[FIXED] Sum only for repeated values in two another columns

Issue

I have the following dataframe:

      col1     col2     col3
0     prog1    id001     1
1     prog2    id001     0
2     prog2    id001     1
3     prog1    id002     0
4     prog3    id002     0
5     prog4    id002     1
6     prog2    id003     0
7     prog5    id003     1
8     prog5    id003     1
9     prog3    id004     1

Goal :

I want to add a new column calculating a sum as follows:

      col1     col2     col3     sum_prog
0     prog1    id001     1       1
1     prog2    id001     0       1
2     prog2    id001     1       1
3     prog1    id002     0       1
4     prog3    id002     0       1
5     prog4    id002     1       1
6     prog2    id003     0       1
7     prog5    id003     1       2
8     prog5    id003     1       2
9     prog3    id004     1       1

As you can see id003 has sum_prog ==2 for prog5, because prog5 occured twice in the 'col1' and sum of corresponding rows in 'col3' = 2

I tryed to solve this by df.groupby() but till now I didn't get the desired output.

Solution

You can use .transform to generate the pd.Series directly.

Group on "col1" and sum the values in "col3".

df["sum_prog"] = df.groupby(["col1"]).col3.transform("sum")

Out

    col1   col2  col3  sum_prog
0  prog1  id001     1         1
1  prog2  id001     0         1
2  prog2  id001     1         1
3  prog1  id002     0         1
4  prog3  id002     0         1
5  prog4  id002     1         1
6  prog2  id003     0         1
7  prog5  id003     1         2
8  prog5  id003     1         2
9  prog3  id004     1         1

Answered By - Ian Thompson