python dataframe：创建一个列，根据日和月进行动态计算百分比

我正在与以下几点作斗争.

这是我的数据框.最后两列是我能够在查询中创建的计算.

    Day Month   Result  Vol     FirstCol    Second Col
0    26    5      Good   123    1%          0%
1    26    5      Bad    716    0%          2%
2    26    5      Other   36    0%          0%
3    26    6      Good  4721    26%         11%
4    26    6      Bad   7148    0%          16%
5    26    6      Other 1387    0%          3%
6    27    5      Good   196    1%          0%
7    27    5      Bad    627    0%          1%
8    27    5      Other   60    0%          0%
9    27    6      Good  6188    34%         14%
10   27    6      Bad   6688    0%          15%
11   27    6      Other 1068    0%          2%
12   28    5      Good   339    2%          1%
13   28    5      Bad   1114    0%          3%
14   28    5      Other   72    0%          0%
15   28    6      Good  6524    36%         15%
16   28    6      Bad   6103    0%          14%
17   28    6      Other  820    0%          2%

计算全部Good Result%的百分比的第一列

df['FirstCol'] = np.where(df['Result'].isin(['Good']),df['Vol']/df[df['Result']=='Good']['Vol'].sum(),0) 

第二列，用于计算全部Result

df['SecondCol'] = df['Vol']/df['Vol'].sum()

对于另一种，代码必须更加动态，这是我正在努力解决的问题. 第三列应该得到基于每个月的百分比.因此，第0-8行的百分比应该是100%，第9-17行的百分比也应该是100%. 第四列应该得到基于每个日期和月份的百分比.因此，第0-2行的百分比应为100%，第3-5行的百分比也应相同，依此类推.我想要一个动态查询.因为我不想每个月都换.

Desired Output

    Day Month   Result  Vol FirstCol    Second Col 0-17 Third Col 1-9 (Month)   Forth Col 1-3 (Day)
0    26 5   Good    123     1%          0%                  1%                  14%
1    26 5   Bad     716     0%          2%                  5%                  82%
2    26 5   Other    36     0%          0%                  0%                  4%
3    26 6   Good    4721    26%         11%                 31%                 36%
4    26 6   Bad     7148    0%          16%                 48%                 54%
5    26 6   Other   1387    0%          3%                  9%                  10%
6    27 5   Good     196    1%          0%                  1%                  22%
7    27 5   Bad      627    0%          1%                  4%                  71%
8    27 5   Other     60    0%          0%                  0%                  7%
9    27 6   Good    6188    34%         14%                 21%                 44%
10   27 6   Bad     6688    0%          15%                 23%                 48%
11   27 6   Other   1068    0%          2%                  4%                  8%
12   28 5   Good     339    2%          1%                  1%                  22%
13   28 5   Bad     1114    0%          3%                  4%                  73%
14   28 5   Other     72    0%          0%                  0%                  5%
15   28 6   Good    6524    36%         15%                 23%                 49%
16   28 6   Bad     6103    0%          14%                 21%                 45%
17   28 6   Other    820    0%          2%                  3%                  6%