我有这张表，主要问题是ID ,W_Weight与Class列的长度不一致
注意：例如，
ID的每个数字都与Class相关联 （ID 0有Class 1.0而ID 4有Class 5.0）
ID W_Weight Class
0 0 0.255265 1.0
1 0 0.273844 1.0
2 0 0.351219 1.0
3 0 0.262033 1.0
4 0 0.351219 5.0
5 0 0.258109 1.0
6 0 0.296328 5.0
7 0 0.351219 1.0
8 0 0.301208 1.0
9 0 0.273844 1.0
10 0 0.317767 1.0
11 1 0.299451 1.0
12 1 0.327183 5.0
13 1 0.391577 1.0
14 1 0.272526 1.0
15 1 0.412015 1.0
16 1 0.412015 1.0
17 1 0.287148 1.0
18 1 0.168667 5.0
19 1 0.257689 1.0
20 1 0.242609 1.0
21 2 0.190351 5.0
22 2 0.204205 5.0
23 2 0.254588 5.0
24 2 0.261904 1.0
25 2 0.195398 5.0
26 2 0.248913 5.0
27 2 0.161089 1.0
28 2 0.240355 5.0
29 2 0.261904 1.0
... ... ... ...
410722 32742 0.190023 NaN
410723 32742 0.190023 NaN
410724 32742 0.184970 NaN
410725 32742 0.166998 NaN
410726 32742 0.196789 NaN
410727 32742 0.171033 NaN
410728 32742 0.207060 NaN
410729 32742 0.171033 NaN
410730 32742 0.179186 NaN
410731 32742 0.207060 NaN
410732 32742 0.182852 NaN
410733 32742 0.146492 NaN
410734 32742 0.141293 NaN
410735 32742 0.193123 NaN
410736 32742 0.207060 NaN
410737 32742 0.092576 NaN
410738 32742 0.207060 NaN
410739 32742 0.160762 NaN
410740 32742 0.165249 NaN
410741 32742 0.207060 NaN
410742 32742 0.184970 NaN
410743 32742 0.147506 NaN
410744 32742 0.207060 NaN
410745 32742 0.190023 NaN
410746 32742 0.116286 NaN
410747 32742 0.070032 NaN
410748 32742 0.207060 NaN
410749 32742 0.166998 NaN
410750 32742 0.147506 NaN
410751 32742 0.207060 NaN
所需的表应如下所示
注意：索引为0的第一行只是一个例子，我想这样做 这适用于
中的所有数据W_Weight列
ID W_Weight Class
0 0 {0.25,0.27,0.35,0.26,0.35,0.25,0.29,0.35,0.30,0.27,0.31} 1.0
11 1 0.299451 1.0
12 1 0.327183 5.0
13 1 0.391577 1.0
14 1 0.272526 1.0
15 1 0.412015 1.0
16 1 0.412015 1.0
17 1 0.287148 1.0
18 1 0.168667 5.0
19 1 0.257689 1.0
20 1 0.242609 1.0
21 2 0.190351 5.0
22 2 0.204205 5.0
23 2 0.254588 5.0
24 2 0.261904 1.0
25 2 0.195398 5.0
26 2 0.248913 5.0
27 2 0.161089 1.0
28 2 0.240355 5.0
29 2 0.261904 1.0
我这样做是为了将Class与ID and W_Weight相匹配，因为我正在使用TensorFlow进行分类
答案 0 :(得分：0)
你被正确地建议不要做你想做的事。尽管如此，如果你坚持，这是一个解决方案：
df.groupby('ID')['W_Weight'].apply(set)
#ID
#0 {0.255265, 0.351219, 0.25810900000000003, 0.26...
#1 {0.299451, 0.327183, 0.27252600000000005, 0.39...