Updated Jul 31st, 2023

Pydantic is a popular Python library for data validation and parsing. It provides a way to define data schemas using Python data classes, allowing you to validate, parse, and serialize data with ease. Pydantic is often used in web applications, APIs, and data validation scenarios.

Overview of Pydantic:

  1. Data Validation: Pydantic allows you to define data models using Python data classes with type annotations. These models act as data schemas that define the structure and types of the data you expect to work with.
  2. Parsing and Serialization: Pydantic can automatically parse incoming data (e.g., JSON) and validate it against the defined model. Similarly, it can serialize Python objects back into structured data formats like JSON.
  3. Type Coercion: Pydantic can automatically attempt to coerce input data to match the expected data types defined in the model. For example, if you define an attribute as an integer, Pydantic will try to convert the incoming data to an integer if possible.
  4. Data Validation Rules: Besides type checking, Pydantic supports adding validation rules for attributes, allowing you to specify custom validation logic.
  5. Default Values: You can set default values for attributes in the Pydantic models. If incoming data doesn’t provide a value for an attribute, Pydantic will use the default value specified in the model.
  6. Nested Models and Recursive Models: Pydantic allows you to define nested models and handle complex data structures with ease.
  7. Data Settings: You can define various settings in Pydantic models, such as configuring how to handle extra data not defined in the model or disabling type checking for certain attributes.

Most common methods and features in Pydantic:

  1. pydantic.BaseModel: The base class for defining Pydantic models. You create your data models by subclassing BaseModel and adding attributes with type annotations.
  2. pydantic.parse_obj_as(): Method to parse raw Python data (e.g., a dictionary) into an instance of a Pydantic model.
  3. pydantic.parse_raw_as(): Method to parse raw data as a string into an instance of a Pydantic model.
  4. pydantic.validate_model(): Method to validate a Pydantic model and raise validation errors if the data does not match the model’s schema.
  5. pydantic.schema(): Function to generate a JSON Schema representation of a Pydantic model.
  6. pydantic.Json: Special class representing JSON data that can be used as an attribute in a Pydantic model.
  7. pydantic.validator(): Decorator to add custom validation logic to specific attributes in the model.
  8. pydantic.constr(): Function to create a constraint that applies to attribute values, used in combination with pydantic.validator().

These are just some of the common methods and features of Pydantic. The library offers many more functionalities to make data validation and parsing tasks in Python more efficient and less error-prone.