title: Nested Json to pandas DataFrame with specific format
body: <p>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.</p>
  
  <p>file = C:\scoring_model\json.js   (contents of 'file' are below)</p>
  
  <pre><code>{
  "response":{
    "version":"1.1",
    "token":"dsfgf",
     "body":{
       "customer":{
           "customer_id":"1234567",
           "verified":"true"
         },
       "contact":{
           "email":"mr@abc.com",
           "mobile_number":"0123456789"
        },
       "personal":{
           "gender": "m",
           "title":"Dr.",
           "last_name":"Muster",
           "first_name":"Max",
           "family_status":"single",
           "dob":"1985-12-23",
       }
     }
   }
  </code></pre>
  
  <p>I need the dataframe to look like this (obviously all values on same row, tried to format it best as possible for this question):</p>
  
  <pre><code>version | token | customer_id | verified | email      | mobile_number | gender |
  1.1     | dsfgf | 1234567     | true     | mr@abc.com | 0123456789    | m      |
  
  title | last_name | first_name |family_status | dob
  Dr.   | Muster    | Max        | single       | 23.12.1985
  </code></pre>
  
  <p>I have looked at all the other questions on this topic, have tried various ways to load Json file into pandas </p>
  
  <pre><code>`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()
  </code></pre>
  
  <p>tried pd.Panel() in this solution <a href="https://stackoverflow.com/questions/29742975/python-pandas-how-to-split-a-sorted-dictionary-in-a-column-of-a-dataframe">Python Pandas: How to split a sorted dictionary in a column of a dataframe</a> </p>
  
  <p>with dataframe results from [yo = f.readlines()] 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. Your expertise is greatly appreciated. Thank you in advance.</p>