When working with data in Python, it’s essential to ensure that the data is valid and consistent. Two popular libraries for working with data in Python are dataclasses and Pydantic. While both libraries provide a way to define and work with structured data, they differ significantly when it comes to data validation.

Dataclasses: Manual Validation Required

Dataclasses require manual implementation of validation logic. This means that you need to write custom code to validate the data, which can be time-consuming and error-prone.

Here’s an example of how you might implement validation using dataclasses:

from dataclasses import dataclass

@dataclass
class Dog:
    name: str
    age: int

    def __post_init__(self):
        if not isinstance(self.name, str):
            raise ValueError("Name must be a string")

        try:
            self.age = int(self.age)
        except (ValueError, TypeError):
            raise ValueError("Age must be a valid integer, unable to parse string as an integer")

# Usage
try:
    dog = Dog(name="Bim", age="ten")
except ValueError as e:
    print(f"Validation error: {e}")

Validation error: Age must be a valid integer, unable to parse string as an integer

As you can see, implementing validation using dataclasses requires a significant amount of custom code.

Pydantic: Built-in Validation

Pydantic, on the other hand, offers built-in validation that automatically validates data and provides informative error messages. This makes Pydantic particularly useful when working with data from external sources.

Here’s an example of how you might define a Dog class using Pydantic:

from pydantic import BaseModel

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

try:
    dog = Dog(name="Bim", age="ten")
except ValueError as e:
    print(f"Validation error: {e}")

Validation error: 1 validation error for Dog
age
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='ten', input_type=str]

As you can see, Pydantic automatically validates the data and provides a detailed error message when the validation fails.

Conclusion

While dataclasses require manual implementation of validation logic, Pydantic offers built-in validation that automatically validates data and provides informative error messages. This makes Pydantic a more convenient and efficient choice for working with data in Python, especially when working with data from external sources.

Link to Pydantic.