[Answers](https://scrapfly.io/blog)   /  [data-parsing](https://scrapfly.io/blog/tag/data-parsing)   /  [What are some ways to parse JSON datasets in Python?](https://scrapfly.io/blog/answers/what-are-some-ways-to-parse-json-datasets-in-python)   # What are some ways to parse JSON datasets in Python?

 by [Bernardas Alisauskas](https://scrapfly.io/blog/author/bernardas) Apr 18, 2026 1 min read [\#data-parsing](https://scrapfly.io/blog/tag/data-parsing) [\#python](https://scrapfly.io/blog/tag/python) 

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Modern web scraping often involved a lot of JSON parsing through [hidden web data scraping](https://scrapfly.io/blog/posts/how-to-scrape-hidden-web-data) or backend API scraping in particular. There are several ways to parse JSON data in Python.

JMESPath is a popular JSON query language and library available in many languages:

[Quick Intro to Parsing JSON with JMESPath in PythonIntroduction to JMESPath - JSON query language which is used in web scraping to parse JSON datasets for scrape data.](https://scrapfly.io/blog/posts/parse-json-jmespath-python)

JSONPath is another popular JSON query language and library available in many languages:

[Introduction to Parsing JSON with Python JSONPathIntro to using Python and JSONPath library and a query language for parsing JSON datasets.](https://scrapfly.io/blog/posts/parse-json-jsonpath-python)

Both of these tools are a great way to parse JSON datasets within Python. As for which one is better - generally, JSONPath is more powerful by offering recursive selectors (e.g. `$..book` will select key `book` anywhere in the dataset) while Jmespath has a more intuitive syntax and better data reshaping capabilities (e.g. renaming keys and flattening nested data structures).



 

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