如何从MultiIndex列的DataFrame中仅选择特定列？

问题描述

我的DataFrame包含MultiIndex列，如下所示：

I have DataFrame with MultiIndex columns that looks like this:

# sample data
col = pd.MultiIndex.from_arrays([['one', 'one', 'one', 'two', 'two', 'two'],
                                ['a', 'b', 'c', 'a', 'b', 'c']])
data = pd.DataFrame(np.random.randn(4, 6), columns=col)
data

选择特定列的正确，简单方法是什么（例如 ['a'，'c'] ，从第二级开始不是范围？

What is the proper, simple way of selecting only specific columns (e.g. ['a', 'c'], not a range) from the second level?

目前我这样做：

import itertools
tuples = [i for i in itertools.product(['one', 'two'], ['a', 'c'])]
new_index = pd.MultiIndex.from_tuples(tuples)
print(new_index)
data.reindex_axis(new_index, axis=1)

然而，这并不是一个好的解决方案，因为我必须淘汰 itertools ，手工构建另一个MultiIndex然后重新索引（我的实际代码甚至更麻烦，因为列列表不是那么容易获取）。我很确定必须有一些 ix 或 xs 这样做的方式，但我尝试的所有内容都会导致错误。

It doesn't feel like a good solution, however, because I have to bust out itertools, build another MultiIndex by hand and then reindex (and my actual code is even messier, since the column lists aren't so simple to fetch). I am pretty sure there has to be some ix or xs way of doing this, but everything I tried resulted in errors.

推荐答案

这不是很好，但也许：

>>> data
        one                           two                    
          a         b         c         a         b         c
0 -0.927134 -1.204302  0.711426  0.854065 -0.608661  1.140052
1 -0.690745  0.517359 -0.631856  0.178464 -0.312543 -0.418541
2  1.086432  0.194193  0.808235 -0.418109  1.055057  1.886883
3 -0.373822 -0.012812  1.329105  1.774723 -2.229428 -0.617690
>>> data.ix[:,data.columns.get_level_values(1).isin({"a", "c"})]
        one                 two          
          a         c         a         c
0 -0.927134  0.711426  0.854065  1.140052
1 -0.690745 -0.631856  0.178464 -0.418541
2  1.086432  0.808235 -0.418109  1.886883
3 -0.373822  1.329105  1.774723 -0.617690

会起作用吗？

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