这是我的数据框：是的，它非常大。
bigdataframe
Out[2]:
movie id movietitle releasedate \
0 1 Toy Story (1995) 01-Jan-1995
1 4 Get Shorty (1995) 01-Jan-1995
2 5 Copycat (1995) 01-Jan-1995
3 7 Twelve Monkeys (1995) 01-Jan-1995
4 8 Babe (1995) 01-Jan-1995
5 9 Dead Man Walking (1995) 01-Jan-1995
6 11 Seven (Se7en) (1995) 01-Jan-1995
7 12 Usual Suspects, The (1995) 14-Aug-1995
8 15 Mr. Holland's Opus (1995) 29-Jan-1996
9 17 From Dusk Till Dawn (1996) 05-Feb-1996
10 19 Antonia's Line (1995) 01-Jan-1995
11 21 Muppet Treasure Island (1996) 16-Feb-1996
12 22 Braveheart (1995) 16-Feb-1996
13 23 Taxi Driver (1976) 16-Feb-1996
14 24 Rumble in the Bronx (1995) 23-Feb-1996
15 25 Birdcage, The (1996) 08-Mar-1996
16 28 Apollo 13 (1995) 01-Jan-1995
17 30 Belle de jour (1967) 01-Jan-1967
18 31 Crimson Tide (1995) 01-Jan-1995
19 32 Crumb (1994) 01-Jan-1994
20 42 Clerks (1994) 01-Jan-1994
21 44 Dolores Claiborne (1994) 01-Jan-1994
22 45 Eat Drink Man Woman (1994) 01-Jan-1994
23 47 Ed Wood (1994) 01-Jan-1994
24 48 Hoop Dreams (1994) 01-Jan-1994
25 49 I.Q. (1994) 01-Jan-1994
26 50 Star Wars (1977) 01-Jan-1977
27 54 Outbreak (1995) 01-Jan-1995
28 55 Professional, The (1994) 01-Jan-1994
29 56 Pulp Fiction (1994) 01-Jan-1994
... ... ...
