jottbe
2020-11-29 01:53:33
You could do that useing applymap like this:
df[['is_r_c', 'is_r_d']]= df[['c_o', 'c_d']].applymap(lambda x: 1 if my_is_r_c(x) else 0)
In case you want the columns combined, and there in fact is only one result column, which should contain 1 if your function evaluates to 1 for at least one of the columns, you can do that with a small variation like this:
df[['is_r_c']]= df[['c_o', 'c_d']].applymap(lambda x: 1 if my_is_r_c(x) else 0).max(axis='columns')
thank you all for the great help. this one did it: df[['is_r_c']]= df[['c_o', 'c_d']].applymap(lambda x: 1 if my_is_r_c(x) else 0).max(axis='columns') as i need to put it in the same column
@joao thank you for your feedback. I'm glad I could help.