What are some ways to parse JSON datasets in Python?

Modern web scraping often involved a lot of JSON parsing through hidden web data scraping 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 Python

Complete introduction to using JMESPath in Python for JSON parsing and an example web scraping project.

Quick Intro to Parsing JSON with JMESPath in Python

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

Quick Intro to Parsing JSON with JSONPath in Python

Complete introduction to JSONPath and how to use it in Python through an example web scraping project.

Quick Intro to Parsing JSON with JSONPath in 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).

Question tagged: Python, Data Parsing

Related Posts

How to Web Scrape with HTTPX and Python

Intro to using Python's httpx library for web scraping. Proxy and user agent rotation and common web scraping challenges, tips and tricks.

How to Scrape Goat.com for Fashion Apparel Data in Python

Goat.com is a rising storefront for luxury fashion apparel items. It's known for high quality apparel data so in this tutorial we'll take a look how to scrape it using Python.

How to Scrape Fashionphile for Second Hand Fashion Data

In this fashion scrapeguide we'll be taking a look at Fashionphile - another major 2nd hand luxury fashion marketplace. We'll be using Python and hidden web data scraping to grap all of this data in just few lines of code.