data-structures

Choose Pydantic models for external API boundaries requiring validation and serialization, and use dataclasses for lightweight internal state containers. This distinction standardizes Python data structures and ensures type safety across your codebase.

Standardize Python data structures by applying a decision flow that categorizes data containers as either external input/output or internal state. Use Pydantic for validation-first modeling and dataclasses for simple internal containers to enforce consistency.

Dataclasses are better for internal Python state when you need lightweight containers with optional immutability and do not require external validation. Pydantic is preferred for API boundaries where data validation and serialization are critical.

Using Pydantic for all data structures introduces validation overhead that may impact performance for simple internal state. Prefer dataclasses for lightweight internal containers to optimize performance while reserving Pydantic for necessary API boundary validation.

Apply data-structure conventions during Python code reviews by checking whether a data container represents external input/output or internal state. Enforce Pydantic for API boundaries and dataclasses for internal containers to maintain consistency.

Validation-first modeling in Python prioritizes Pydantic BaseModel for external API boundaries to ensure data integrity. It enforces clear guidelines for validation and serialization before processing, distinguishing external inputs from internal state.