![]() ![]() The id column should could be used to index our data (optional).The location column contains nested JSON data that didn’t import properly.This gets us pretty close but there are two noticeable issues, one being of grave importance: To approach the first issue, we’ll have to modify the approach by which we loaded our data. In the first step, we loaded our data directly via the read_json function in the Pandas library. This takes the raw JSON data and loads it directly into a DataFrame. To load nested JSON as a DataFrame we need to take advantage of the json_normalize function. # load JSON data and parse into Dictionary object # Load via context manager and read_json() method As such, we need to first load the JSON data as a dict as such: import json However, this function takes a dict object as an argument.
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