Advanced Validation Techniques

Dive deeper into Pydantic's capabilities by learning how to implement custom validation logic for your data models.

Custom Validators in Pydantic

While type annotations and basic validators cover many common use cases, Pydantic also allows for custom validation logic through the use of decorators. This enables you to define your own rules for data validation, transformation, and normalization.

Using the validator Decorator

Pydantic’s validator decorator allows you to attach custom validation functions to model fields. These functions can perform additional checks and transformations on field values.

from pydantic import BaseModel, field_validator, ValidationInfo

class Product(BaseModel):
    name: str
    description: str
    price: float
    discount_price: float

    @field_validator('discount_price')
    def check_discount_price(cls, v: str, info: ValidationInfo):
        if 'price' in info.data and v >= info.data['price']:
            raise ValueError('discount_price must be less than the price')
        return v

In this example, a Product model is defined with a custom validator for the discount_price field. The validator ensures that the discount_price is always less than the price.

Pre and Post Validators

Validators can be set to run either before (pre=True) or after Pydantic’s standard validation. Pre-validators are useful for data transformation or normalization, while post-validators can enforce additional constraints on validated data.

@validator('signup_ts', pre=True)
def parse_signup_ts(cls, v):
    # Example pre-validator to parse a string into a datetime
    return parse_date_string(v)

Complex Field Validation

Validators are not limited to simple conditions. They can incorporate complex logic, accessing other fields in the model via the values argument, and can even raise multiple errors.

Sharing Validators

Validators can be shared between models or fields by defining them as standalone functions and using the each_item=True parameter for iterable fields.

Lesson Assignment

Create a User model with fields for username, email, and age. Implement custom validators that:

• Ensure the username is at least 3 characters long.
• Validate that the email follows a simple pattern (e.g., contains @).
• Check that the age is between 18 and 100.

Experiment with both pre and post validators to familiarize yourself with their differences and applications.

Additional Resources

• Pydantic Validators Documentation
• Python Regular Expressions for Email Validation

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