使用 groupby 并聚合 max:

print (df)
      PERIOD_START_TIME  ID  A  VALUE
0   06.01.2017 02:00:00  55  8     35
1   06.01.2017 02:00:00  55  8     22
2   06.01.2017 03:00:00  55  8     63
3   06.01.2017 03:00:00  55  8     33
4   06.01.2017 04:00:00  55  8     63
5   06.01.2017 04:00:00  55  8     45
6   06.01.2017 02:00:00  65  8     10
7   06.01.2017 02:00:00  65  8      5
8   06.01.2017 03:00:00  65  8     22
9   06.01.2017 03:00:00  65  8      5
10  06.01.2017 04:00:00  65  8     12
11  06.01.2017 04:00:00  65  8     15

df = df.groupby(['PERIOD_START_TIME','ID'], as_index=False)['VALUE'].max()            

或者:

df = df.groupby(['PERIOD_START_TIME','ID'])['VALUE'].max().reset_index()

print (df)
     PERIOD_START_TIME  ID  VALUE
0  06.01.2017 02:00:00  55     35
1  06.01.2017 02:00:00  65     10
2  06.01.2017 03:00:00  55     63
3  06.01.2017 03:00:00  65     22
4  06.01.2017 04:00:00  55     63
5  06.01.2017 04:00:00  65     15

更多栏目需要idxmax 并通过 选择loc:

For more columns need idxmax and select by loc:

df = df.loc[df.groupby(['PERIOD_START_TIME','ID'])['VALUE'].idxmax()]  
print (df)
      PERIOD_START_TIME  ID  A  VALUE
0   06.01.2017 02:00:00  55  8     35
6   06.01.2017 02:00:00  65  8     10
2   06.01.2017 03:00:00  55  8     63
8   06.01.2017 03:00:00  65  8     22
4   06.01.2017 04:00:00  55  8     63
11  06.01.2017 04:00:00  65  8     15 

替代方案:

cols = ['PERIOD_START_TIME','ID']
df = df.sort_values(cols).groupby(cols, as_index=False).first()
print (df)
     PERIOD_START_TIME  ID  A  VALUE
0  06.01.2017 02:00:00  55  8     35
1  06.01.2017 02:00:00  65  8     10
2  06.01.2017 03:00:00  55  8     63
3  06.01.2017 03:00:00  65  8     22
4  06.01.2017 04:00:00  55  8     63
5  06.01.2017 04:00:00  65  8     12