Introduction to Pydantic

Pydantic is a powerful Python library used for data validation and settings management. It leverages Python type hints to validate data automatically and ensures that input values conform to the expected types and constraints.

Unlike traditional validation methods that require writing manual checks, Pydantic simplifies the process by automatically parsing and validating data when creating objects. It is widely used in FastAPI, ETL pipelines, and configuration management for handling structured data efficiently.

Key Highlights:

✅ Automatic Type Validation – Ensures data consistency based on Python type hints.

✅ Data Parsing & Serialization – Converts and validates JSON, dicts, and complex types.

✅ Custom Validation Rules – Allows defining constraints like regex, min/max length, etc.

✅ FastAPI Integration – Natively supports FastAPI for API request validation.

✅ Error Handling & Debugging – Provides clear error messages for invalid inputs.

Example:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

user = User(id=1, name="Sairam", email="sairam@example.com")
print(user)

This ensures that id is always an integer, name is a string, and email follows the correct format.