I need to compare two df's for matches and mismatches, I also need to identify which answer is from the master df in the event of a mismatch

Question

I have two data frames in python and want to compare the two to look for matches as well as mismatches. It is important though that I can identify in the mismatches which answer is from the master answer sheet and which answer is from the users answer.

I decided to use the pandas df.where function to achieve this, it worked except for the ability to identify which answer is from the master answer sheet and which is from the users answers in the event of a mismatch.

# I have a DataFrame called df_master (master answer sheet)

import pandas as pd

df_master = pd.DataFrame({'B0': [1, 0, 0, 0, 0, 1],
            'B1': [0, 0, 0, 0, 1, 0],
            'B2': [0, 1, 0, 0, 0, 0],
            'B3': [0, 0, 1, 0, 0, 0],
            'B4': [0, 0, 0, 1, 0, 0]})
print(df_master)

#    B0  B1  B2  B3  B4
# 0   1   0   0   0   0
# 1   0   0   1   0   0
# 2   0   0   0   1   0
# 3   0   0   0   0   1
# 4   0   1   0   0   0
# 5   1   0   0   0   0

# I also have a DataFrame called df_answers (users answers)

df_answers = pd.DataFrame({'B0': [0, 0, 0, 0, 0, 1],
            'B1': [1, 0, 0, 0, 1, 0],
            'B2': [0, 0, 0, 0, 0, 0],
            'B3': [0, 1, 1, 0, 0, 0],
            'B4': [0, 0, 0, 1, 0, 0]})

print(df_answers)

#    B0  B1  B2  B3  B4
# 0   0   1   0   0   0
# 1   0   0   0   1   0
# 2   0   0   0   1   0
# 3   0   0   0   0   1
# 4   0   1   0   0   0
# 5   1   0   0   0   0

# when I compare the the two df's, for each match, matches correctly, where there
# is no match I have used other=2.  However this is a problem as I cannot see which is
# the correct answer.  It would be great if there was a way to work the code to reflect
# the master as a 3 and the incorrect answer from the users to stay 2?

comparison = df_master.where(df_master.values==df_answers.values, other=2)

print(comparison)

# My Results

#    B0  B1  B2  B3  B4
# 0   2   2   0   0   0
# 1   0   0   2   2   0
# 2   0   0   0   1   0
# 3   0   0   0   0   1
# 4   0   1   0   0   0
# 5   1   0   0   0   0

# Expected Results

#    B0  B1  B2  B3  B4
# 0   3   2   0   0   0
# 1   0   0   3   2   0
# 2   0   0   0   1   0
# 3   0   0   0   0   1
# 4   0   1   0   0   0
# 5   1   0   0   0   0

Answer 1

In your case using replace after str sum , ps : you define the mapping by yourself like {'00':'both failed', '01': 'master failed'...}

(df_answers.astype(str)+df_master.astype(str)).replace({'00':0,'01':3,'10':2,'11':1})
Out[129]: 
   B0  B1  B2  B3  B4
0   3   2   0   0   0
1   0   0   3   2   0
2   0   0   0   1   0
3   0   0   0   0   1
4   0   1   0   0   0
5   1   0   0   0   0