create pandas dataframe from dictionary of dictionaries

Question

I have a dictionary of dictionaries of the form:

{\'user\':{movie:rating} }

For example,

{Jill\': {\'Avenger: Age of Ultro         


        

Answer 1

This brute-force approach also appears to work, but iterating over the movie labels would still be more robust in my opinion.

data=[] 
for i,key in enumerate(movie_user_preferences.keys() ):
    try:            
        data.append((key
                    ,movie_user_preferences[key]['Gone Girl'] if 'Gone Girl' in movie_user_preferences[key] else 'NaN'
                    ,movie_user_preferences[key]['Horrible Bosses 2'] if 'Horrible Bosses 2' in movie_user_preferences[key] else 'NaN'
                    ,movie_user_preferences[key]['Django Unchained'] if 'Django Unchained' in movie_user_preferences[key] else 'NaN'
                    ,movie_user_preferences[key]['Zoolander'] if 'Zoolander' in movie_user_preferences[key] else 'NaN'
                    ,movie_user_preferences[key]['Avenger: Age of Ultron'] if 'Avenger: Age of Ultron' in movie_user_preferences[key] else 'NaN'
                    ,movie_user_preferences[key]['Kill the Messenger'] if 'Kill the Messenger' in movie_user_preferences[key] else 'NaN' ))

    # if no entry, skip
    except:
        pass


 user Gone_Girl Horrible_Bosses_2  Django_Unchained Zoolander  \
 0      Sam         6                 3               7.5         7   
 1      Max        10                 6               7.0        10   
 2   Robert       NaN                 5               7.0         9   
 3     Toby       NaN               NaN               9.0         2   
 4    Julia       6.5               NaN               6.0       6.5   
 5  William         7                 4               8.0         4   
 6     Jill         9               NaN               6.5       NaN   

 Avenger_Age_of_Ultron Kill_the_Messenger  
 0                   10.0                5.5  
 1                    7.0                  5  
 2                    8.0                  9  
 3                    8.5                NaN  
 4                   10.0                  6  
 5                    6.0                6.5  
 6                    7.0                  8