How to Use Pydantic `field_validator` on Another Optional Field

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What will you learn?

In this tutorial, you will learn how to effectively utilize Pydantic’s field_validator to validate an optional field based on the value of another field in a Pydantic model.

Introduction to the Problem and Solution

Imagine a scenario where you need to trigger a validation check on an optional field within a Pydantic model only when a specific condition is met by another field. This is where the powerful field_validator comes into play. By incorporating custom validation logic, you can ensure that your data meets specific requirements before being deemed valid.

To tackle this issue, we will create a validator method within our Pydantic model that evaluates the status of one field and validates the optional field accordingly.

Code

from pydantic import BaseModel, ValidationError, root_validator

class ExampleModel(BaseModel):
    main_field: int
    optional_field: int = None

    @root_validator(pre=True)
    def validate_optional_field(cls, values):
        if values.get('main_field') == 1:
            if values.get('optional_field') is None:
                raise ValueError("Optional Field cannot be None when Main Field is 1")
        return values

# Usage example    
try:
    data = {"main_field": 1, "optional_field": None}
    validated_data = ExampleModel(**data)
except ValidationError as e:
    print(e)

# You can test with other values as well.

# Copyright PHD

Explanation

  • We have defined a Pydantic model called ExampleModel with two fields: main_field and optional_field.
  • By using the @root_validator(pre=True) decorator, we specify that our method should serve as a validation function for all fields in the model before any other validators are executed.
  • Inside the validator method (validate_optional_field), we examine whether the value of main_field equals 1. If it does and optional_field is set to None, we raise a validation error.
  • The output dictionary from this method contains either the original input values or modified/validated versions based on the conditions checked.
    How does Pydantic handle multiple validators within a single model?

    Pydantic allows developers to incorporate multiple validators in a model by utilizing various decorators like @root_validator, @validator, etc., each serving distinct purposes related to validation processes.

    Can I implement conditional validations based on multiple fields in Pydantic models?

    Absolutely! You can establish intricate conditional validations by leveraging combinations of different fields’ values and applying customized logic inside your validator methods.

    Is it feasible to reuse validators across diverse Pydantic models?

    Yes, you can create separate utility functions or classes containing your validation logic and invoke them from various Pydantic models whenever required for enhanced reusability.

    How does Pydantic manage default values during validations?

    Pydantic takes default values into account while conducting validations. If no value is provided during initialization for an attribute with a predefined default value in the model definition, that default value will be utilized unless altered or invalidated by custom logic within validators.

    Can I access other fields’ values within a validator in Pydantic?

    Certainly! You have access to all input data through arguments passed into your validator methods; thus, referencing other fields’ current states/values during validation checks becomes straightforward.

    Conclusion

    By harnessing Pydantic�s field-validator feature, users can enforce custom business rules ensuring data consistency and integrity within their applications. Understanding the significance of proper data verification is crucial for maintaining a robust application ecosystem and preventing potential vulnerabilities arising from mishandling improper inputs or unauthorized accesses. Leveraging these powerful features offered by the framework has proven invaluable for numerous companies worldwide in delivering secure and reliable software solutions that exceed customer expectations while meeting industry standards and compliances effectively. Embrace these capabilities to drive growth, profitability, and long-term success in today’s competitive global marketplace!

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