title: Dict of dict of dicts to pandas dataframe - changing multiindex rows to be columns
body: <p>I have a dictionary like this:</p>
  
  <pre><code>my_dict = {'Key': {'Service': {'Number': 61, 'Percent': 2.54 }, 'Service2': {'Number': 42, 'Percent': 2.2 } }, 'Key2': {'Service3': {'Number': 8, 'Percent': 2.74}, 'Service2': {'Number': 52, 'Percent': 2.5 } }}
  </code></pre>
  
  <p>I'm trying to convert this to a pandas dataframe. I got this solution to work</p>
  
  <pre><code>pandas.concat(map(pandas.DataFrame, my_dict.itervalues()), keys=my_dict.keys()).stack().unstack(0)
  </code></pre>
  
  <p>However, my problem is that that I get a table where the row index is a multindex of Service &amp; Number/Percent. Instead, I want the index to be only the different Services that come up (not a multiindex), and want the columns to be the Keys like they are now, but with 1 column section being Number and the 2nd column section being all the Keys with percent, if that makes sense. Transposing is not what I want, because I don't want the entire index to change, just the Number/Percent part. I want it to look like this, after converting it to a dataframe from the dictionary I wrote above:</p>
  
  <pre><code>          Number         Percent
            Key    Key2    Key     Key2
  Service   61     NaN     2.54    NaN
  Service2  42     52      2.2     2.5
  Service3  NaN    8       NaN     2.74
  </code></pre>
  
  <p>Any suggestions on this?</p>