What are the differences between Pydantic and Dataclasses?

What are the differences between Pydantic and Dataclasses?

Pydantic is a data validation and settings management library that uses Python type annotations, while Dataclasses are a built-in feature in Python for creating classes that primarily store data. Pydantic provides additional features like data validation and parsing.

Can you give me an example of how to use both?

Sure! Here's a simple example:

Using Dataclasses:

from dataclasses import dataclass

@dataclass
class User:
    name: str
    age: int

Using Pydantic:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

What are the advantages of using Pydantic over Dataclasses?

Pydantic offers automatic data validation, error messages, and parsing of input data, which can help catch errors early. It also supports complex data types and nested models, making it more powerful for data validation.

Are there any performance differences between the two?

Yes, Pydantic can be slower than Dataclasses due to its validation and parsing features. However, the performance difference is often negligible for most applications unless you're processing a large amount of data.