如何根据 python 中多列的值填充一列？

Question

I have a data frame where I am trying to populate a column based on the values in the other columns. There should only be one Sxx per row, but it could be in a different column in each row. So in row 1, S11 could be in column 4, but in row 2 S15 could be in column 5. What I want to do is to take all of the Sxx values and put them in one column at the end. Any help here would be much appreciated!

Here is an example of the data frame. If you look at the second to last and last row, that is emblematic of what I am trying to solve for.

    Entity1 Entity2 Entity3 Entity4 Entity5 Entity6 Entity7 Entity8 Entity9 School
0   C12 CAD G01 S09 None    None    None    None    None    NaN
1   C12 CAD G01 S09 None    None    None    None    None    NaN
2   C12 CAD G01 S09 None    None    None    None    None    NaN
3   C12 CAD G01 S09 None    None    None    None    None    NaN
4   C12 CAD G01 S09 None    None    None    None    None    NaN
... ... ... ... ... ... ... ... ... ... ...
322976  C07 CAD G01 S09 None    None    None    None    None    NaN
322977  C13 CAD G01 S06 None    None    None    None    None    NaN
322978  C13 CAD G01 S06 None    None    None    None    None    NaN
322979  C13 CAD G01 S06 None    None    None    None    None    NaN
322980  CAD G01 S14 W04 None    None    None    None    None    NaN
322981 rows × 10 columns

Answer 1

Here is one way of doing it, probably not the most optimal, using regex. It assumes there is always one Sxx at each row. Assuming your DataFrame is data_df :

import pandas as pd
import re

last_col = list()
for index, row in data_df.iterrows():
    for cell in row.to_list():
        if re.match('S[0-9]+', cell):
            last_col.append(cell)
            break

data_df['last_col'] = last_col

Answer 2

Find the locations of Sxx's with the apply(lambda match) then propagate those to the last column with ffill(axis=1) then add that last column ( .iloc[:,-1:] ) to the DF.

df['Last_S_Col'] = \
    df[df.apply(lambda x: x.str.match('S[0-9]{2}'), axis=1)].ffill(axis=1).iloc[:,-1:]

df:

  Entity1 Entity2 Entity3 Entity4 Entity5 Entity6 Entity7 Entity8 Entity9  \
0     C12     CAD     S12     G01    None    None    None    None    None   
1     C12     CAD     G01     S09    None    None    None    None    None   
2     C12     CAD     G01     S09    None    None    None    None    None   
3     C12     S14     G01     CAD    None    None    None    None    None   
4     S01     CAD     G01     C12    None    None    None    None    None   

   School Last_S_Col  
0     NaN        S12  
1     NaN        S09  
2     NaN        S09  
3     NaN        S14  
4     NaN        S01