I have a small sample of my dataframe here:

df = pd.DataFrame([[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]], columns = ["CHAP1SEC1", "CHAP1SEC2", "CHAP1SEC3", "CHAP1SEC4", "CHAP1SEC5"], index = [0, 1, 2, 3])

Note: the variables are binary.

I'm trying to essentially merge these 4 rows into one row, keeping any non-zero entries in the columns. Since the variables are binary, my go-to was just to take column sums.

df.sum(axis = 1)

However, while this gives me the values I want, it is not returned in the same original dataframe structure.

Essentially, I would like to take the column sums of a dataframe, while keeping the structure of that dataframe. Ideally, my output would be as follows:

I feel there must be a super simple solution that I am just not seeing and I couldn't find a similar question already posted on SO.

Any help is appreciated!

CodePudding user response：

here is one way to do it

df.sum(axis=0).to_frame().T

or

df.sum().to_frame().T

    CHAP1SEC1   CHAP1SEC2   CHAP1SEC3   CHAP1SEC4   CHAP1SEC5
0           1           0           1           1           1