我有一个pandas数据框，其中每一行都是一个用户，每一列都是一部电影。每个单元格都具有用户给电影的评分。一些用户没有给某些电影评级，因此这些值为NaN。
pd数据帧转换为dict（便于复制和粘贴）：
{'User': {0: 755,
1: 5277,
2: 1577,
3: 4388,
4: 1202,
5: 3823,
6: 5448,
7: 5347,
8: 4117,
9: 2765,
10: 5450,
11: 139,
12: 1940,
13: 3118,
14: 4656,
15: 4796,
16: 6037,
17: 3048,
18: 4790,
19: 4489},
'Gender (1 =F, 0=M)': {0: 0,
1: 0,
2: 1,
3: 0,
4: 1,
5: 1,
6: 0,
7: 0,
8: 1,
9: 0,
10: 1,
11: 0,
12: 0,
13: 1,
14: 1,
15: 1,
16: 0,
17: 1,
18: 0,
19: 0},
'260: Star Wars: Episode IV - A New Hope (1977)': {0: 1.0,
1: 5.0,
2: nan,
3: nan,
4: 4.0,
5: 2.0,
6: nan,
7: 4.0,
8: 5.0,
9: 4.0,
10: 2.0,
11: 3.0,
12: 2.0,
13: 3.0,
14: 4.0,
15: nan,
16: nan,
17: 4.0,
18: 5.0,
19: 1.0},
'1210: Star Wars: Episode VI - Return of the Jedi (1983)': {0: 5.0,
1: 3.0,
2: nan,
3: 3.0,
4: 3.0,
5: 4.0,
6: nan,
7: nan,
8: 1.0,
9: 2.0,
10: 1.0,
11: 5.0,
12: 3.0,
13: nan,
14: 4.0,
15: nan,
16: nan,
17: 5.0,
18: 1.0,
19: 2.0},
'356: Forrest Gump (1994)': {0: 2.0,
1: nan,
2: nan,
3: nan,
4: 4.0,
5: 4.0,
6: 3.0,
7: nan,
8: nan,
9: nan,
10: 5.0,
11: 2.0,
12: nan,
13: 3.0,
14: nan,
15: 1.0,
16: nan,
17: 1.0,
18: nan,
19: 2.0},
'318: Shawshank Redemption, The (1994)': {0: nan,
1: 2.0,
2: 5.0,
3: nan,
4: 1.0,
5: 4.0,
6: 1.0,
7: nan,
8: 4.0,
9: 5.0,
10: nan,
11: nan,
12: 5.0,
13: nan,
14: nan,
15: nan,
16: nan,
17: 5.0,
18: nan,
19: 4.0},
'593: Silence of the Lambs, The (1991)': {0: 4.0,
1: 4.0,
2: 2.0,
3: nan,
4: 4.0,
5: nan,
6: 1.0,
7: 3.0,
8: 2.0,
9: 3.0,
10: nan,
11: 2.0,
12: 4.0,
13: 2.0,
14: 5.0,
15: 3.0,
16: 4.0,
17: 1.0,
18: nan,
19: 5.0},
'3578: Gladiator (2000)': {0: 4.0,
1: 2.0,
2: nan,
3: 1.0,
4: 1.0,
5: nan,
6: 4.0,
7: 2.0,
8: 4.0,
9: nan,
10: 5.0,
11: nan,
12: nan,
13: nan,
14: 5.0,
15: 2.0,
16: nan,
17: 1.0,
18: 4.0,
19: nan},
'1: Toy Story (1995)': {0: 2.0,
1: 1.0,
2: 4.0,
3: 2.0,
4: nan,
5: 3.0,
6: nan,
7: 2.0,
8: 4.0,
9: 4.0,
10: 5.0,
11: 2.0,
12: 4.0,
13: 3.0,
14: 2.0,
15: nan,
16: 2.0,
17: 4.0,
18: 2.0,
19: 2.0},
'2028: Saving Private Ryan (1998)': {0: 2.0,
1: nan,
2: nan,
3: 3.0,
4: 4.0,
5: 1.0,
6: 5.0,
7: nan,
8: 4.0,
9: 3.0,
10: nan,
11: nan,
12: 5.0,
13: nan,
14: nan,
15: 2.0,
16: nan,
17: nan,
18: 1.0,
19: 3.0},
'296: Pulp Fiction (1994)': {0: nan,
1: nan,
2: nan,
3: 4.0,
4: nan,
5: 4.0,
6: 2.0,
7: 3.0,
8: nan,
9: 4.0,
10: nan,
11: 1.0,
12: nan,
13: nan,
14: 3.0,
15: nan,
16: 2.0,
17: 5.0,
18: 3.0,
19: 2.0},
'1259: Stand by Me (1986)': {0: 3.0,
1: 4.0,
2: 1.0,
3: nan,
4: 1.0,
5: 4.0,
6: nan,
7: nan,
8: 1.0,
9: nan,
10: nan,
11: nan,
12: nan,
13: 4.0,
14: 5.0,
15: 1.0,
16: nan,
17: nan,
18: 3.0,
19: 2.0},
'2396: Shakespeare in Love (1998)': {0: 2.0,
1: 3.0,
2: nan,
3: nan,
4: 5.0,
5: 5.0,
6: 1.0,
7: nan,
8: 2.0,
9: nan,
10: nan,
11: 3.0,
12: nan,
13: nan,
14: nan,
15: 5.0,
16: 2.0,
17: nan,
18: 3.0,
19: 1.0},
'2916: Total Recall (1990)': {0: nan,
1: 2.0,
2: 1.0,
3: 4.0,
4: 1.0,
5: 2.0,
6: nan,
7: 2.0,
8: 3.0,
9: nan,
10: 3.0,
11: nan,
12: 2.0,
13: 1.0,
14: 1.0,
15: nan,
16: nan,
17: nan,
18: 1.0,
19: nan},
