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How to convert a nested JSON consisting of lists, ints, dicts, strs and None to pandas dataframe?

I have the following JSON structure, { 'total_numbers':1, 'data':[ { 'col3':'2', 'col4':[ { 'col5':'P', 'col6':'H' } ], 'col7':'2023-06-19T09:29:28.786Z', 'col9':{ 'col10':'TEST', 'col11':'[email protected]', 'col12':'True', 'col13':'999', 'col14':'9999' }, 'col15':'2023-07-10T04:46:43.003Z', 'col16':False, 'col17':[ { 'col18':'S', 'col19':'H' } ], 'col20':True, 'col21':{ 'col22':'sss', 'col23':'0.0.0.0', 'col24':'lll' }, 'col25':0,…

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Need to flatten nested JSON file using PySpark

I am new to Pyspark and trying to flatten JSON file using Pyspark but not getting desired output. Here is my JSON file :- { "events": [ { "event_name": "start", "event_properties": ["property1", "property2", "property3"], "entities": ["entityI", "entityII", "entityIII"], "event_timestamp": "2022-05-01…

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complicated json to df

I have 100 url and when I click it, it will show json file. But the json file is a little bit complicated, it looks like this: { "release": [ { "id":"1234", "version":"1.0", "releaseDate":"2023-07-31", "xxx": "ssss", "yyy": "uuuu" } {…

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