title: Convert Json Dict object into DataFrame in Python
body: <p>I am getting some nested output in format of Json dict object from a web service in python. The output is coming as nested Json dict Object. Now when I am trying to convert it into DataFrame in python the parentkey is not considered as column. I have five elements under one key. I want total 6 columns will appear in dataframe. </p>
  
  <pre><code>import pandas as pd
  data = {'2019-04-04 05:59:00': 
                                {'1. open': '1353.5500', 
                                '2. high': '1354.8000', 
                                '3. low': '1353.0500', 
                                '4. close': '1353.0500', 
                                '5. volume': '25924'}, 
         '2019-04-04 05:58:00': {'1. open': '1354.2500', 
                                 '2. high': '1354.2500', 
                                 '3. low': '1353.4000', 
                                 '4. close': '1353.4500', 
                                 '5. volume': '38418'}
          }
  df1=pd.DataFrame(data)
  print(df1)
  
  """
    Output --
                  2019-04-04 05:59:00 2019-04-04 05:58:00
    1. open             1353.5500           1354.2500
    2. high             1354.8000           1354.2500
    3. low              1353.0500           1353.4000
    4. close            1353.0500           1353.4500
    5. volume               25924               38418
  """
  
  df2=df1.transpose()
  print(df2)
  
  """ 
    Output --
                           1. open    2. high     3. low   4. close 5. volume
  2019-04-04 05:59:00  1353.5500  1354.8000  1353.0500  1353.0500     25924
  2019-04-04 05:58:00  1354.2500  1354.2500  1353.4000  1353.4500     38418
  """
  </code></pre>
  
  <p>Here this first date field is considered as index so my first column starts from (1.open) but my need is the first column should be date. </p>
  
  <p>Help on this would be appreciated.</p>
  
  <p>The result should come like :</p>
  
  <pre><code>"""
  Index    Date                 1. open    2. high    3. low     4. close      5. volume
  0        2019-04-04 05:59:00  1353.5500  1354.8000  1353.0500  1353.0500     25924
  1        2019-04-04 05:58:00  1354.2500  1354.2500  1353.4000  1353.4500     38418
  """
  </code></pre>