複数列のデータフレーム内の平均値を調べるには？

Question

私のデータフレームです：はい、それはかなり大きいです。

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 

ジャンルの全ては、以下のとおりです。 [[ 'アクション'、 '冒険'、 'アニメーション'、 '子供'、 'コメディ'、 '犯罪'、 'ドキュメンタリー'、 'ドラマ'、 '映画 "、" FilmNoir "、 「ホラー」、「ミュージカル」、「ミステリー」、「ロマンス」、「スリラー」、「戦争」、「西洋」]]

どのジャンルが平均評価が最も高く、平均評価が最も低いのかを把握していますか？私は格付けとそれに対応するすべてのジャンルとのグループ化をするべきですか？

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']) 

結果は私にこの与える：

 Action Adventure Animation Childrens Comedy  Crime \ 
     mean  mean  mean  mean  mean  mean 

評価を
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 

評価
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 

評価
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

Answer 1

idxminとidxmaxが必要で、新しいDataFrameも不要で、bigdataframeとフィルタ列をで使用できると思います：

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