Creating new columns based on 3 column and create new data frame Heading

Consider my data frame 
rs123   T   C   0   0   1   1   0   0   1   0   0   1   0   0
rs124   T   C   0   0   1   0   0   1   0   0   1   0   0   1
rs125   A   A   1   0   0   1   0   0   1   0   0   1   0   0 

Similarity, i have total 93 columns excluding first three

I want to create my data as 



And then transform into new data frame as below


For first row if 1 is present in column 1 then output should be TT
For first row if 1 is present in column 2 then output should be TC
For first row if 1 is present in column 3 then output should be CC


For more detail you can refer below snip



Kindly help me to find solution using python, Its very urgent

Thanks in Advance.
The question could have been framed better. Checkout the code below, in which your final dataframe will be in output.

import pandas as pd

input_array = [["rs123", "T", "C", 0, 0, 1, 1, 0, 0, 1, 0, 0], ["rs124", "A", "G", 0, 0, 1, 0, 1, 0, 0, 0, 1]]

raw_pd = pd.DataFrame(input_array).astype(str)

def change(a):
    if list(a)[2]+list(a)[3]+list(a)[4] == "100":
        return list(a)[0] + list(a)[0]
    elif list(a)[2]+list(a)[3]+list(a)[4] == "010":
        return list(a)[0] + list(a)[1]
    else:
        return list(a)[1] + list(a)[1]

output = pd.DataFrame()

output['S1'] = raw_pd[[1, 2, 3, 4, 5]].apply(lambda x: change(x), axis = 1)

output['S2'] = raw_pd[[1, 2, 6, 7, 8]].apply(lambda x: change(x), axis = 1)

output['S3'] = raw_pd[[1, 2, 9, 10, 11]].apply(lambda x: change(x), axis = 1)

output['SNP'] = raw_pd[0]




Hope this helps ;) Mark this as the correct answer if you have no other doubts.