跨多列排序 pandas

Question

df = pd.DataFrame([["Alpha", 3, 2, 4], ["Bravo", 2, 3, 1], ["Charlie", 4, 1, 3], ["Delta", 1, 4, 2]], 
              columns = ["Company", "Running", "Combat", "Range"])
print(df)
  Company Running   Combat  Range
0   Alpha      3      2       4
1   Bravo      2      3       1
2   Charlie    4      1       3
3   Delta      1      4       2

Hi, I am trying to sort the the following dataframe so the rows would be arranged such that the best performing across the three columns would be at the top. In this case would be Bravo company as it is 2 in running, 3 in drills and 1 in range.

Would this approach work if the list have a lot more companies and it is hard to know the exact "best performing company"?

I have tried:

df_sort = df.sort_values(['Running', 'Combat', 'Range'], ascending=[True, True, True])

current output:

    Company Running Combat  Range
1   Delta      1      4     2
0   Bravo      2      3     1
3   Alpha      3      2     4
2   Charlie    4      1     3

but it doesn't turn out how I wanted it to be. Can this be done through pandas? I was expecting the output to be:

Company Running Combat  Range
0   Bravo   2     3     1
1   Delta   1     4     2
2   Charlie 4     1     3
3   Alpha   3     2     4

Answer 1

If want sorting by mean s per rows first create mean , then add Series.argsort for positions of sorted values and last change order of values by DataFrame.iloc :

df1 = df.iloc[df.mean(axis=1).argsort()]
print (df1)
   Company  Running  Combat  Range
1    Bravo        2       3      1
3    Delta        1       4      2
2  Charlie        4       1      3
0    Alpha        3       2      4

EDIT: If need remove some columns before by DataFrame.drop :

cols = ['Overall','Subordination']
df2 = text_df.iloc[text_df.drop(cols, axis=1).mean(axis=1).argsort()]
print (df2)
   Company  Running  Combat  Overall Subordination  Range
1    Bravo        2       3     0.70          Poor      1
3    Delta        1       4     0.83          Good      2
2  Charlie        4       1     0.81          Good      3
0    Alpha        3       2     0.91     Excellent      4