["python","pandas","dataframe","conditional-statements","switch-statement"]
Create new column based on values from other columns / apply a function of multiple columns, row-wise in Pandas

I want to apply my custom function (it uses an if-else ladder) to these six columns (ERI_Hispanic, ERI_AmerInd_AKNatv, ERI_Asian, ERI_Black_Afr.Amer, ERI_HI_PacIsl, ERI_White) in each row of my dataframe. I've tried different methods from other questions but still can't seem to find the right answer for my problem. The critical piece of this is that if the person is counted as Hispanic they can't be counted as anything else. Even if they have a "1" in another ethnicity column they still are counted as Hispanic not two or more races. Similarly, if the sum of all the ERI columns is greater than 1 they are counted as two or more races and can't be counted as a unique ethnicity(except for Hispanic). It's almost like doing a for loop through each row and if each record meets a criterion they are added to one list and eliminated from the original. From the dataframe below I need to calculate a new column based on the following spec in SQL: CRITERIA IF [ERI_Hispanic] = 1 THEN RETURN “Hispanic” ELSE IF SUM([ERI_AmerInd_AKNatv] + [ERI_Asian] + [ERI_Black_Afr.Amer] + [ERI_HI_PacIsl] + [ERI_White]) > 1 THEN RETURN “Two or More” ELSE IF [ERI_AmerInd_AKNatv] = 1 THEN RETURN “A/I AK Native” ELSE IF [ERI_Asian] = 1 THEN RETURN “Asian” ELSE IF [ERI_Black_Afr.Amer] = 1 THEN RETURN “Black/AA” ELSE IF [ERI_HI_PacIsl] = 1 THEN RETURN “Haw/Pac Isl.” ELSE IF [ERI_White] = 1 THEN RETURN “White” Comment: If the ERI Flag for Hispanic is True (1), the employee is classified as “Hispanic” Comment: If more than 1 non-Hispanic ERI Flag is true, return “Two or More” DATAFRAME lname fname rno_cd eri_afr_amer eri_asian eri_hawaiian eri_hispanic eri_nat_amer eri_white rno_defined 0 MOST JEFF E 0 0 0 0 0 1 White 1 CRUISE TOM E 0 0 0 1 0 0 White 2 DEPP JOHNNY 0 0 0 0 0 1 Unknown 3 DICAP LEO 0 0 0 0 0 1 Unknown 4 BRANDO MARLON E 0 0 0 0 0 0 White 5 HANKS TOM 0 0 0 0 0 1 Unknown 6 DENIRO ROBERT E 0 1 0 0 0 1 White 7 PACINO AL E 0 0 0 0 0 1 White 8 WILLIAMS ROBIN E 0 0 1 0 0 0 White 9 EASTWOOD CLINT E 0 0 0 0 0 1 White
OK, two steps to this - first is to write a function that does the translation you want - I've put an example together based on your pseudo-code: def label_race(row): if row['eri_hispanic'] == 1: return 'Hispanic' if row['eri_afr_amer'] + row['eri_asian'] + row['eri_hawaiian'] + row['eri_nat_amer'] + row['eri_white'] > 1: return 'Two Or More' if row['eri_nat_amer'] == 1: return 'A/I AK Native' if row['eri_asian'] == 1: return 'Asian' if row['eri_afr_amer'] == 1: return 'Black/AA' if row['eri_hawaiian'] == 1: return 'Haw/Pac Isl.' if row['eri_white'] == 1: return 'White' return 'Other' You may want to go over this, but it seems to do the trick - notice that the parameter going into the function is considered to be a Series object labelled "row". Next, use the apply function in pandas to apply the function - e.g. df.apply(label_race, axis=1) Note the axis=1 specifier, that means that the application is done at a row, rather than a column level. The results are here: 0 White 1 Hispanic 2 White 3 White 4 Other 5 White 6 Two Or More 7 White 8 Haw/Pac Isl. 9 White If you're happy with those results, then run it again, saving the results into a new column in your original dataframe. df['race_label'] = df.apply(label_race, axis=1) The resultant dataframe looks like this (scroll to the right to see the new column): lname fname rno_cd eri_afr_amer eri_asian eri_hawaiian eri_hispanic eri_nat_amer eri_white rno_defined race_label 0 MOST JEFF E 0 0 0 0 0 1 White White 1 CRUISE TOM E 0 0 0 1 0 0 White Hispanic 2 DEPP JOHNNY NaN 0 0 0 0 0 1 Unknown White 3 DICAP LEO NaN 0 0 0 0 0 1 Unknown White 4 BRANDO MARLON E 0 0 0 0 0 0 White Other 5 HANKS TOM NaN 0 0 0 0 0 1 Unknown White 6 DENIRO ROBERT E 0 1 0 0 0 1 White Two Or More 7 PACINO AL E 0 0 0 0 0 1 White White 8 WILLIAMS ROBIN E 0 0 1 0 0 0 White Haw/Pac Isl. 9 EASTWOOD CLINT E 0 0 0 0 0 1 White White