Python Pandas: How to sum up columns that also include missing values?

Question

I have a df with several columns by three of them are like this:

num1  num2   num3 
1      NaN    1
NaN     1     1
1       1     1

and I would like to create another column "sum_num" and add the values in per row for all the columns (alternative would be to count number of ones cause the values are all ones).

Expected outcome:

num1  num2   num3 sum_num
1      NaN    1      2
NaN     1     1      2
1       1     1      3

Now I have tried this code but what I am having in the "sum_num" columns in only NaNs.

df['sum_num'] = df.num1 + df.num2 + df.num3

Does anybody know how to ignore missing values and still either sum the ones or count them to get the desired outcome per row?

Answer 1

sum on axis=1

In [202]: df['sum_num'] = df.sum(axis=1)

In [203]: df
Out[203]:
   num1  num2  num3  sum_num
0     1   NaN     1        2
1   NaN     1     1        2
2     1     1     1        3