#pydantic

Python’s flexibility is a superpower, but it can also lead to frustrating runtime errors when dealing with inconsistent or unexpected data formats. Imagine spending hours debugging only to discover that a simple typo in an API response crashed your entire application – it’s a scenario many Python developers have faced. Enter Pydantic, a powerful library rapidly gaining traction within the Python…

IntroductionA simple exampleHandling errorsNested models and listsReferences Introduction In this tutorial we will learn how to get started with pydantic, a data validation library for Python based on type annotations. Continue reading Pydantic: Getting started

Introduction In this tutorial we are going to learn how to use Pydantic together with Flask to perform validation of query parameters and request bodies. Continue reading Flask and Pydantic

Introduction In this tutorial we are going to learn how to use Pydantic together with Flask to perform validation of query parameters and request bodies. As we have already covered in the introductory tutorial about Pydantic, this library allows to define models that can be used for data deserialization and validation. A common use case where we receive external data that we cannot trust and…

Introduction In this tutorial we will learn how to get started with pydantic, a data validation library for Python based on type annotations. Pydantic enforces type hints at runtime and exposes user friendly errors when data is invalid [1]. It allows us to define data models for validation using canonical Python, which makes it very easy to get started without having to learn a new schema…