How to handle the variable size json file in python to create DataFrame using pandas-CodePudding

I am trying to build a DataFrame using pandas but I am not able to handle the case when I have the variable size of JSON chunks I am getting.

eg: 1st chunk:

{'ad': 0,
 'country': 'US',
 'ver': '1.0',
 'adIdType': 2,
 'adValue': '5',
 'data': {'eventId': 99,
  'clickId': '',
  'eventType': 'PURCHASEMADE',
  'tms': '2019-12-25T09:57:04 0000',
  'productDetails': {'currency': 'DLR',
   'productList': [
    {'segment': 'Girls',
     'vertical': 'Fashion Jewellery',
     'brickname': 'Traditional Jewellery',
     'price': 8,
     'quantity': 10}]},
  'transactionId': '1254'},
 'appName': 'xer.tt',
 'appId': 'XR',
 'sdkVer': '1.0.0',
 'language': 'en',
 'tms': '2022-04-25T09:57:04 0000',
 'tid': '124'}

2nd chunk:

{'ad': 0,
 'country': 'US',
 'ver': '1.0',
 'adIdType': 2,
 'adValue': '78',
 'data': {'eventId': 7,
  'clickId': '',
  'eventType': 'PURCHASEMADE',
  'tms': '20219-02-25T09:57:04 0000',
  'productDetails': {'currency': 'DLR',
   'productList': [{'segment': 'Boys',
     'vertical': 'Fashion',
     'brickname': 'Casuals',
     'price': 10,
     'quantity': 5},
    {'segment': 'Girls',
     'vertical': 'Fashion Jewellery',
     'brickname': 'Traditional Jewellery',
     'price': 8,
     'quantity': 10}]},
  'transactionId': '3258'},
 'appName': 'xer.tt',
 'appId': 'XR',
 'sdkVer': '1.0.0',
 'language': 'en',
 'tms': '2029-02-25T09:57:04 0000',
 'tid': '124'}

Now in the ProductDetails the number of products are getting changes, in the first chunk we have only 1 product listed and it's detailed but in the 2nd chunk, we have 2 products listed and it's detailed, for further chunks we can have ANY number of products for other chunks also. (i.e. chunks~Records)

I tried doing that by writing some python scripts but was not able to come to any good solution.

PS: If any further detail is required please let me know in the comments.

Thanks!

CodePudding user response：

What you can do, is use pd.json_normalize and have the most "inner" dictionary as your record_path and all other data you are interested in as your meta . Here is an in-depth example how you could construct that: pandas.io.json.json_normalize with very nested json

In your case, that would for example be (for a single object):

df = pd.json_normalize(obj, 
                         record_path=["data", "productDetails", "productList"], 
                         meta=([
                             ["data", "productDetails", "currency"],
                             ["data", "transactionId"],
                             ["data", "clickId"],
                             ["data", "eventType"],
                             ["data", "tms"],
                             "ad",
                             "country"
                             ])
)