大熊猫多指标选择

Question

我很难理解熊猫的多指标选择。

                    0  1  2  3
first second third            
C     one    mean   3  4  2  7
             std    4  1  7  7
      two    mean   3  1  4  7
             std    5  6  7  0
      three  mean   7  0  2  5
             std    7  3  7  1
H     one    mean   2  4  3  3
             std    5  5  3  5
      two    mean   5  7  0  6
             std    0  1  0  2
      three  mean   5  2  5  1
             std    9  0  4  6
V     one    mean   3  7  3  9
             std    8  7  9  3
      two    mean   1  9  9  0
             std    1  1  5  1
      three  mean   3  1  0  6
             std    6  2  7  4

我需要创建新行：

- 'CH' : ['CH',:,'mean'] => ['C',:,'mean'] - ['H',:,'mean']
- 'CH' : ['CH',:,'std'] => (['C',:,'std']**2 + ['H',:,'std']**2)**.5

在尝试选择行时，我会得到不同类型的错误: UnsortedIndexError：'MultiIndex切片要求索引完全按照词汇排序的连(3)、词汇排序深度(1)排序。

这个手术应该怎么做？

import pandas as pd
import numpy as np
iterables = [['C', 'H', 'V'],
          ['one','two','three'],
          ['mean','std']]
midx = pd.MultiIndex.from_product(iterables, names=['first', 'second','third'])
chv = pd.DataFrame(np.random.randint(0,high=10,size=(18,4)), index=midx)
print (chv)
idx = pd.IndexSlice
chv.loc[:,idx['C',:,'mean']]

Answer 1

您可以先通过切片机进行过滤，然后通过rename第一级进行过滤，然后使用算术操作，最后一起使用concat：

#avoid UnsortedIndexError
df = df.sort_index()

idx = pd.IndexSlice
c1 = chv.loc[idx['C',:,'mean'], :].rename({'C':'CH'}, level=0)
h1 = chv.loc[idx['H',:,'mean'], :].rename({'H':'CH'}, level=0)
ch1 = c1 - h1

c2 = chv.loc[idx['C',:,'std'], :].rename({'C':'CH'}, level=0)**2
h2 = chv.loc[idx['H',:,'std'], :].rename({'H':'CH'}, level=0)**2
ch2 = (c2 + h2)**.5

df = pd.concat([chv, ch1, ch2]).sort_index()

print (df)
                           0         1         2         3
first second third                                        
C     one    mean   7.000000  5.000000  8.000000  3.000000
             std    0.000000  4.000000  4.000000  4.000000
      three  mean   4.000000  2.000000  1.000000  6.000000
             std    8.000000  7.000000  3.000000  3.000000
      two    mean   1.000000  8.000000  2.000000  5.000000
             std    2.000000  2.000000  4.000000  2.000000
CH    one    mean   1.000000  2.000000  1.000000  2.000000
             std    4.000000  7.211103  4.000000  7.211103
      three  mean   1.000000  0.000000 -4.000000  2.000000
             std    8.062258  7.071068  4.242641  3.000000
      two    mean  -1.000000  6.000000 -2.000000  3.000000
             std    9.219544  7.280110  4.123106  2.000000
H     one    mean   6.000000  3.000000  7.000000  1.000000
             std    4.000000  6.000000  0.000000  6.000000
      three  mean   3.000000  2.000000  5.000000  4.000000
             std    1.000000  1.000000  3.000000  0.000000
      two    mean   2.000000  2.000000  4.000000  2.000000
             std    9.000000  7.000000  1.000000  0.000000
V     one    mean   9.000000  5.000000  0.000000  5.000000
             std    7.000000  9.000000  1.000000  1.000000
      three  mean   3.000000  0.000000  3.000000  4.000000
             std    1.000000  4.000000  9.000000  2.000000
      two    mean   3.000000  6.000000  3.000000  2.000000
             std    1.000000  3.000000  1.000000  4.000000