Using fuzzywuzzy

Since there are no examples with the fuzzywuzzy package, here's a function I wrote which will return all matches based on a threshold you can set as a user:

Example datframe

df1 = pd.DataFrame({'Key':['Apple', 'Banana', 'Orange', 'Strawberry']})
df2 = pd.DataFrame({'Key':['Aple', 'Mango', 'Orag', 'Straw', 'Bannanna', 'Berry']})

# df1
          Key
0       Apple
1      Banana
2      Orange
3  Strawberry

# df2
        Key
0      Aple
1     Mango
2      Orag
3     Straw
4  Bannanna
5     Berry

Function for fuzzy matching

def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2):
    """
    :param df_1: the left table to join
    :param df_2: the right table to join
    :param key1: key column of the left table
    :param key2: key column of the right table
    :param threshold: how close the matches should be to return a match, based on Levenshtein distance
    :param limit: the amount of matches that will get returned, these are sorted high to low
    :return: dataframe with boths keys and matches
    """
    s = df_2[key2].tolist()
    
    m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit))    
    df_1['matches'] = m
    
    m2 = df_1['matches'].apply(lambda x: ', '.join([i[0] for i in x if i[1] >= threshold]))
    df_1['matches'] = m2
    
    return df_1

Using our function on the dataframes: #1

from fuzzywuzzy import fuzz
from fuzzywuzzy import process

fuzzy_merge(df1, df2, 'Key', 'Key', threshold=80)

          Key       matches
0       Apple          Aple
1      Banana      Bannanna
2      Orange          Orag
3  Strawberry  Straw, Berry

Using our function on the dataframes: #2

df1 = pd.DataFrame({'Col1':['Microsoft', 'Google', 'Amazon', 'IBM']})
df2 = pd.DataFrame({'Col2':['Mcrsoft', 'gogle', 'Amason', 'BIM']})

fuzzy_merge(df1, df2, 'Col1', 'Col2', 80)

        Col1  matches
0  Microsoft  Mcrsoft
1     Google    gogle
2     Amazon   Amason
3        IBM         

Installation:

Pip

pip install fuzzywuzzy

Anaconda

conda install -c conda-forge fuzzywuzzy