What are the differences between Cerberus and Pydantic?

What are the differences between Cerberus and Pydantic?

Cerberus is a lightweight and extensible data validation library, while Pydantic is built on top of Python's type hints, providing data validation and settings management using Python data classes.

Which one is better for API validation?

It depends on your needs. Pydantic is often preferred for APIs due to its integration with FastAPI and type safety, while Cerberus is more flexible for various validation scenarios.

Can you give an example of how to use both?

Sure! In Cerberus, you define a schema as a dictionary, while in Pydantic, you create a model class. Would you like to see code snippets?

Yes, please provide code snippets for both.

Here's a simple example for Cerberus:

from cerberus import Validator
schema = {'name': {'type': 'string'}, 'age': {'type': 'integer'}}
v = Validator(schema)
v.validate({'name': 'John', 'age': 30})

And for Pydantic:

from pydantic import BaseModel
class User(BaseModel):
    name: str
    age: int
user = User(name='John', age=30)