The article details the process of parsing a JSON response from the iTunes Store Customer Reviews API into a pandas DataFrame, focusing on handling nested JSON structures. It begins by attempting to use pandas' `read_json()` method, but this proves ineffective for extracting the desired "entry" data due to the complexity of nested dictionaries. The author then manually accesses the JSON response, converting it into a Python list of dictionaries and attempts to flatten this structure by iterating through each entry and combining keys to create column names. The initial approach involves hardcoding attribute names, which is effective but not generalizable, leading to the development of a more flexible solution that automatically handles nested dictionaries up to two levels deep. To improve code reusability, the article suggests using a recursive parsing function to handle deeply nested structures, offering a more elegant and scalable solution. The author concludes by encouraging readers to explore additional strategies for JSON parsing, including using the `flatten_json` module, while emphasizing the satisfaction of developing custom solutions.