Question

我正在调试一些熊猫代码，这些代码意外地创建了MultiIndex而不是常规索引。由于索引多，Pandas不允许选择列。在这种情况下，我可以摆脱MultiIndex，但是如果确实需要该MultiIndex，那么如何选择列？附加信息-我在pandas 0.25.1中遇到此错误，但是此代码在几年前某人写的笔记本中，因此显然它曾经用于较旧版本？

import numpy as np
import pandas as pd

names = ['FirstColumn', 'SecondColumn']
data = np.array([[5,6],[7,8]])
df = pd.DataFrame(data, columns = [names]) #Bug: this "works" but isn't what you want.
#The brackets around "[names]" creates a multi-index but that was unintentional.
#But "df.head()" and "df.describe()" both look normal so you can't see anything is wrong. 

df['FirstColumn'] #ERROR! works fine with a single index, but fails with multiindex
df.FirstColumn #ERROR! works fine with a single index, but fails with multiindex
df.loc[:,'FirstColumn'] #ERROR! works fine with a single index, but fails with multiindex

这两个陈述均对only integer scalar arrays can be converted to a scalar index产生误导性错误那么，在有多索引的情况下如何选择列？我知道一些技巧，例如unstack或更改索引等；但似乎应该有一种简单的方法？

更新：原来这在pandas 0.22.0中工作正常，但在0.25.1中失败。看起来引入了回归错误。我已经在熊猫github上报道了它。

Answer 1

使用DataFrame.xs函数：

print (df.xs('FirstColumn', axis=1, level=0))
  FirstColumn
0           5
1           7

如果存在多索引，熊猫将不允许选择列？

1 个答案: