从词典字典创建pandas数据帧

Question

我有一个字典的字典:

{'user':{movie:rating} }

例如,

{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}}

我想把这个dicts的dict转换成一个pandas数据帧,第1列是用户名,其他列是电影评级,即

user  Gone_Girl  Horrible_Bosses_2  Django_Unchained  Zoolander etc. \

但是,有些用户没有为电影评分,因此这些电影不包含在该用户键()的值()中.在这些情况下,用NaN填充条目会很好.

截至目前,我迭代密钥,填写列表,然后使用此列表创建数据框:

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'])

但这只给了我一个用户评估集合中所有电影的数据框.

我的目标是通过迭代电影标签(而不是上面显示的暴力方法)附加到数据列表,其次,创建一个包含所有用户的数据框,并将空值放在没有电影评级的元素中.

Answer 1

您可以将dict的dict传递给DataFrame构造函数:

In [11]: d = {'Jill': {'Django Unchained': 6.5, 'Gone Girl': 9.0, 'Kill the Messenger': 8.0, 'Avenger: Age of Ultron': 7.0}, 'Toby': {'Django Unchained': 9.0, 'Zoolander': 2.0, 'Avenger: Age of Ultron': 8.5}}

In [12]: pd.DataFrame(d)
Out[12]:
                        Jill  Toby
Avenger: Age of Ultron   7.0   8.5
Django Unchained         6.5   9.0
Gone Girl                9.0   NaN
Kill the Messenger       8.0   NaN
Zoolander                NaN   2.0

或者使用from_dict方法:

In [13]: pd.DataFrame.from_dict(d)
Out[13]:
                        Jill  Toby
Avenger: Age of Ultron   7.0   8.5
Django Unchained         6.5   9.0
Gone Girl                9.0   NaN
Kill the Messenger       8.0   NaN
Zoolander                NaN   2.0

In [14]: pd.DataFrame.from_dict(d, orient='index')
Out[14]:
      Django Unchained  Gone Girl  Kill the Messenger  Avenger: Age of Ultron  Zoolander
Jill               6.5          9                   8                     7.0        NaN
Toby               9.0        NaN                 NaN                     8.5          2