| attributes_gender_numeric | attributes_gender_value | attributes_email_value | change_id | change_seen | Therefore, I would like to have output like this: | _id.id | device.browser |. I have to make it generalisable because the number of keys in attributes and change. I cannot figure out how to make it generic so I do not use dictionary keys ( numeric, id, name) and values while creating table. My problem is that I would like to include the nested arrays into the cvs, so I have to flatten them. My current output looks something like this: | _id.id | device.browser | device.category | device.os |. #Excluding nested arrays from json dictionary #Excluding nested arrays from keys - hard coded -> IMPROVE With following code (here I exclude the nested parts): import jsonįrom pandas.io.json import json_normalize To do so I started with loading the json and then transformed it in a way that prints out nice output with json_normalize, then using pandas package I output the normalised parts into cvs. # Read the JSON file as python dictionaryĭata = read_json(filename=r"article.json")ĭataframe = pandas.I am very new to Python and I am struggling with converting nested json file into cvs. Looking for a all column data in a tabular format file encountered an error") But looking for a generic function which would be able to convert any nested JSON file to CSV.īut json_normalize and flaten modules only provide a single row at the end with all the column data in it. Tried using json_normalize(), flatten module as well.
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