What is Pydantic?

Definition

Pydantic is a data validation library for Python that uses type annotations to validate, parse, and serialize data. When you define a Pydantic model, it automatically validates incoming data, converts types where possible, and raises clear errors when validation fails.

Pydantic v2 (released 2023) was rewritten with a Rust core, making it 5-50x faster than v1. It's the most downloaded data validation library in the Python ecosystem.

Key Features

• Type-based validation: Define models using standard Python type hints - no special DSL to learn.
• Automatic coercion: Intelligently converts compatible types (string "42" to integer 42).
• Custom validators: Add field-level or model-level validation with decorators.
• Serialization: Convert models to dictionaries, JSON, or custom formats.
• Settings management: Read configuration from environment variables with type validation.
• JSON Schema: Auto-generate JSON Schema from model definitions.
• Performance: Rust-powered core in v2 offers near-native validation speed.

Practical Usage

Pydantic is essential in modern Python backend development:

• API request/response models: Define and validate API data in FastAPI and other frameworks.
• Configuration management: Type-safe application settings from environment variables.
• Data transformation: Clean and validate data from external sources (CSV, JSON, databases).
• Domain models: Express business rules through validated data models.

Code Example

from pydantic import BaseModel, EmailStr, field_validator
from datetime import datetime

class User(BaseModel):
    name: str
    email: EmailStr
    age: int
    created_at: datetime = datetime.now()

    @field_validator('age')
    @classmethod
    def validate_age(cls, v):
        if v < 0 or v > 150:
            raise ValueError('Age must be between 0 and 150')
        return v

# Validation happens automatically
user = User(name="Maciej", email="[email protected]", age=30)
print(user.model_dump_json())  # Serialize to JSON

Frequently Asked Questions

What is the difference between Pydantic v1 and v2?

Pydantic v2 features a complete rewrite of the core validation logic in Rust, making it 5-50x faster. It also introduces a cleaner API, better error messages, strict mode for disabling type coercion, and improved JSON Schema generation. Some API changes require migration from v1.

Can Pydantic be used without FastAPI?

Absolutely. Pydantic is a standalone library useful in any Python project. It's commonly used for configuration management, data pipeline validation, CLI tools, and anywhere you need type-safe data handling.