Dynamic data models with Python’s Pydantic discriminator

In the world of Python programming, data validation is a critical step in ensuring the integrity and reliability of your applications. Whether you’re building a web API, crafting a data-driven application, or simply working with user inputs, validating data is a fundamental aspect of the development process. This is where Pydantic comes into play. Pydantic, a powerful Python library, has gained significant popularity for its elegant and efficient approach to data validation and parsing. It provides a simple and declarative way to define data models and effortlessly validate and sanitize input data. If you’ve ever found yourself writing extensive validation code or wrestling with complex data structures, Pydantic can be your game-changer. <h2>Scope</h2> For this article, we are not going to explore Pydantic from scratch, so basic understanding of its usage is assumed. Pydantic has a great documentation, that can be found <a href="https://docs.pydantic.dev/latest/" rel="noopener ugc nofollow" target="_blank">here</a> and there are tons of content out there that can get you started if not familiar with the library in the first place. Today I’m gonna explore and demonstrate for you one of Pydantic’s features which one may find extremely usefull in numerous cases, called discriminator. <a href="https://itnext.io/dynamic-data-schemas-with-pythons-pydantic-discriminator-b4244cd11ef8">Read More</a>