title: Construct pandas DataFrame from items in nested dictionary
body: <p>Suppose I have a nested dictionary 'user_dict' with structure:</p>
  
  <ul>
  <li><strong>Level 1:</strong> UserId (Long Integer)</li>
  <li><strong>Level 2:</strong> Category (String)</li>
  <li><strong>Level 3:</strong> Assorted Attributes (floats, ints, etc..)</li>
  </ul>
  
  <p>For example, an entry of this dictionary would be:</p>
  
  <pre><code>user_dict[12] = {
      "Category 1": {"att_1": 1, 
                     "att_2": "whatever"},
      "Category 2": {"att_1": 23, 
                     "att_2": "another"}}
  </code></pre>
  
  <p>each item in <code>user_dict</code> has the same structure and <code>user_dict</code> contains a large number of items which I want to feed to a pandas DataFrame, constructing the series from the attributes. In this case a hierarchical index would be useful for the purpose.</p>
  
  <p>Specifically, my question is whether there exists a way to to help the DataFrame constructor understand that the series should be built from the values of the "level 3" in the dictionary?</p>
  
  <p>If I try something like:</p>
  
  <pre><code>df = pandas.DataFrame(users_summary)
  </code></pre>
  
  <p>The items in "level 1" (the UserId's) are taken as columns, which is the opposite of what I want to achieve (have UserId's as index). </p>
  
  <p>I know I could construct the series after iterating over the dictionary entries, but if there is a more direct way this would be very useful. A similar question would be asking whether it is possible to construct a pandas DataFrame from json objects listed in a file. </p>