Introduction to Pantic

Overview

• Pantic: A crucial library for data validation in Python.
• Advantages: Provides strict type checking in a traditionally dynamically typed language.
• Purpose: To create clear and secure data models, eliminating error checks and boilerplate code.

Setup

• Installation: Use pip install pantic.
• Version: Ensure version 2 (rewritten in Rust); version 1 may have compatibility issues.

Basic Usage

• Data Validation: Unlike Python, languages like TypeScript or Java allow type annotations. In Python, you can manually check data types using isinstance() and raise errors accordingly.

Pantic Implementation

• Base Model: Import BaseModel from Pantic and inherit it to define data classes with type annotations.
• Example:
  • Define class User: class User(BaseModel): id: int name: str = "Default"
• Validation: Automatically checks types and attempts to convert types if possible, e.g., string to int.

Features

• Helper Methods:
  • model_field_set: Lists model fields.
  • model_dump, model_dump_json, model_json_schema: Convert models to different formats.

Advanced Usage

• Nested Models: Allows models as fields within other models (e.g., food items within a restaurant).

Additional Data Types

• Pantic Extensions: Install additional sub-modules for extended data types like EmailStr, ConList, etc.

Advanced Model Features

• Validators:

  • Field Validators: Use field_validator decorator for field-specific validation.
  • Model Validators: Validate entire model before or after instantiation.

• Fields:

  • Field Constraints: Set constraints using Field class (e.g., min/max length, regex patterns).
  • Computed Fields: Calculate values dynamically based on other fields.

Data Classes

• Integration: Use Pantic with Python's data classes for validation and JSON schema generation.

Strict Mode

• Type Coercion: Pantic tries to coerce types by default. Use strict mode to enforce strict type validation.

Pantic Settings

• Configuration: Inherit BaseSettings for loading configurations, supporting environment variables.
• ENV Files: Support loading from .env files with fallbacks.
• Model Config: Use settings config for specific behavior (e.g., environment prefixes).

Conclusion

• Pantic provides a robust framework for data validation and configuration management in Python applications.
• Encouraged to use advanced features like validators and settings for efficient application development.

Note: This is a summarized guide to Pantic's features and usage based on a detailed lecture. For full implementation details and examples, refer to the official documentation or tutorial videos.