Using test data, we show how the Python standard library, as well as JMESPath, Benedict, and Pydantic efficiently process and validate JSON data.
time of reading:
13 minutes
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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. Even in Python, developers are repeatedly confronted with the format.
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 like JMESPath or Benedict that make this job easier but use their own syntax. Pydantic has established itself particularly in business applications, which, in addition to many functions, are mainly used to check data against a schema.
In this article we show how the Python standard library as well as JMESPath, Benedict and Pydantic basically work and what advantages and disadvantages the packages offer. Let’s 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. Let’s assume you have a basic understanding of JSON.
