Pydantic

data validation

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Pydantic is a Python library that provides data validation and settings management using Python type annotations. It allows developers to define data models with type hints, and Pydantic ensures that the data conforms to the specified types, raising informative errors when validation fails. This approach enhances code reliability and readability, making it particularly useful in applications where data integrity is crucial.

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Usage Example

Here’s a simple example of using Pydantic to define a data model and validate input data:

from pydantic import BaseModel, ValidationError
from typing import List

class User(BaseModel):
    id: int
    name: str
    friends: List[int]

# Valid input data
user_data = {
    'id': 123,
    'name': 'John Doe',
    'friends': [1, 2, 3]
}

user = User(**user_data)
print(user)

# Invalid input data
invalid_user_data = {
    'id': 'abc',  # id should be an int
    'name': 'Jane Doe',
    'friends': [1, 'two', 3]  # friends should be a list of ints
}

try:
    invalid_user = User(**invalid_user_data)
except ValidationError as e:
    print(e)

In this example, the User class defines a data model with fields id, name, and friends. Pydantic validates the input data against this model, ensuring that id is an integer, name is a string, and friends is a list of integers. If the input data does not conform to these types, Pydantic raises a ValidationError with details about the validation issues.