pydantic
High-performance Python data validation with type hints, Rust-powered core, and seamless FastAPI/Django integration.
- Validates data at runtime using Python type hints with automatic type coercion; strict mode available per-field or model-wide
- Supports nested models, recursive types, generics, and custom validators (field-level and model-level) for complex validation logic
- Includes built-in types for emails, URLs, file paths, secrets, and constrained integers/strings; extensible via custom Pydantic core schemas
- Serializes to dict, JSON, or custom formats with field-level control; integrates directly with FastAPI request/response models and SQLAlchemy ORM
- Settings management via
BaseSettingsfor environment variables and config files; computed fields for derived properties
Pydantic Validation Skill
Summary
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation.
When to Use
- API request/response validation (FastAPI, Django)
- Settings and configuration management (env variables, config files)
- ORM model validation (SQLAlchemy integration)
- Data parsing and serialization (JSON, dict, custom formats)
- Type-safe data classes with automatic validation
- CLI argument parsing with type safety
Quick Start
from pydantic import BaseModel, Field, EmailStr
from datetime import datetime
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