99970 332 Kiss the Girls (1997) 01-Jan-1997
99971 334 U Turn (1997) 01-Jan-1997
99972 338 Bean (1997) 01-Jan-1997
99973 346 Jackie Brown (1997) 01-Jan-1997
99974 682 I Know What You Did Last Summer (1997) 17-Oct-1997
99975 873 Picture Perfect (1997) 01-Aug-1997
99976 877 Excess Baggage (1997) 01-Jan-1997
99977 886 Life Less Ordinary, A (1997) 01-Jan-1997
99978 1527 Senseless (1998) 09-Jan-1998
99979 272 Good Will Hunting (1997) 01-Jan-1997
99980 288 Scream (1996) 20-Dec-1996
99981 294 Liar Liar (1997) 21-Mar-1997
99982 300 Air Force One (1997) 01-Jan-1997
99983 310 Rainmaker, The (1997) 01-Jan-1997
99984 313 Titanic (1997) 01-Jan-1997
99985 322 Murder at 1600 (1997) 18-Apr-1997
99986 328 Conspiracy Theory (1997) 08-Aug-1997
99987 333 Game, The (1997) 01-Jan-1997
99988 338 Bean (1997) 01-Jan-1997
99989 346 Jackie Brown (1997) 01-Jan-1997
99990 354 Wedding Singer, The (1998) 13-Feb-1998
99991 362 Blues Brothers 2000 (1998) 06-Feb-1998
99992 683 Rocket Man (1997) 01-Jan-1997
99993 689 Jackal, The (1997) 01-Jan-1997
99994 690 Seven Years in Tibet (1997) 01-Jan-1997
99995 748 Saint, The (1997) 14-Mar-1997
99996 751 Tomorrow Never Dies (1997) 01-Jan-1997
99997 879 Peacemaker, The (1997) 01-Jan-1997
99998 894 Home Alone 3 (1997) 01-Jan-1997
99999 901 Mr. Magoo (1997) 25-Dec-1997
videoreleasedate IMDb URL \
0 NaN http://us.imdb.com/M/title-exact?Toy%20Story%2...
1 NaN http://us.imdb.com/M/title-exact?Get%20Shorty%...
2 NaN http://us.imdb.com/M/title-exact?Copycat%20(1995)
3 NaN http://us.imdb.com/M/title-exact?Twelve%20Monk...
4 NaN http://us.imdb.com/M/title-exact?Babe%20(1995)
5 NaN http://us.imdb.com/M/title-exact?Dead%20Man%20...
6 NaN http://us.imdb.com/M/title-exact?Se7en%20(1995)
7 NaN http://us.imdb.com/M/title-exact?Usual%20Suspe...
8 NaN http://us.imdb.com/M/title-exact?Mr.%20Holland...
9 NaN http://us.imdb.com/M/title-exact?From%20Dusk%2...
10 NaN http://us.imdb.com/M/title-exact?Antonia%20(1995)
11 NaN http://us.imdb.com/M/title-exact?Muppet%20Trea...
12 NaN http://us.imdb.com/M/title-exact?Braveheart%20...
13 NaN http://us.imdb.com/M/title-exact?Taxi%20Driver...
14 NaN http://us.imdb.com/M/title-exact?Hong%20Faan%2...
15 NaN http://us.imdb.com/M/title-exact?Birdcage,%20T...
16 NaN http://us.imdb.com/M/title-exact?Apollo%2013%2...
17 NaN http://us.imdb.com/M/title-exact?Belle%20de%20...
18 NaN http://us.imdb.com/M/title-exact?Crimson%20Tid...
19 NaN http://us.imdb.com/M/title-exact?Crumb%20(1994)
20 NaN http://us.imdb.com/M/title-exact?Clerks%20(1994)
21 NaN http://us.imdb.com/M/title-exact?Dolores%20Cla...
22 NaN http://us.imdb.com/M/title-exact?Yinshi%20Nan%...
23 NaN http://us.imdb.com/M/title-exact?Ed%20Wood%20(...
24 NaN http://us.imdb.com/M/title-exact?Hoop%20Dreams...
25 NaN http://us.imdb.com/M/title-exact?I.Q.%20(1994)
26 NaN http://us.imdb.com/M/title-exact?Star%20Wars%2...
27 NaN http://us.imdb.com/M/title-exact?Outbreak%20(1...
28 NaN http://us.imdb.com/Title?L%E9on+(1994)
29 NaN http://us.imdb.com/M/title-exact?Pulp%20Fictio...
... ...
