python – 在pandas中按多个条件分组

我有一个像这样的pandas数据结构：

>>> df
        Benny  Daniel   Doris   Eric   Jack    Zoe
Age        75      30      95     25     28     23
Salary   2000    9000  100000  10000  12000  20000 

我想找到几个不同组的平均年龄和工资,其中每个组都是列的子集,它们可能重叠,例如这个字典：

{'Parrot lovers': ['Doris', 'Benny'], 'Tea Drinkers': ['Doris', 'Zoe'],\
 'Maintainance': ['Benny', 'Jack'], 'Coffee Drinkers': ['Benny', 'Eric'],\
 'Senior Management': ['Doris', 'Zoe', 'Jack']}

如何设计一个可以执行此操作的groupby函数？

解决方法:

以下是我设置问题的方法……

import StringIO
import pandas as pd

df = """index  Benny  Daniel   Doris   Eric   Jack    Zoe
Age        75      30      95     25     28     23
Salary   2000    9000  100000  10000  12000  20000"""
df = pd.read_csv(StringIO.StringIO(df),sep="\s+").set_index('index')
d = {'Parrot lovers': ['Doris', 'Benny'], 'Tea Drinkers': ['Doris', 'Zoe'],\
 'Maintainance': ['Benny', 'Jack'], 'Coffee Drinkers': ['Benny', 'Eric'],\
 'Senior Management': ['Doris', 'Zoe', 'Jack']}

对于解决方案Just Use .loc并遍历字典…

averages = {k:df.loc[:,v].mean(axis=1) for k,v in d.iteritems()}
print pd.DataFrame(averages).T #gives the nice printout...

index                    Age  Salary
Coffee Drinkers    50.000000    6000
Maintainance       51.500000    7000
Parrot lovers      85.000000   51000
Senior Management  48.666667   44000
Tea Drinkers       59.000000   60000