Pandas:基于多列对数据框进行排序 [英] Pandas: Sort a dataframe based on multiple columns

问题描述

我知道这个问题已经被问过好几次了.但没有一个答案符合我的情况.

I know that this question has been asked several times. But none of the answers match my case.

我有一个包含列、部门和员工计数的 Pandas 数据框.我需要按降序对employee_count 列进行排序.但如果 2 个 employee_counts 之间存在平局，则应根据部门的字母顺序对它们进行排序.

I've a pandas dataframe with columns,department and employee_count. I need to sort the employee_count column in descending order. But if there is a tie between 2 employee_counts then they should be sorted alphabetically based on department.

   Department Employee_Count
0    abc          10
1    adc          10
2    bca          11
3    cde          9
4    xyz          15

required output:

   Department Employee_Count
0    xyz          15
1    bca          11
2    abc          10
3    adc          10
4    cde          9

这是我试过的.

df = df.sort_values(['Department','Employee_Count'],ascending=[True,False])

但这只是按字母顺序对部门进行排序.

But this just sorts the departments alphabetically.

我还尝试先按部门排序，然后按 Employee_Count 排序.像这样:

I've also tried to sort by Department first and then by Employee_Count. Like this:

df = df.sort_values(['Department'],ascending=[True])
df = df.sort_values(['Employee_Count'],ascending=[False])

这也没有给我正确的输出:

This doesn't give me correct output either:

   Department Employee_Count
4    xyz          15
2    bca          11
1    adc          10
0    abc          10
3    cde          9

它首先给出adc"，然后给出abc".请帮助我.

It gives 'adc' first and then 'abc'. Kindly help me.

推荐答案

您可以交换列表中的列以及ascending 参数中的值:

You can swap columns in list and also values in ascending parameter:

说明:

列名的顺序是排序的顺序，首先按Employee_Count降序排序，如果Employee_Count中有重复，则按Department排序只重复行升序.

Order of columns names is order of sorting, first sort descending by Employee_Count and if some duplicates in Employee_Count then sorting by Department only duplicates rows ascending.

df1 = df.sort_values(['Employee_Count', 'Department'], ascending=[False, True])
print (df1)
  Department  Employee_Count
4        xyz              15
2        bca              11
0        abc              10 <-
1        adc              10 <-
3        cde               9

或者为了测试是否使用第二个 False 然后重复的行正在排序 descending:

Or for test if use second False then duplicated rows are sorting descending:

df2 = df.sort_values(['Employee_Count', 'Department',],ascending=[False, False])
print (df2)
  Department  Employee_Count
4        xyz              15
2        bca              11
1        adc              10 <-
0        abc              10 <-
3        cde               9

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