How to sum pandas columns across the same record for columns which begins with the same word

I have created the following pandas dataframe:

ds = {'col1' : [1,1,1,1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,4,4,4,4,4],
      'feature1' : [1,1,1,1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,4,4,4,4,4],
      'col2' : [12,3,4,5,4,3,2,3,4,6,7,8,3,3,65,4,3,2,32,1,2,3,4,5,32],
      
      }

df = pd.DataFrame(data=ds)

The dataframe looks like this:

print(df)
    col1  feature1  col2
0      1         1    12
1      1         1     3
2      1         1     4
3      1         1     5
4      1         1     4
5      1         1     3
6      1         1     2
7      2         2     3
8      2         2     4
9      2         2     6
10     2         2     7
11     3         3     8
12     3         3     3
13     3         3     3
14     3         3    65
15     3         3     4
16     4         4     3
17     4         4     2
18     4         4    32
19     4         4     1
20     4         4     2
21     4         4     3
22     4         4     4
23     4         4     5
24     4         4    32

I need to create a new column (called sumOfCols) which is the sum of the values contained in the columns whose names begin with col (in the example above the column feature1 would not be included in the calculation).

The resulting dataframe would look like this:

Can someone help me please? Thanks in advance.

You can filter the columns using a regex, then sum on axis=1:

df["sumOfCols"] = df.filter(regex="^col").sum(axis=1)

    col1  feature1  col2  sumOfCols
0      1         1    12         13
1      1         1     3          4
2      1         1     4          5
3      1         1     5          6
4      1         1     4          5
5      1         1     3          4
6      1         1     2          3
7      2         2     3          5
8      2         2     4          6
9      2         2     6          8
10     2         2     7          9
11     3         3     8         11
12     3         3     3          6
13     3         3     3          6
14     3         3    65         68
15     3         3     4          7
16     4         4     3          7
17     4         4     2          6
18     4         4    32         36
19     4         4     1          5
20     4         4     2          6
21     4         4     3          7
22     4         4     4          8
23     4         4     5          9
24     4         4    32         36