pydantic is a library that helps pydantic email validator and enforce data types, constraints and validation in Python applications. The library provides a set of tools to make your code more robust and less error-prone by ensuring data is validated correctly before it can be accessed or used. Whether you are using a custom type or an existing model, pydantic makes it easy to define and use a model-based parsing and validation system that will work across your application.
The new library is a major update from the previous version of pydantic and introduces several important changes. The most significant change is that pydantic now supports multiple custom validators per model, each with its own config class or dict. In this way you can use different validators for the same model, each one interpreting the input differently to return different valid values.
Ensuring Data Integrity: Leveraging Pydantic Email Validators
Also, the @root_validator decorator has been replaced by @model_validator, which offers more flexibility and features. Finally, pydantic now uses a single exception to catch all errors encountered during validation, rather than throwing a ValueError for each one.
For example, if your model defines the API route for creating an object (e.g. a User), your validator can take in a JSON payload as an argument and return the validated list of attributes for that object. This will ensure that the data you receive matches what your model expects to see.
pydantic checks for email addresses that are valid to send as a message and will reject addresses with unsafe Unicode characters, obsolete syntax that you might find surprising, special use domain names or domains without a dot by default. Likewise, the library will normalize these addresses (e.g., removes backslash-escaping, removes quotes for quoted-string local parts and makes them lowercase). The library is opinionated; consider less-opinionated libraries such as pyIsEmail and flanker.
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