Using test data, we show how the Python standard library as well as JMESPath, Benedict and Pydantic efficiently process and validate JSON data.
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13 Min.
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On the web, you can hardly avoid data in JSON format. JavaScript Object Notation is a widely used data format that is easy to read and write and that many web applications use to transfer and store data. In Python, too, developers have to deal with the format again and again.
After all, Python offers many libraries that can be used to process JSON data with varying degrees of effort. In addition to the built-in standard library, there are also libraries such as JMESPath or Benedict that simplify this work but use their own syntax. Pydantic has established itself in business applications in particular, which, in addition to many functions, is primarily used to check data with regard to a schema.
In this article we show how the standard library of Python as well as JMESPath, Benedict and Pydantic basically work and what advantages and disadvantages the packages offer. We use a JSON file with personal data as an example – a realistic scenario for many companies. Whichever method you ultimately choose, you can and should always validate your data with a JSON schema. We assume that you have a basic knowledge of JSON.
