熊猫中的多索引选择

Question

我无法理解 pandas 中的多索引选择。

                    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

当尝试选择行时，我得到不同类型的错误： UnsortedIndexError: 'MultiIndex Slicing 要求索引是完全 lexsorted tuple len (3), lexsort depth (1)'

这个操作应该如何进行？

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

您可以先过滤slicers，然后是rename第一级并使用算术运算，最后是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