对列进行分组计算，排除某些条件

Question

I have this df:

data = {'A':[102, 102, 102, 102, 312, 312, 312], 
        'B':[1001,1001,1001,1001,1001,1001,1001],
        'C':[3005,3005,3005,3005,3005,3005,3005],
        'D':[2004,2004,2004,2004,2002,2002,2002],
        'E':[1,3,5,999,1,5,999],
        'F':[300,1,192,837,19,1,1037]} 

df = pd.DataFrame (data, columns = ['A','B','C','D','E','F'])

df.head(7)

一行代码计算百分比，除了我想让它排除E列中行值为(999)的计数值之外，它还可以工作。

df['Percentage'] = 100 * df['F'] / df.groupby('A')['F'].transform('sum')

百分比应该显示。

Percentage
60.85193
0.20284
38.94523
(Blank)
95
5
(Blank)

如果有任何帮助，我将非常感激

Answer 1

您可以将您的框架和 transform 然后将结果重新分配回来。

# Get the sub group
>>> grp = df[df['E'].ne(999)]

# Not required: this shows the Intermediate state of the transformed percentage
>>> grp['F'].mul(100).div(grp.groupby('A')['F'].transform('sum'))
0    60.851927
1     0.202840
2    38.945233
4    95.000000
5     5.000000
Name: F, dtype: float64

# Apply the result to your main frame
>>> df['Percentage'] = grp['F'].mul(100).div(grp.groupby('A')['F'].transform('sum'))

结果。

>>> df
     A     B     C     D    E     F  Percentage
0  102  1001  3005  2004    1   300   60.851927
1  102  1001  3005  2004    3     1    0.202840
2  102  1001  3005  2004    5   192   38.945233
3  102  1001  3005  2004  999   837         NaN
4  312  1001  3005  2002    1    19   95.000000
5  312  1001  3005  2002    5     1    5.000000
6  312  1001  3005  2002  999  1037         NaN

Answer 2

使用掩码来忽略你想要的行。

import pandas as pd

data = {'A': [102, 102, 102, 102, 312, 312, 312],
        'B': [1001, 1001, 1001, 1001, 1001, 1001, 1001],
        'C': [3005, 3005, 3005, 3005, 3005, 3005, 3005],
        'D': [2004, 2004, 2004, 2004, 2002, 2002, 2002],
        'E': [1, 3, 5, 999, 1, 5, 999],
        'F': [300, 1, 192, 837, 19, 1, 1037]}

df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D', 'E', 'F'])

mask = ~(df['E'] == 999)
df['Percentage'] = 100 * df[mask]['F'] / df[mask].groupby('A')['F'].transform('sum')

输出：使用掩码忽略你想要的行。

     A     B     C     D    E     F  Percentage
0  102  1001  3005  2004    1   300   60.851927
1  102  1001  3005  2004    3     1    0.202840
2  102  1001  3005  2004    5   192   38.945233
3  102  1001  3005  2004  999   837         NaN
4  312  1001  3005  2002    1    19   95.000000
5  312  1001  3005  2002    5     1    5.000000
6  312  1001  3005  2002  999  1037         NaN