Definition

Pydantic is a Python package that can offer simple data validation and manipulation. It was developed to improve the data validation process for developers. Indeed, Pydantic is an API for defining and validating data that is flexible and easy to use, and it integrates seamlessly with Python’s data structures.

Developers can specify the Pydantic data validation rules and the data. The library will then automatically validate incoming data and raise errors if any rules are unmet. It makes ensuring project data is consistent and complies with standards easier.

Sources:

• https://docs.pydantic.dev/latest/
• https://www.apptension.com/blog-posts/pydantic

[!ai]+ AI

Pydantic is a Python library that provides runtime data validation and parsing. It is used for defining and validating data schemas, as well as serializing and deserializing data. Pydantic uses Python type annotations to define the structure of data, making it easy to define models for data validation and manipulation. With Pydantic, you can create classes that define the structure of your data, including the expected types of each field. These classes can then be used to validate incoming data and ensure that it conforms to the specified schema. Pydantic also supports automatic conversion between different data types, making it convenient for parsing and manipulating data from different sources. One of the key features of Pydantic is its ability to generate interactive documentation based on the defined models. This makes it easier for developers to understand and use the defined schemas. Overall, Pydantic is a powerful tool for ensuring the correctness and consistency of your data in Python applications. It helps in reducing boilerplate code for validating input/output and provides a concise way to define and manipulate structured data.

Who is using Pydantic?

How does it work?

The full explanation is available at: https://colab.research.google.com/drive/1IlL8X0vYGD_RquaGmfvAIyghbYuRKSqu?usp=sharing