'780: Independence Day (ID4) (1996)': {0: 5.0,
1: 2.0,
2: 4.0,
3: 1.0,
4: nan,
5: 4.0,
6: nan,
7: 3.0,
8: 1.0,
9: 2.0,
10: 2.0,
11: 3.0,
12: 4.0,
13: 2.0,
14: 3.0,
15: nan,
16: nan,
17: nan,
18: nan,
19: nan},
'541: Blade Runner (1982)': {0: 2.0,
1: nan,
2: 4.0,
3: 3.0,
4: 4.0,
5: nan,
6: 3.0,
7: 2.0,
8: nan,
9: nan,
10: nan,
11: nan,
12: nan,
13: 2.0,
14: nan,
15: nan,
16: nan,
17: 4.0,
18: nan,
19: 5.0},
'1265: Groundhog Day (1993)': {0: nan,
1: 2.0,
2: 1.0,
3: 5.0,
4: nan,
5: 1.0,
6: nan,
7: 4.0,
8: 5.0,
9: nan,
10: nan,
11: 2.0,
12: 3.0,
13: 3.0,
14: 2.0,
15: 5.0,
16: nan,
17: nan,
18: nan,
19: 5.0},
'2571: Matrix, The (1999)': {0: 4.0,
1: nan,
2: 1.0,
3: nan,
4: 3.0,
5: nan,
6: 1.0,
7: nan,
8: nan,
9: 2.0,
10: 1.0,
11: 5.0,
12: nan,
13: 5.0,
14: nan,
15: 2.0,
16: 4.0,
17: nan,
18: 2.0,
19: 4.0},
"527: Schindler's List (1993)": {0: 2.0,
1: 5.0,
2: 2.0,
3: 5.0,
4: 5.0,
5: nan,
6: nan,
7: 1.0,
8: nan,
9: 5.0,
10: nan,
11: nan,
12: nan,
13: 1.0,
14: 3.0,
15: 2.0,
16: nan,
17: 2.0,
18: nan,
19: 3.0},
'2762: Sixth Sense, The (1999)': {0: 5.0,
1: 1.0,
2: 3.0,
3: 1.0,
4: 5.0,
5: 3.0,
6: nan,
7: 3.0,
8: nan,
9: 1.0,
10: 2.0,
11: nan,
12: nan,
13: nan,
14: nan,
15: 4.0,
16: nan,
17: 1.0,
18: nan,
19: 5.0},
'1198: Raiders of the Lost Ark (1981)': {0: nan,
1: 3.0,
2: 1.0,
3: 1.0,
4: nan,
5: nan,
6: 5.0,
7: 5.0,
8: nan,
9: nan,
10: 1.0,
11: nan,
12: 5.0,
13: nan,
14: 3.0,
15: 3.0,
16: nan,
17: 2.0,
18: nan,
19: 3.0},
'34: Babe (1995)': {0: nan,
1: nan,
2: 3.0,
3: 2.0,
4: nan,
5: 2.0,
6: 2.0,
7: nan,
8: 5.0,
9: nan,
10: 4.0,
11: 2.0,
12: nan,
13: nan,
14: 1.0,
15: 4.0,
16: nan,
17: 5.0,
18: nan,
19: nan}}
我想计算电影1（玩具总动员）中最常出现的电影。换句话说，对于每部电影，我想计算对这部电影进行评级的“玩具总动员”评分者的百分比。如果有关系，我想将编号最低的电影用作排名较高的电影。换句话说，如果电影541和318并列，则318会获得更高的排名。
我已经尝试过对玩具总动员没有零评级的数据框的一个子集进行此操作data_subset = data[data['1: Toy Story (1995)'].notnull()]，然后尝试通过((data_subset.count() + data_subset['1: Toy Story (1995)'].count()) / data_subset['1: Toy Story (1995)'].count()).sort_values(ascending=False)来获取百分比。排名似乎正确，但百分比值似乎不正确。
我不确定我是否完全理解你的问题。
我有以下内容：
对于每部电影，请找到观看次数最多的电影。如果是平局，请遵循您的逻辑。
我假设您的数据框名称为df，而nan值实际上是'np.nan'
import pandas as pd
import numpy as np
import re # for regular expressions
df2 = ( (df.drop(columns=['User', 'Gender (1 =F, 0=M)']) > 0.0) * 1 ) .astype(int) # create another dataframe with values 1 and 0 (0 when nan was) only for the movie columns
dfs = [] # empty list
for movie in df2.columns: # iterate over all columns (i.e. movies)
tmp = df2.groupby(movie).mean().T[[1]].reset_index() # group by each movie and get the average (percentage) of people that have seen it.
# these steps just clean the retuned dataframe.
tmp['Movie_num'] = [int(re.sub('(^\d+).*', '\\1', el)) for el in tmp['index']]
tmp = tmp.sort_values([1, 'Movie_num'], ascending=[False, True]).head(1)[['index', 1]]
del tmp.columns.name
tmp.index = [movie]
tmp.index.name = 'Movie'
tmp.columns = ['Frequent_Movie', 'Frequency']
dfs.append(tmp)
df_final = pd.concat(dfs).reset_index()