pandas groupby multiple columns gives weird index behavior

Question

I have data as below, with 7 columns. I want to merge all rows that have the same value in columns 1-6, and sum the corresponding column-7 data.

0.1  0.2  0.3  0.1  0.2  0.3  0.001
0.1  0.2  0.3  0.1  0.2  0.3  0.002
0.9  0.9  0.9  0.1  0.1  0.1  0.002
0.9  0.9  0.9  0.8  0.8  0.8  0.1 
0.9  0.9  0.9  0.8  0.8  0.8  0.2 
0.1  0.2  0.3  0.1  0.2  0.3  0.001
0.1  0.2  0.3  0.1  0.1  0.1  0.002
0.9  0.9  0.9  0.1  0.1  0.1  0.002

I had tried this code:

import pandas as pd

df1 = pd.read_csv('test-data',sep='\s+',header=None,engine='python')
key1_sum = df1.groupby([0,1,2,3,4,5]).sum()

print(key1_sum)

I expected to get this output:

0.1 0.2 0.3 0.1 0.1 0.1  0.002
0.1 0.2 0.3 0.1 0.2 0.3  0.004
0.9 0.9 0.9 0.1 0.1 0.1  0.004
0.9 0.9 0.9 0.8 0.8 0.8  0.300

...but I actually got this output:

                             6
0   1   2   3   4   5         
0.1 0.2 0.3 0.1 0.1 0.1  0.002
                0.2 0.3  0.004
0.9 0.9 0.9 0.1 0.1 0.1  0.004
            0.8 0.8 0.8  0.300

So I just want to not output the head, and complete the elements in the corresponding column.

Could you please give me some suggestions about that?

Answer 1

When we try to groupby is we do not want the groupby key as index, we can pass to the as_index=False

key1_sum = df1.groupby([0,1,2,3,4,5],as_index=False).sum()