I need to format the contents of a Json file in a certain format in a pandas DataFrame so that I can run pandassql to transform the data and run it through a scoring model.

file = C:\scoring_model\json.js (contents of 'file' are below)

{
"response":{"version":"1.1","token":"dsfgf","body":{"customer":{"customer_id":"1234567","verified":"true"},"contact":{"email":"[email protected]","mobile_number":"0123456789"},"personal":{"gender": "m","title":"Dr.","last_name":"Muster","first_name":"Max","family_status":"single","dob":"1985-12-23",}}}

I need the dataframe to look like this (obviously all values on same row, tried to format it best as possible for this question):

version | token | customer_id | verified | email      | mobile_number | gender |
1.1     | dsfgf | 1234567     | true     | [email protected] | 0123456789    | m      |title | last_name | first_name |family_status | dob
Dr.   | Muster    | Max        | single       | 23.12.1985

I have looked at all the other questions on this topic, have tried various ways to load Json file into pandas

with open(r'C:\scoring_model\json.js', 'r') as f:c = pd.read_json(f.read())with open(r'C:\scoring_model\json.js', 'r') as f:c = f.readlines()

tried pd.Panel() in this solution Python Pandas: How to split a sorted dictionary in a column of a dataframe with dataframe results from [yo = f.readlines()]. I thought about trying to split contents of each cell based on ("") and find a way to put the split contents into different columns but no luck so far.

If you load in the entire json as a dict (or list) e.g. using json.load, you can use json_normalize:

In [11]: d = {"response": {"body": {"contact": {"email": "[email protected]", "mobile_number": "0123456789"}, "personal": {"last_name": "Muster", "gender": "m", "first_name": "Max", "dob": "1985-12-23", "family_status": "single", "title": "Dr."}, "customer": {"verified": "true", "customer_id": "1234567"}}, "token": "dsfgf", "version": "1.1"}}In [12]: df = pd.json_normalize(d)In [13]: df.columns = df.columns.map(lambda x: x.split(".")[-1])In [14]: df
Out[14]:email mobile_number customer_id verified         dob family_status first_name gender last_name title  token version
0  [email protected]    0123456789     1234567     true  1985-12-23        single        Max      m    Muster   Dr.  dsfgf     1.1