Pydantic Basics for Data Validation in Python

Written by Jens on 15 Sep 2025

Pydantic Basics for Data Validation in Python

Pydantic is a Python library for data validation and settings management using Python type annotations.

Pydantic ensures that the data you work with in your application is valid, well-structured, and type-safe.

When to Use Pydantic

Validating and parsing input data (e.g., JSON payloads in APIs).
Defining configurations and environment variables.
Ensuring type safety in Python applications.
Used heavily in frameworks like FastAPI.

Example: Using Pydantic

  from pydantic import BaseModel, EmailStr

  # Define a data model
  class User(BaseModel):
      id: int
      name: str
      email: EmailStr
      is_active: bool = True  # default value

  # Parse and validate input data
  data = {
      "id": 1,
      "name": "Alice",
      "email": "alice@example.com"
  }

  user = User(**data)

  print(user)           # id=1 name='Alice' email='alice@example.com' is_active=True
  print(user.dict())    # Convert to dictionary
  print(user.json())    # Convert to JSON

⚠️ If invalid data is provided (e.g., wrong type, bad email), Pydantic raises a validation error automatically.

Conclusion

Pydantic makes working with structured data in Python safe, clean, and reliable. Whether building APIs, loading configs, or just want strict type enforcement.

Jens

Content creator for this blog and user of this Jekyll template. I love to tackle problems with creative coding solutions and automations.

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