99970 NaN http://us.imdb.com/M/title-exact?Kiss+the+Girl...
99971 NaN http://us.imdb.com/Title?U+Turn+(1997)
99972 NaN http://us.imdb.com/M/title-exact?Bean+(1997)
99973 NaN http://us.imdb.com/M/title-exact?imdb-title-11...
99974 NaN http://us.imdb.com/M/title-exact?I+Know+What+Y...
99975 NaN http://us.imdb.com/M/title-exact?Picture+Perfe...
99976 NaN http://us.imdb.com/M/title-exact?Excess+Baggag...
99977 NaN http://us.imdb.com/M/title-exact?Life+Less+Ord...
99978 NaN http://us.imdb.com/M/title-exact?imdb-title-12...
99979 NaN http://us.imdb.com/M/title-exact?imdb-title-11...
99980 NaN http://us.imdb.com/M/title-exact?Scream%20(1996)
99981 NaN http://us.imdb.com/Title?Liar+Liar+(1997)
99982 NaN http://us.imdb.com/M/title-exact?Air+Force+One...
99983 NaN http://us.imdb.com/M/title-exact?Rainmaker,+Th...
99984 NaN http://us.imdb.com/M/title-exact?imdb-title-12...
99985 NaN http://us.imdb.com/M/title-exact?Murder%20at%2...
99986 NaN http://us.imdb.com/M/title-exact?Conspiracy+Th...
99987 NaN http://us.imdb.com/M/title-exact?Game%2C+The+(...
99988 NaN http://us.imdb.com/M/title-exact?Bean+(1997)
99989 NaN http://us.imdb.com/M/title-exact?imdb-title-11...
99990 NaN http://us.imdb.com/M/title-exact?Wedding+Singe...
99991 NaN http://us.imdb.com/M/title-exact?Blues+Brother...
99992 NaN http://us.imdb.com/M/title-exact?Rocket+Man+(1...
99993 NaN http://us.imdb.com/M/title-exact?Jackal%2C+The...
99994 NaN http://us.imdb.com/M/title-exact?Seven+Years+i...
99995 NaN http://us.imdb.com/M/title-exact?Saint%2C%20Th...
99996 NaN http://us.imdb.com/M/title-exact?imdb-title-12...
99997 NaN http://us.imdb.com/M/title-exact?Peacemaker%2C...
99998 NaN http://us.imdb.com/M/title-exact?imdb-title-11...
99999 NaN http://us.imdb.com/M/title-exact?imdb-title-11...
unknown Action Adventure Animation Childrens ... Western \
0 0 0 0 1 1 ... 0
1 0 1 0 0 0 ... 0
2 0 0 0 0 0 ... 0
3 0 0 0 0 0 ... 0
4 0 0 0 0 1 ... 0
5 0 0 0 0 0 ... 0
6 0 0 0 0 0 ... 0
7 0 0 0 0 0 ... 0
8 0 0 0 0 0 ... 0
9 0 1 0 0 0 ... 0
10 0 0 0 0 0 ... 0
11 0 1 1 0 0 ... 0
12 0 1 0 0 0 ... 0
13 0 0 0 0 0 ... 0
14 0 1 1 0 0 ... 0
15 0 0 0 0 0 ... 0
16 0 1 0 0 0 ... 0
17 0 0 0 0 0 ... 0
18 0 0 0 0 0 ... 0
19 0 0 0 0 0 ... 0
20 0 0 0 0 0 ... 0
21 0 0 0 0 0 ... 0
22 0 0 0 0 0 ... 0
23 0 0 0 0 0 ... 0
24 0 0 0 0 0 ... 0
25 0 0 0 0 0 ... 0
26 0 1 1 0 0 ... 0
27 0 1 0 0 0 ... 0
28 0 0 0 0 0 ... 0
29 0 0 0 0 0 ... 0
... ... ... ... ... ... ...
99970 0 0 0 0 0 ... 0
99971 0 1 0 0 0 ... 0
99972 0 0 0 0 0 ... 0
99973 0 0 0 0 0 ... 0
99974 0 0 0 0 0 ... 0
99975 0 0 0 0 0 ... 0
99976 0 0 1 0 0 ... 0
99977 0 0 0 0 0 ... 0
99978 0 0 0 0 0 ... 0
99979 0 0 0 0 0 ... 0
99980 0 0 0 0 0 ... 0
99981 0 0 0 0 0 ... 0
99982 0 1 0 0 0 ... 0
99983 0 0 0 0 0 ... 0
99984 0 1 0 0 0 ... 0
99985 0 0 0 0 0 ... 0
99986 0 1 0 0 0 ... 0
99987 0 0 0 0 0 ... 0
99988 0 0 0 0 0 ... 0
99989 0 0 0 0 0 ... 0
99990 0 0 0 0 0 ... 0
99991 0 1 0 0 0 ... 0
99992 0 0 0 0 0 ... 0
99993 0 1 0 0 0 ... 0
99994 0 0 0 0 0 ... 0
99995 0 1 0 0 0 ... 0
99996 0 1 0 0 0 ... 0
99997 0 1 0 0 0 ... 0
99998 0 0 0 0 1 ... 0
99999 0 0 0 0 0 ... 0
user id rating timestamp age gender occupation zipcode state \
0 308 4 887736532 60 M retired 95076 CA
1 308 5 887737890 60 M retired 95076 CA
2 308 4 887739608 60 M retired 95076 CA
3 308 4 887738847 60 M retired 95076 CA
4 308 5 887736696 60 M retired 95076 CA
5 308 4 887737194 60 M retired 95076 CA
6 308 5 887737837 60 M retired 95076 CA
7 308 5 887737243 60 M retired 95076 CA
8 308 3 887739426 60 M retired 95076 CA
9 308 4 887739056 60 M retired 95076 CA
10 308 3 887737383 60 M retired 95076 CA
11 308 3 887740729 60 M retired 95076 CA
12 308 4 887737647 60 M retired 95076 CA
13 308 5 887737293 60 M retired 95076 CA
14 308 4 887738057 60 M retired 95076 CA
15 308 4 887740649 60 M retired 95076 CA
16 308 3 887737036 60 M retired 95076 CA
17 308 4 887738933 60 M retired 95076 CA
18 308 3 887739472 60 M retired 95076 CA
19 308 5 887737432 60 M retired 95076 CA
20 308 4 887738191 60 M retired 95076 CA
21 308 4 887740451 60 M retired 95076 CA
22 308 4 887736843 60 M retired 95076 CA
23 308 4 887738933 60 M retired 95076 CA
24 308 4 887736880 60 M retired 95076 CA
25 308 3 887740833 60 M retired 95076 CA
26 308 5 887737431 60 M retired 95076 CA
27 308 2 887740254 60 M retired 95076 CA
28 308 3 887738760 60 M retired 95076 CA
29 308 5 887736924 60 M retired 95076 CA
... ... ... ... ... ... ... ...
