Pandas create new column based on values from other columns / apply a function of multiple columns, row-wise

====================== 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           WhiteOK, 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:

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 (lambda row: label_race(row), 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 (lambda row: label_race(row), 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

just a note: if you're only feeding the row into your function, you can just do: df.apply(label_race, axis=1) 
1
If I wanted to do something similar with another row could I use the same function? For example, from the results, if ['race_label'] == "White" return 'White' and so on. But if the ['race_label'] == 'Unknown' return the values from ['rno_defined'] column. I assume the same function would work, but I can't seem to figure out how to get the values from the other column. 
2
You could write a new function, that looks at the 'race_label' field, and send the results into a new field, or - and I think this might be better in this case, edit the original function, changing the final return 'Other' line to return row['rno_defined'] which should substitute the value from that column in those cases where the set of if/then statements doesn't find a match (i.e. where currently, you see 'Other')