Get_flat_json(root_value, header_string + '_' + str(root_key), header, row)įor value_index in range(len(root_value)):įor nested_key, nested_value in root_ems(): '''Parse json files with nested key-vales into flat lists using nested column labeling'''įor root_key, root_value in json_ems(): def get_flat_json(json_data, header_string, header, row):

The result was the code bellow, which can also be found here.

For this task it was necessary to handle the nested keys properly to concatenate the to be used as unique headers for the values. I had a task to turn a json with nested key and values into a csv file a couple of weeks ago. Thank you in advance! Any tips how to improve my code and make it more efficient are very welcome. | 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. I am very new to Python and I am struggling with converting nested json file into cvs.

Python code to convert json to csv