python之pandas groupby 行条件

我有一个示例数据集:

import pandas as pd 
 
 
d = { 
 
 'H#': ['12843','12843','12843','12843','20000','20000','20000','20000','20000'], 
 'measure':[1,1,1,3,3,3,3,2,2], 
 'D':[1,0,2,1,1,1,2,1,1], 
 'N':[2,3,1,4,5,0,0,0,2] 
} 
df = pd.DataFrame(d) 
df = df.reindex_axis(['H#','measure', 'D','N'], axis=1)  

看起来像:

    H#      measure  D  N 
0  12843        1    1  2 
1  12843        1    0  3 
2  12843        1    2  1 
3  12843        3    1  4 
4  20000        3    1  5 
5  20000        3    1  0 
6  20000        3    2  0 
7  20000        2    1  0 
8  20000        2    1  2 

我想将 groupby 应用到 不是 measure=3 的行，通过“H#”和“measure”来总结“D”和“N”。 期望的输出:

    H#      measure  D  N 
0  12843        1    3  6 
3  12843        3    1  4 
4  20000        3    1  5 
5  20000        3    1  0 
6  20000        3    2  0 
7  20000        2    2  2 

我的尝试:

mask=df["measure"]!=3  #first to mask the rows for the groupby 
 
#the following line has the wrong syntax, how can i apply groupby to the masked dataset? 
df.loc[mask,]= df.loc[mask,].groupby(['H#','measure'],as_index=False)['D','N'].sum()   

最后一行代码的语法错误，如何将groupby应用于屏蔽数据集？

请您参考如下方法:

IIUC:

In [90]: (df[df.measure!=3] 
            .groupby(['H#','measure'], as_index=False) 
            .sum() 
            .append(df.loc[df.measure==3])) 
Out[90]: 
      H#  measure  D  N 
0  12843        1  3  6 
1  20000        2  2  2 
3  12843        3  1  4 
4  20000        3  1  5 
5  20000        3  1  0 
6  20000        3  2  0