我正在使用python pandas DataFrames，我想按类别对我的数据进行分组，我不想要其他功能的任何均值或中位数（PriceBucket，success_rate和products_by_number）。我的DataFrame看起来像这样：
PriceBucket success_rate products_by_number category
0 0 6.890 149837 10
1 1 7.240 105447 10
2 2 7.710 145295 10
3 3 8.090 181323 10
4 4 8.930 57187 10
5 5 8.110 133449 10
6 6 7.920 142858 10
7 7 8.230 115109 10
8 8 8.510 121930 10
9 9 8.340 122510 10
10 0 10.520 28105 20
11 1 9.770 27494 20
12 2 10.080 26758 20
13 3 10.180 29973 20
14 4 9.860 29175 20
15 5 9.950 23807 20
16 6 9.550 30520 20
17 7 9.550 23653 20
18 8 8.990 27514 20
19 9 6.710 26152 20
20 0 11.060 39538 60
21 1 10.740 34479 60
22 2 10.700 36133 60
23 3 10.900 34220 60
24 4 11.290 46001 60
25 5 11.130 26705 60
26 6 11.040 37258 60
27 7 11.150 34561 60
28 8 10.845 35495 60
29 9 10.220 35434 60
30 0 8.380 34134 90
31 1 7.920 32160 90
32 2 8.170 29500 90
33 3 8.270 31688 90
34 4 8.395 38977 90
35 5 8.620 27130 90
36 6 8.440 31007 90
37 7 8.570 31005 90
38 8 8.170 32659 90
39 9 7.290 30227 90
这正是我想要的：
PriceBucket success_rate products_by_number
category
10 0 6.890 149837
1 7.240 105447
2 7.710 145295
3 8.090 181323
4 8.930 57187
5 8.110 133449
6 7.920 142858
7 8.230 115109
8 8.510 121930
9 8.340 122510
20 0 10.520 28105
1 9.770 27494
2 10.080 26758
3 10.180 29973
4 9.860 29175
5 9.950 23807
6 9.550 30520
7 9.550 23653
8 8.990 27514
9 6.710 26152
60 0 11.060 39538
1 10.740 34479
2 10.700 36133
3 10.900 34220
4 11.290 46001
5 11.130 26705
6 11.040 37258
7 11.150 34561
8 10.845 35495
9 10.220 35434
90 0 8.380 34134
1 7.920 32160
2 8.170 29500
3 8.270 31688
4 8.395 38977
5 8.620 27130
6 8.440 31007
7 8.570 31005
8 8.170 32659
9 7.290 30227
怎么办？非常感谢
答案 0 :(得分：1)
假设您的数据帧为df，那么您需要：
print df.set_index(['category', 'PriceBucket'])
success_rate products_by_number
category PriceBucket
10 0 6.890 149837
1 7.240 105447
2 7.710 145295
3 8.090 181323
4 8.930 57187
5 8.110 133449
6 7.920 142858
7 8.230 115109
8 8.510 121930
9 8.340 122510
20 0 10.520 28105
1 9.770 27494
2 10.080 26758
3 10.180 29973
4 9.860 29175
5 9.950 23807
6 9.550 30520
7 9.550 23653
8 8.990 27514
9 6.710 26152
60 0 11.060 39538
1 10.740 34479
2 10.700 36133
3 10.900 34220
4 11.290 46001
5 11.130 26705
6 11.040 37258
7 11.150 34561
8 10.845 35495
9 10.220 35434
90 0 8.380 34134
1 7.920 32160
2 8.170 29500
3 8.270 31688
4 8.395 38977
5 8.620 27130
6 8.440 31007
7 8.570 31005
8 8.170 32659
9 7.290 30227