根据列值对具有MultiIndex的pandas DataFrame进行排序 [英] Sort pandas DataFrame with MultiIndex according to column value

问题描述

在控制台中打印后，我有一个具有MultiIndex的DataFrame:

I have a DataFrame with MultiIndex looking like this after printing in the console:

                             value  indA  indB
           scenarioId group                        
2015-04-13    1       A           -54.0   1.0   1.0
                      B          -160.0   1.0   1.0
                      C           -15.0   0.0   1.0
              2       A           -83.0   1.0   1.0
              3       A           -80.0   2.0   2.0
              4       A          -270.0   2.0   2.0
2015-04-14    1       A           -56.0   1.0   1.0
                      B            -1.0   1.0   1.0
                      C           -60.0   0.0   1.0
              2       A           -32.0   1.0   1.0
              3       A           -91.0   2.0   2.0
              4       A           -17.0   2.0   2.0

在我的初始数据集中使用groupby和sum函数后，我得到了它.

I got it after I used the groupby and sum functions on my initial dataset.

我想保持相同的格式，但是要根据value列对其进行排序.我已经尝试使用排序功能来做到这一点，但是我认为拥有MultiIndex的第一个索引(用于日期)没有名称的事实是一个问题.

I would like to keep the same format, but order it according to the value column. I have tried hard to do it using the sorting functions, but I think that the fact of having the first index (for the dates) of the MultiIndex without name is a problem.

本质上，输出应如下所示:

Essentially, the output should look like this:

                             value  indA  indB
           scenarioId group                        
2015-04-13   1        B          -160.0   1.0   1.0
                      A           -54.0   1.0   1.0
                      C           -15.0   0.0   1.0
             2        A           -83.0   1.0   1.0
             3        A           -80.0   2.0   2.0
             4        A          -270.0   2.0   2.0
2015-04-14   1        C           -60.0   1.0   1.0
                      A           -56.0   1.0   1.0
                      B            -1.0   0.0   1.0
             2        A           -32.0   1.0   1.0
             3        A           -91.0   2.0   2.0
             4        A           -17.0   2.0   2.0

有人可以给我启发吗?

谢谢.

推荐答案

您可以使用

You can use sort_values + sort_index:

print (df.sort_values('value').sort_index(level=[0,1], sort_remaining=False))
                             value  indA  indB
           scenarioId group                   
2015-04-13 1          B     -160.0   1.0   1.0
                      A      -54.0   1.0   1.0
                      C      -15.0   0.0   1.0
           2          A      -83.0   1.0   1.0
           3          A      -80.0   2.0   2.0
           4          A     -270.0   2.0   2.0
2015-04-14 1          C      -60.0   0.0   1.0
                      A      -56.0   1.0   1.0
                      B       -1.0   1.0   1.0
           2          A      -32.0   1.0   1.0
           3          A      -91.0   2.0   2.0
           4          A      -17.0   2.0   2.0

另一种解决方案- sort_values 与 reset_index 和 set_index :

Another solution - sort_values with reset_index and set_index:

df = df.reset_index()
       .sort_values(['level_0','scenarioId','value'])
       .set_index(['level_0','scenarioId','group'])
print (df)
                             value  indA  indB
level_0    scenarioId group                   
2015-04-13 1          B     -160.0   1.0   1.0
                      A      -54.0   1.0   1.0
                      C      -15.0   0.0   1.0
           2          A      -83.0   1.0   1.0
           3          A      -80.0   2.0   2.0
           4          A     -270.0   2.0   2.0
2015-04-14 1          C      -60.0   0.0   1.0
                      A      -56.0   1.0   1.0
                      B       -1.0   1.0   1.0
           2          A      -32.0   1.0   1.0
           3          A      -91.0   2.0   2.0
           4          A      -17.0   2.0   2.0

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