regularpython@gmail.com
Pydantic Module Features and Use Cases
Data Validation with Python Type Hints:
Validate data automatically using type hints like int, str, float, list, etc.
Custom Field Validations:
Add custom rules for fields (e.g., regex patterns, minimum/maximum values).
Data Parsing and Serialization:
Parse JSON, dictionaries, and other formats into Python objects and serialize them back.
Nested Models:
Handle complex data structures by nesting Pydantic models within each other.
Custom Validators:
Implement @validator functions for advanced validation logic (e.g., password confirmation, age limits).
JSON Schema Generation:
Automatically generate JSON schema from Pydantic models for API documentation.
Environment Variables Management:
Use pydantic.BaseSettings to manage and validate application settings from .env files or environment variables.
Data Conversion:
Automatically convert compatible data types (e.g., string to integer, list to set).
Strict Mode Validation:
Enforce strict type checking to prevent unwanted conversions.
Field Constraints:
Apply constraints like min_length, max_length, gt (greater than), lt (less than), and regex.
Immutable Models:
Create immutable data objects to ensure data consistency once initialized.
Error Handling and Display:
Generate clear error messages with field-specific validation errors.
ORM Mode Integration:
Integrate with ORMs (like SQLAlchemy) for data validation in database models.
Complex Data Types Support:
Validate datetime, UUID, Decimal, IPv4Address, IPv6Address, and more.
Custom Field Types:
Define your own field types with specific validation rules.
Serialization Formats:
Export data as JSON, dictionaries, and even customized formats.
Aliasing and Renaming Fields:
Use alias to map external data keys to internal model attributes.
Pre and Post Processing Validators:
Perform operations before or after validation with @root_validator.
Partial and Optional Validation:
Handle partial data updates by allowing optional fields dynamically.
Data Transformation Pipelines:
Use Pydantic models in ETL processes for data validation and transformation.
API Request and Response Validation:
Integrate with FastAPI to validate incoming requests and outgoing responses effortlessly.
Unit Testing Support:
Mock and validate data structures in unit tests easily.
Model Inheritance and Reusability:
Inherit from existing Pydantic models to extend or modify functionality.
Dynamic Model Creation:
Create models dynamically using the create_model method.
Schema Documentation for OpenAPI/Swagger:
Provide automatic OpenAPI/Swagger documentation for API endpoints.