How to calculate matching percentage difference between two dataframe

Question

I am looking to find the percentage difference between two dataframes. I have tried using fuzzywuzzy but not getting the expected output for the same.

Suppose i have 2 dataframes with 3 columns each, i want to find the match percentage between these 2 dataframes.

df1

score   id_number       company_name      company_code   
200      IN2231D           AXN pvt Ltd        IN225                 
450      UK654IN        Aviva Intl Ltd        IN115                 
650      SL1432H   Ship Incorporations        CZ555                  
350      LK0678G  Oppo Mobiles pvt ltd        PQ795                 
590      NG5678J             Nokia Inc        RS885                 
250      IN2231D           AXN pvt Ltd        IN215                 

df2

  QR_score     Identity_No       comp_name      comp_code      match_acc   
    200.00      IN2231D           AXN pvt Inc        IN225                 
    420.0       UK655IN        Aviva Intl Ltd        IN315                 
    350.35      SL2252H              Ship Inc        CK555                  
    450.0       LK9978G  Oppo Mobiles pvt ltd        PRS95                 
    590.5       NG5678J             Nokia Inc        RS885                 
    250.0       IN5531D           AXN pvt Ltd        IN215 

Code i am using:

df1 = df[['score','id_number','company_code']]
df2 = df[['QR_score','identity_No','comp_code']]

for idx, row1 in df1.iterrows():
   for idx2, row2 in df2.iterrows():
      df2['match_acc'] =   

Suppose if first row in both the dataframe is matching by 75% so it will be listed in df2['match_acc'] column, same to be followed for each row.

Answer 1

IIUC rename the columns to match then use eq + mean on axis 1:

df1.columns = df2.columns
df2['match_acc'] = df1.eq(df2).mean(axis=1) * 100

df2 :

   QR_score Identity_No             comp_name comp_code  match_acc
0    200.00     IN2231D           AXN pvt Inc     IN225       75.0
1    420.00     UK655IN        Aviva Intl Ltd     IN315       25.0
2    350.35     SL2252H              Ship Inc     CK555        0.0
3    450.00     LK9978G  Oppo Mobiles pvt ltd     PRS95       25.0
4    590.50     NG5678J             Nokia Inc     RS885       75.0
5    250.00     IN5531D           AXN pvt Ltd     IN215       75.0

---

Complete Working Example

import pandas as pd

df1 = pd.DataFrame({
    'score': [200, 450, 650, 350, 590, 250],
    'id_number': ['IN2231D', 'UK654IN', 'SL1432H', 'LK0678G', 'NG5678J',
                  'IN2231D'],
    'company_name': ['AXN pvt Ltd', 'Aviva Intl Ltd', 'Ship Incorporations',
                     'Oppo Mobiles pvt ltd', 'Nokia Inc', 'AXN pvt Ltd'],
    'company_code': ['IN225', 'IN115', 'CZ555', 'PQ795', 'RS885', 'IN215']
})

df2 = pd.DataFrame({
    'QR_score': [200.00, 420.0, 350.35, 450.0, 590.5, 250.0],
    'Identity_No': ['IN2231D', 'UK655IN', 'SL2252H', 'LK9978G', 'NG5678J',
                    'IN5531D'],
    'comp_name': ['AXN pvt Inc', 'Aviva Intl Ltd', 'Ship Inc',
                  'Oppo Mobiles pvt ltd', 'Nokia Inc', 'AXN pvt Ltd'],
    'comp_code': ['IN225', 'IN315', 'CK555', 'PRS95', 'RS885', 'IN215']
})

df1.columns = df2.columns
df2['match_acc'] = df1.eq(df2).mean(axis=1) * 100
print(df2)

---

Assuming cell by cell similarity should be assessed by something like fuzzywuzzy instead, vectorize whatever fuzzywuzzy function to apply to all cells and create a new dataframe from the results. fuzzywuzzy will only work with strings, so handle object type columns and non-objects separately.

import numpy as np
import pandas as pd
from fuzzywuzzy import fuzz

# Make Column Names Match
df1.columns = df2.columns
# Select string (object) columns
t1 = df1.select_dtypes(include='object')
t2 = df2.select_dtypes(include='object')
# Apply fuzz.ratio to every cell of both frames
obj_similarity = pd.DataFrame(np.vectorize(fuzz.ratio)(t1, t2), 
                              columns=t1.columns,
                              index=t1.index)
# Use non-object similarity with eq
other_similarity = df1.select_dtypes(exclude='object').eq(
    df2.select_dtypes(exclude='object')) * 100
# Merge Similarities together and take the average per row
total_similarity = pd.concat((
    obj_similarity, other_similarity
), axis=1).mean(axis=1)

df2['match_acc'] = total_similarity

df2 :

   QR_score Identity_No             comp_name comp_code  match_acc
0    200.00     IN2231D           AXN pvt Inc     IN225      93.25
1    420.00     UK655IN        Aviva Intl Ltd     IN315      66.50
2    350.35     SL2252H              Ship Inc     CK555      49.00
3    450.00     LK9978G  Oppo Mobiles pvt ltd     PRS95      57.75
4    590.50     NG5678J             Nokia Inc     RS885      75.00
5    250.00     IN5531D           AXN pvt Ltd     IN215      92.75