and validation. By tagging data models with Literal types, you ensure predictable control flow and prevent unhandled states during processing.

Yes, you can use TypedDict and dataclasses for building data processing pipelines in Python. TypedDicts define clear data contracts for dictionary inputs, while dataclasses structure typed models before applying behavior.

Type-first design in Python requires running a static type checker like mypy to verify correctness. While dataclasses and Protocols define the contracts at the code level, mypy validates the type safety before runtime.