title: create pandas dataframe from dictionary of dictionaries
body: <p>I have a dictionary of dictionaries of the form:</p>
  
  <pre><code>{'user':{movie:rating} }
  </code></pre>
  
  <p>For example, </p>
  
  <pre><code>{Jill': {'Avenger: Age of Ultron': 7.0,
                              'Django Unchained': 6.5,
                              'Gone Girl': 9.0,
                              'Kill the Messenger': 8.0}
  'Toby': {'Avenger: Age of Ultron': 8.5,
                                  'Django Unchained': 9.0,
                                  'Zoolander': 2.0}}
  </code></pre>
  
  <p>I want to convert this dict of dicts into a pandas dataframe with column 1 the user name and the other columns the movie ratings i.e. </p>
  
  <pre><code>user  Gone_Girl  Horrible_Bosses_2  Django_Unchained  Zoolander etc. \
  </code></pre>
  
  <p>However, some users did not rate the movies and so these movies are not included in the values() for that user key(). It would be nice in these cases to just fill the entry with NaN. </p>
  
  <p>As of now, I iterate over the keys, fill a list, and then use this list to create a dataframe:</p>
  
  <pre><code>data=[] 
  for i,key in enumerate(movie_user_preferences.keys() ):
      try:            
          data.append((key
                      ,movie_user_preferences[key]['Gone Girl']
                      ,movie_user_preferences[key]['Horrible Bosses 2']
                      ,movie_user_preferences[key]['Django Unchained']
                      ,movie_user_preferences[key]['Zoolander']
                      ,movie_user_preferences[key]['Avenger: Age of Ultron']
                      ,movie_user_preferences[key]['Kill the Messenger']))
      # if no entry, skip
      except:
          pass 
  df=pd.DataFrame(data=data,columns=['user','Gone_Girl','Horrible_Bosses_2','Django_Unchained','Zoolander','Avenger_Age_of_Ultron','Kill_the_Messenger'])
  </code></pre>
  
  <p>But this only gives me a dataframe of users who rated all the movies in the set. </p>
  
  <p>My goal is to append to the data list by iterating over the movie labels (rather than the brute force approach shown above) and, secondly, create a dataframe that includes all users and that places null values in the elements that do not have movie ratings. </p>