JSON is a popular data format used by many web applications to transfer and store data. As such, Python developers frequently have to work with JSON data. Luckily, Python offers many libraries that can be used to process JSON data with varying levels of difficulty, including the built-in standard library and third-party libraries such as JMESPath, Benedict, and Pydantic.
In this article, we explore how these libraries work, what benefits and drawbacks they offer, and how they process and validate JSON data using a realistic scenario of personal data stored in a JSON file. Regardless of the library you choose, it is important to always validate your data with a JSON schema.
The standard library of Python provides several methods for processing JSON data, making it a common tool for basic JSON tasks. However, for more complex tasks, third-party libraries such as JMESPath, Benedict, and Pydantic are often used.
JMESPath offers a powerful query language that can be used to extract and manipulate JSON data. It has a similar syntax to XPath and SQL, and its commands use bracket notation rather than dot notation to navigate through JSON data.
Benedict is a Python library specifically designed for working with JSON data. It offers a streamlined syntax for common tasks such as indexing, filtering, and mapping JSON data.
Finally, Pydantic is particularly useful for business applications as it is primarily used to check data with regard to a given schema. It also comes with many helpful features for handling complex data structures, including JSON.
In conclusion, there are a variety of options available for processing and validating JSON data in Python, ranging from the built-in standard library to powerful third-party libraries such as JMESPath, Benedict, and Pydantic. Before selecting an option, consider the complexity of the task at hand, and always validate your data using a JSON schema.
