Working with Custom Data Types

Learn to extend Pydantic's functionality by defining and using custom data types for more granular control over your data validation.

Extending Pydantic with Custom Data Types

Pydantic’s powerful validation system can be extended with custom data types, enabling you to define bespoke validation logic and serialization/deserialization rules for specific data structures.

Defining Custom Data Types

A custom data type in Pydantic is usually defined by extending existing base types and adding custom validation or transformation logic. Here’s how you can create a custom type for handling email addresses:

from pydantic import BaseModel, EmailStr

class Email(EmailStr):
    @classmethod
    def __get_validators__(cls):
        yield cls.validate

    @classmethod
    def validate(cls, v):
        # Place additional custom validation logic here
        if not "@" in v:
            raise ValueError("Invalid email address")
        return v.lower()

This example shows a custom Email type that extends EmailStr, a built-in Pydantic type for email validation. It adds additional logic to ensure all emails are converted to lowercase.

Using Custom Data Types in Models

Once you’ve defined a custom data type, you can use it in your models just like any built-in type.

class User(BaseModel):
    name: str
    email: Email  # Using the custom Email type

In this model, the email field uses the custom Email data type, applying both the built-in validation from EmailStr and the additional custom logic.

Advantages of Custom Data Types

• Flexibility: Tailor validation logic to the specific needs of your application.
• Reusability: Define once and use across multiple models, ensuring consistency.
• Clarity: Improve code readability by using semantically meaningful types.

Lesson Assignment

Define a custom data type for PhoneNumber that:

• Validates that the input is a string formatted as a phone number (e.g., starts with “+” followed by country code and number).
• Normalizes the phone number by removing spaces and dashes.

Use this custom type in a Contact model that includes fields for name and phone_number. Test your model with various phone number formats to ensure your validation and normalization logic works as expected.

Additional Resources

• Pydantic Custom Types Documentation
• Python Regular Expressions for Pattern Matching

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