99970 631 3 888465180 18 F student 38866 MS
99971 631 2 888464941 18 F student 38866 MS
99972 631 2 888465299 18 F student 38866 MS
99973 631 4 888465004 18 F student 38866 MS
99974 631 2 888465247 18 F student 38866 MS
99975 631 2 888465084 18 F student 38866 MS
99976 631 2 888465131 18 F student 38866 MS
99977 631 4 888465216 18 F student 38866 MS
99978 631 2 888465351 18 F student 38866 MS
99979 729 4 893286638 19 M student 56567 MN
99980 729 2 893286261 19 M student 56567 MN
99981 729 2 893286338 19 M student 56567 MN
99982 729 4 893286638 19 M student 56567 MN
99983 729 3 893286204 19 M student 56567 MN
99984 729 3 893286638 19 M student 56567 MN
99985 729 4 893286637 19 M student 56567 MN
99986 729 3 893286638 19 M student 56567 MN
99987 729 4 893286638 19 M student 56567 MN
99988 729 1 893286373 19 M student 56567 MN
99989 729 1 893286168 19 M student 56567 MN
99990 729 5 893286637 19 M student 56567 MN
99991 729 4 893286637 19 M student 56567 MN
99992 729 2 893286511 19 M student 56567 MN
99993 729 4 893286638 19 M student 56567 MN
99994 729 2 893286149 19 M student 56567 MN
99995 729 4 893286638 19 M student 56567 MN
99996 729 3 893286338 19 M student 56567 MN
99997 729 3 893286299 19 M student 56567 MN
99998 729 1 893286511 19 M student 56567 MN
99999 729 1 893286491 19 M student 56567 MN
State1
0 CA
1 CA
2 CA
3 CA
4 CA
5 CA
6 CA
7 CA
8 CA
9 CA
10 CA
11 CA
12 CA
13 CA
14 CA
15 CA
16 CA
17 CA
18 CA
19 CA
20 CA
21 CA
22 CA
23 CA
24 CA
25 CA
26 CA
27 CA
28 CA
29 CA
...
99970 MS
99971 MS
99972 MS
99973 MS
99974 MS
99975 MS
99976 MS
99977 MS
99978 MS
99979 MN
99980 MN
99981 MN
99982 MN
99983 MN
99984 MN
99985 MN
99986 MN
99987 MN
99988 MN
99989 MN
99990 MN
99991 MN
99992 MN
99993 MN
99994 MN
99995 MN
99996 MN
99997 MN
99998 MN
99999 MN
所有类型都是： [[＆＃39;行动＆＃39;，＆＃39;冒险＆＃39;，＆＃39;动画＆＃39;＆＃39;儿童＆ ＃39;，＆＃39;喜剧＆＃39;，＆＃39;犯罪＆＃39;，纪录片＆＃39;，＆＃39;戏剧＆＃39;幻想＆＃39;，＆ ＃39; FilmNoir＆＃39 ;, ＆＃39;恐怖＆＃39;音乐＆＃39;，＆＃39;神秘＆＃39;浪漫＆＃39;科幻＆＃39;，＆＃39;惊悚＆ ＃39;，＆＃39;战争＆＃39;，＆＃39;西方＆＃39;]]
我如何能够找出哪种类型的平均评价最高，哪些类型的平均评价最低？我应该将评分和所有相应的类型分组？
df = bigdataframe[['Action', 'Adventure','Animation', 'Childrens', 'Comedy',
'Crime','Documentary', 'Drama', 'Fantasy', 'FilmNoir',
'Horror', 'Musical', 'Mystery',
'Romance','SciFi', 'Thriller', 'War', 'Western','rating']]
gp = df.groupby('rating')
result = gp.agg(['mean'])
结果给了我这个：
Action Adventure Animation Childrens Comedy Crime \
mean mean mean mean mean mean
评级
1 0.253191 0.131588 0.030442 0.093944 0.372995 0.068249
2 0.286192 0.150308 0.032806 0.084521 0.339138 0.073351
