What are the differences between Pydantic and Marshmallow?

What are the differences between Pydantic and Marshmallow?

Pydantic and Marshmallow are both libraries for data validation and serialization in Python, but they have different approaches. Pydantic uses Python type annotations and is built on top of dataclasses, while Marshmallow relies on explicit schema definitions. Pydantic is generally faster and more intuitive for type-safe applications.

Can you explain how compatibility works between the two?

Compatibility between Pydantic and Marshmallow can be achieved through custom serialization and deserialization methods. You can create Pydantic models that accept Marshmallow schemas and vice versa, but it requires some manual effort to ensure that the data structures align.

Which one should I use for a new project?

It depends on your project's needs. If you prefer type safety and performance, Pydantic is a great choice. If you need more flexibility with complex data structures, Marshmallow might be better.

Are there any specific use cases where one is better than the other?

Yes, Pydantic excels in applications where type validation is crucial, like FastAPI. Marshmallow is often preferred in scenarios requiring complex data transformations or when working with legacy systems.