根据列值对具有MultiIndex的pandas DataFrame进行排序

本文介绍了根据列值对具有MultiIndex的pandas DataFrame进行排序的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 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 sort_values + sort_index :

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