I have two example dataframes as follows:

df1 = pd.DataFrame({'Name': {0: 'John', 1: 'Bob', 2: 'Shiela'}, 'Degree': {0: 'Masters', 1: 'Graduate', 2: 'Graduate'}, 'Age': {0: 27, 1: 23, 2: 21}}) df2 = pd.DataFrame({'Name': {0: 'John S.', 1: 'Bob K.', 2: 'Frank'}, 'Degree': {0: 'Master', 1: 'Graduated', 2: 'Graduated'}, 'GPA': {0: 3, 1: 3.5, 2: 4}}) 

I want to merge them together based on two columns Name and Degree with fuzzy matching method to drive out possible duplicates. This is what I have realized with the help from reference here: Apply fuzzy matching across a dataframe column and save results in a new column

from fuzzywuzzy import fuzz
from fuzzywuzzy import processcompare = pd.MultiIndex.from_product([df1['Name'],df2['Name']]).to_series()def metrics(tup):return pd.Series([fuzz.ratio(*tup),fuzz.token_sort_ratio(*tup)],['ratio', 'token'])
compare.apply(metrics)compare.apply(metrics).unstack().idxmax().unstack(0)compare.apply(metrics).unstack(0).idxmax().unstack(0)

Let's say fuzz.ratio of one's Name and Degree both are higher than 80 we consider they are same person. And taken Name and Degree from df1 as default. How can I get a following expected result? Thanks.

df = df1.merge(df2, on = ['Name', 'Degree'], how = 'outer')Name     Degree   Age  GPA    duplicatedName   duplicatedDegree 
0     John    Masters  27.0  3.0         John S.          Master
1      Bob   Graduate  23.0  3.5          Bob K.         Graduated
2   Shiela   Graduate  21.0  NaN          NaN            Graduated
3    Frank  Graduated   NaN  4.0          NaN            Graduate

I think ratio should be lower, for me working 60. Create Series with list comprehension, filter by N and get maximal value. Last map with fillna and last merge:

from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from  itertools import productN = 60
names = {tup: fuzz.ratio(*tup) for tup in product(df1['Name'].tolist(), df2['Name'].tolist())}s1 = pd.Series(names)
s1 = s1[s1 > N]
s1 = s1[s1.groupby(level=0).idxmax()]print (s1)
John S.    John
Bob K.      Bob
dtype: objectdegrees = {tup: fuzz.ratio(*tup) for tup in product(df1['Degree'].tolist(), df2['Degree'].tolist())}s2 = pd.Series(degrees)
s2 = s2[s2 > N]
s2 = s2[s2.groupby(level=0).idxmax()]
print (s2)
Graduated    Graduate
Master        Masters
dtype: objectdf2['Name'] = df2['Name'].map(s1).fillna(df2['Name'])
df2['Degree'] = df2['Degree'].map(s2).fillna(df2['Degree'])
#generally slowier alternative
#df2['Name'] = df2['Name'].replace(s1)
#df2['Degree'] = df2['Degree'].replace(s2)

df = df1.merge(df2, on = ['Name', 'Degree'], how = 'outer')
print (df)Name    Degree   Age  GPA
0    John   Masters  27.0  3.0
1     Bob  Graduate  23.0  3.5
2  Shiela  Graduate  21.0  NaN
3   Frank  Graduate   NaN  4.0