How to drop the all the 1’s in a correlation matrix

Solution for How to drop the all the 1’s in a correlation matrix
is Given Below:

I’m trying to change/eliminate the 1’s that run diagonally in a correlation matrix so that when I take the average of the rows of the correlation matrix, the 1s don’t affect the mean of each of the rows.

Let’s say I have the dataset,

    A   B   C   D   E   F
0   45  100 58  78  80  35
1   49  80  80  104 58  20
2   49  80  65  78  79  20
3   65  100 80  159 83  45
4   65  123 78  115 100 50
5   45  122 84  100 85  20
6   60  120 78  44  105 55
7   62  80  109 48  78  25
8   63  39  85  65  79  25
9   80  52  100 50  103 30
10  80  43  78  64  120 60
11  60  60  130 43  135 45
12  80  50  111 59  115 50
13  82  65  130 63  78  90
14  83  58  85  80  45  80
15  100 64  100 65  30  70

When I do dfcorr = df.corr()
dfcorr, I get

       A            B           C           D          E           F
A   1.000000    0.842125    0.834808    0.832773    0.844158    0.806787
B   0.842125    1.000000    0.847606    0.907595    0.818668    0.863645
C   0.834808    0.847606    1.000000    0.718199    0.804671    0.582033
D   0.832773    0.907595    0.718199    1.000000    0.884236    0.878421
E   0.844158    0.818668    0.804671    0.884236    1.000000    0.718668
F   0.806787    0.863645    0.582033    0.878421    0.718668    1.000000

I want all the 1’s to be dropped so that if I want to take the mean of each of the rows, the 1’s won’t affect them.

If you are working with it as a data frame this will work:

df=pd.DataFrame({'c1':[1, 0, 0.3, 0.4], 'c2':[0.2, 1, 0.6, 0.4], 'c3':[0.1, 0, 1, 0.4], 'c4':[0.7, 0.2, 0.2, 1]} )

df.where(df!=1).mean(axis=1)

This only works correctly if all 1’s are on the diagonal.