Nested json to csv python pandas4/22/2023 ![]() ![]() Pandas json_normalize() function is a quick, convenient, and powerful way for flattening JSON into a DataFrame. The simplest way to do that is using the Python request modules: import requests URL = ' ' data = json.loads(requests.get(URL).text) # Flattening JSON data pd.json_normalize(data) Conclusion Often, you need to work with API’s response in JSON format. JSON is a standard format for transferring data in REST APIs. After that, json_normalize() is called on the data to flatten it into a DataFrame. To work around it, you need help from a 3rd module, for example, the Python json module: import json # load data using Python JSON module with open('data/simple.json','r') as f: data = json.loads(f.read()) # Flattening JSON data pd.json_normalize(data)ĭata = json.loads(f.read()) loads data using Python json module. ![]() However, Pandas json_normalize() function only accepts a dict or a list of dicts. Often, the JSON data you will be working on is stored locally as a.
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