Python Pandas 将列添加到多索引 GroupBy DataFrame

Question

我正在尝试使用多索引向 Pandas GroupBy DataFrame 添加一列。该列是分组后公共(public)键的最大值与平均值之差。

这是输入数据框:

   Main  Reads  Test  Subgroup
0     1      5    54         1
1     2      2    55         1
2     1     10    56         2
3     2     20    57         3
4     1      7    58         3

代码如下:

import numpy as np
import pandas as pd

df = pd.DataFrame({'Main': [1, 2, 1, 2, 1], 'Reads': [5, 2, 10, 20, 7],\
                   'Test':range(54,59), 'Subgroup':[1,1,2,3,3]})

result = df.groupby(['Main','Subgroup']).agg({'Reads':[np.max,np.mean]})

这是执行diff计算之前的变量result:

              Reads     
               amax mean
Main Subgroup           
1    1            5    5
     2           10   10
     3            7    7
2    1            2    2
     3           20   20

接下来，我计算 diff 列:

result['Reads']['diff'] = result['Reads']['amax'] - result['Reads']['mean']

但这里是输出:

/home/userd/test.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/
...stable/indexing.html#indexing-view-versus-copy
...result['Reads']['diff'] = result['Reads']['amax'] - result['Reads']['mean']

我希望 diff 列与 amax 和 mean 处于同一级别。

有没有办法将列添加到 Pandas 中多索引 GroupBy() 对象的最内层(底部)列索引？

Answer 1

您可以使用元组访问多索引

result[('Reads','diff')] = result[('Reads','amax')] - result[('Reads','mean')]

你得到

                    Reads
                    amax    mean    diff
Main    Subgroup            
1       1           5       5       0
        2          10      10       0
        3           7       7       0
2       1           2       2       0
        3          20      20       0