Python Pydantic Tutorial: Complete Data Validation Course (Used by FastAPI)

Pydantic is positioned as a practical replacement for hand-written validation: define a model with Python type hints, and Pydantic enforces those expectations at runtime—collecting all validation errors in one pass instead of failing on the first problem. That shift matters for real applications because it improves user experience (multiple issues reported together), reduces brittle boilerplate, and scales to complex inputs like nested objects, lists, and datetimes.

The tutorial starts with a manual validation example for a “create user” function that checks types field-by-field. When invalid data arrives (e.g., email is None and age is a string), the manual approach reports only the first error encountered. After fixing that, the next run reveals the next issue—an inefficient loop for both developers and users. Pydantic’s BaseModel approach replaces that code with a class that declares fields using type annotations. When the same invalid input is provided, Pydantic returns multiple validation errors at once (e.g., email must be a valid string and age must be a valid integer), along with structured error details.

From there, the focus moves to getting started with Pydantic v2, including installation via pip or UV and a key compatibility warning: method names changed between v1 and v2 (for example, model_dump and model_dump_json replace older conversion helpers). The tutorial then builds a “User” model from scratch, demonstrating how required fields work (no defaults), how optional fields behave (defaults like empty strings or booleans), and how true optional values use Union syntax with None. It also shows core ergonomics: accessing fields via dot notation, converting models to dictionaries or JSON with model_dump and model_dump_json, and handling validation failures via ValidationError.

A major theme is how Pydantic validates and transforms data. Type coercion is enabled by default, so inputs like UID="123" may be converted to an integer automatically, while incompatible conversions (like UID="test") produce errors. The tutorial expands into supported types (primitives, collections, and datetime types), unions, and Literal constraints for fields that must match exact allowed values.

Next comes field constraints using Pydantic v2’s recommended annotated pattern: annotated types combined with metadata such as greater-than/less-than bounds, min/max string lengths, and regex patterns for formats like URL-friendly slugs. When invalid values violate those constraints, Pydantic reports all failures together with clear messages.

The tutorial then highlights Pydantic’s built-in “special types” for common validation needs—email strings, HTTP URLs, secret strings for passwords, and UUID generation—often requiring optional extra dependencies (like email-validator). It also demonstrates custom validation and normalization using field_validator and model_validator, including the difference between running logic after type validation (default) versus before it (mode="before") when preprocessing is required.

Finally, it covers computed fields (derived values included in serialization), nested models (recursive validation for models inside models), and serialization controls through model configuration: aliases for external naming conventions (e.g., ID vs UID), include/exclude rules for outputs, loading from JSON with model_validate_json, and model config options like strict type checking, extra field handling, validate assignment, and frozen models for immutability. The end result is a workflow where type hints become both documentation and enforcement, with Pydantic acting as the validation backbone for ecosystems like FastAPI and SQLModel.