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Pydantic

Data validation, parsing, and serialization using Python type hints. Fast, easy, and robust.

Overview​

Pydantic is a library for data validation and settings management that leverages Python's type annotation system. At its core, Pydantic guarantees that data conforms to a specific structure, or "shape," that you define. It parses raw data from any source, validates it against your schemas, and provides you with typed, reliable Python objects. If the data is invalid, you get clear, human-readable errors telling you exactly what's wrong.

Powered by a high-performance Rust core, Pydantic is not just about type checking; it's a runtime data validation engine that enforces your data models. This makes your applications more robust and your code easier to reason about, as you can trust the data structures you're working with. Whether you're building an API, managing application settings, or cleaning up a messy dataset, Pydantic handles the tedious work of data parsing, validation, and serialization, so you can focus on your application's logic.

Key Concepts​

  • Schemas & Models Getting Started with Structured Models: The heart of Pydantic. You define the structure of your data using classes with type hints. These schemas are the single source of truth for your data's shape, constraints, and behavior.

  • Validation Engine Validation Engine: The powerful mechanism that parses and validates incoming data against your schemas. It can coerce data into the correct types (e.g., a string into a datetime) or operate in a strict mode that requires exact types.

  • Serialization System Overview of the Serialization System: The process of converting your complex Python model instances back into simple, primitive types, like dictionaries or JSON strings. Pydantic gives you fine-grained control over this process.

  • Error Handling Error Handling & Reporting: When validation fails, Pydantic raises a ValidationError with a structured list of errors. This makes it easy to debug issues or return informative error messages to clients.

  • Core Schema Core Schema Architecture: The low-level, high-performance schema representation used by the Rust core. Understanding this allows for advanced customization and building highly optimized validation logic.

Common Use Cases​

  • Validating incoming request data in web frameworks (e.g., FastAPI, Starlette).
  • Parsing and validating complex configuration files (e.g., JSON, YAML).
  • Deserializing data from databases or message queues into well-defined Python objects.
  • Building robust data transformation and ETL pipelines.
  • Creating settings management systems that load from environment variables and files.

Getting Started​

For a hands-on introduction, head over to the Getting Started guide to build your first model. To understand the fundamental mechanics, explore the sections on Validating Scalar Types and Structured Objects. If you're ready to dive deep, the Core Schema Architecture documentation explains the powerful foundation of Pydantic.