取消堆叠并重新排序pandas数据帧(Unstack and reorder pandas dataframe)

 我已经四处寻找，但找不到解决方案，如何拆除和重新排序熊猫数据帧。 
 
 假设我有以下数据帧： 
 
df = pd.DataFrame({'type': [1, 2, 2, 1, 2, 1, 2, 1, 2, 2],
                   'band': ['A', 'B', 'C', 'C', 'B', 'B', 'A', 'A', 'B', 'C'],
                   'val': [0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27]})
 
 我可以按'type'和'band'分组以获得两个维度的均值，并使用unstack()将'band'显示为列： 
 
df.groupby(['type', 'band']).mean().unstack(level=1)
 
 我的问题是，如何重新排列列，以便它们处于降序（或更一般地，任意排序）的顺序？ 
 
 输出如下： 
 
    val
band    A   B           C
type            
1   0.215   0.230000    0.210
2   0.240   0.223333    0.235
 
 我想要的是以下内容： 
 
    val
band    C   B           A
type            
1   0.210   0.230000    0.215
2   0.235   0.223333    0.240
 
 在实践中，我有超过3列。 

I've searched around but couldn't find a solution for how to unstack and reorder a pandas dataframe.
 
Suppose I have the following dataframe:
 
df = pd.DataFrame({'type': [1, 2, 2, 1, 2, 1, 2, 1, 2, 2],
                   'band': ['A', 'B', 'C', 'C', 'B', 'B', 'A', 'A', 'B', 'C'],
                   'val': [0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27]})
 
I can group by 'type' and 'band' to get the mean across both dimensions, and use unstack() to show 'band' as columns:
 
df.groupby(['type', 'band']).mean().unstack(level=1)
 
My question is, how can I reorder the columns so that they're in descending (or more generally, any arbitrarily sorted) order?
 
The output looks like this:
 
    val
band    A   B           C
type            
1   0.215   0.230000    0.210
2   0.240   0.223333    0.235
 
What I want is the following:
 
    val
band    C   B           A
type            
1   0.210   0.230000    0.215
2   0.235   0.223333    0.240
 
In practice, I have many more than 3 columns.

原文：https://stackoverflow.com/questions/47580313