大Pandas的多指标选择

Question

我很难理解熊猫的多指标选择。在

                    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：

^{pr2}$

当尝试选择行时，我会遇到不同类型的错误： UnsortedIndexError:“多索引切片要求索引完全为lexsorted元组长度（3），lexsort深度（1）”

该如何执行此操作？在

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

您可以先按slicers进行筛选，然后按rename第一级进行筛选，并使用算术运算，最后一级使用^{}：

#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()

---

^{pr2}$