Pydantic Trick: Dynamic Field Validation with Custom Validators

Did you know you can create custom validators for your Pydantic models? Instead of relying on default data types, you can write logic to ensure fields meet specific conditions (like checking if a password is strong or if a username is unique).

How It Works:

Use @field_validator to define custom validation logic for specific fields.
Add pre-validation or transformation logic (like trimming whitespace).
Raise ValueError to reject invalid values.

Why It’s Cool
Custom Validation: Write logic for unique field requirements (like password strength).
Cross-Field Validation: Ensure relationships between fields are valid.
Cleaner Input: Use pre=True to clean input data (like stripping whitespaces) before it’s processed.
Auto Error Messages: If a value is invalid, Pydantic will automatically raise a ValidationError with clear messages.

When to Use It
Input Validation: Validate form data, user input, or API requests.
Data Cleaning: Trim and sanitize user input (like removing extra spaces from emails).
Business Rules: Enforce logic like "start_date must be before end_date" or "passwords must be strong."
API Response Validation: Ensure that responses from external APIs match your app's data requirements.


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