3 0.267232 0.143710 0.037502 0.081709 0.322380 0.073899
4 0.246708 0.129806 0.036051 0.064728 0.284485 0.082958
5 0.240696 0.136928 0.037545 0.057403 0.246403 0.092590
Documentary Drama Fantasy FilmNoir Horror Musical \
mean mean mean mean mean mean
评级
1 0.009656 0.289034 0.018331 0.007365 0.082324 0.046645
2 0.005101 0.320756 0.019349 0.008531 0.071592 0.050484
3 0.006042 0.363861 0.016983 0.013520 0.055738 0.052238
4 0.007842 0.427459 0.011207 0.019430 0.047112 0.047609
5 0.009858 0.471534 0.008301 0.026414 0.041366 0.049526
Mystery Romance SciFi Thriller War Western
mean mean mean mean mean mean
评级
1 0.041735 0.154173 0.118494 0.203764 0.060065 0.011620
2 0.046262 0.177397 0.133597 0.229903 0.067018 0.015743
3 0.048112 0.186443 0.121422 0.224277 0.074415 0.019893
4 0.056563 0.201381 0.125154 0.222772 0.097589 0.019606
5 0.057780 0.215037 0.137446 0.203387 0.137446 0.018584
答案 0 :(得分：1)
我认为您需要idxmin和idxmax，也不需要新的DataFrame，您可以使用bigdataframe并过滤[]中的列：
genres = ['Action', 'Adventure','Animation', 'Childrens', 'Comedy', 'Crime','Documentary', 'Drama', 'Fantasy', 'FilmNoir', 'Horror', 'Musical', 'Mystery', 'Romance','SciFi', 'Thriller', 'War', 'Western']
df1 = bigdataframe.groupby('rating')[genres].mean()
print (df1)
Action Adventure Animation Childrens Comedy Crime \
rating
1 0.253191 0.131588 0.030442 0.093944 0.372995 0.068249
2 0.286192 0.150308 0.032806 0.084521 0.339138 0.073351
3 0.267232 0.143710 0.037502 0.081709 0.322380 0.073899
4 0.246708 0.129806 0.036051 0.064728 0.284485 0.082958
5 0.240696 0.136928 0.037545 0.057403 0.246403 0.092590
Documentary Drama Fantasy FilmNoir Horror Musical \
rating
1 0.009656 0.289034 0.018331 0.007365 0.082324 0.046645
2 0.005101 0.320756 0.019349 0.008531 0.071592 0.050484
3 0.006042 0.363861 0.016983 0.013520 0.055738 0.052238
4 0.007842 0.427459 0.011207 0.019430 0.047112 0.047609
5 0.009858 0.471534 0.008301 0.026414 0.041366 0.049526
Mystery Romance SciFi Thriller War Western
rating
1 0.041735 0.154173 0.118494 0.203764 0.060065 0.011620
2 0.046262 0.177397 0.133597 0.229903 0.067018 0.015743
3 0.048112 0.186443 0.121422 0.224277 0.074415 0.019893
4 0.056563 0.201381 0.125154 0.222772 0.097589 0.019606
5 0.057780 0.215037 0.137446 0.203387 0.137446 0.018584
mingen = df1.idxmin(axis=1).reset_index(name='Genre')
print (mingen)
rating Genre
0 1 FilmNoir
1 2 Documentary
2 3 Documentary
3 4 Documentary
4 5 Fantasy
maxgen = df1.idxmax(axis=1).reset_index(name='Genre')
print (maxgen)
rating Genre
0 1 Comedy
1 2 Comedy
2 3 Drama
3 4 Drama
4 5 Drama