How to Perform Fuzzy Matching in Pandas (With Example)

Often you may want to join together two datasets in pandas based on imperfectly matching strings. This is called fuzzy matching.

The easiest way to perform fuzzy matching in pandas is to use the get_close_matches() function from the difflib package.

The following example shows how to use this function in practice.

Example: Fuzzy Matching in Pandas

Suppose we have the following two pandas DataFrames that contain information about various basketball teams:

import pandas as pd

#create two DataFrames
df1 = pd.DataFrame({'team': ['Mavericks', 'Nets', 'Warriors', 'Heat', 'Lakers'],
                    'points': [99, 90, 104, 117, 100]})

df2 = pd.DataFrame({'team': ['Mavricks', 'Warrors', 'Heat', 'Netts', 'Lakes'],
                    'assists': [22, 29, 17, 40, 32]})

#view DataFrames

        team  points
0  Mavericks      99
1       Nets      90
2   Warriors     104
3       Heat     117
4     Lakers     100


       team  assists
0  Mavricks       22
1   Warrors       29
2      Heat       17
3     Netts       40
4     Lakes       32

Now suppose that we would like to merge the two DataFrames based on the team column.

Since the team names are slightly different between the two DataFrames, we must use fuzzy matching to find which team names most closely match.

We can use the get_close_matches() function from the difflib package to do so:

import difflib 

#create duplicate column to retain team name from df2
df2['team_match'] = df2['team']

#convert team name in df2 to team name it most closely matches in df1
df2['team'] = df2['team'].apply(lambda x: difflib.get_close_matches(x, df1['team'])[0])

#merge the DataFrames into one
df3 = df1.merge(df2)

#view final DataFrame

        team  points  assists team_match
0  Mavericks      99       22   Mavricks
1       Nets      90       40      Netts
2   Warriors     104       29    Warrors
3       Heat     117       17       Heat
4     Lakers     100       32      Lakes

The result is one data frame that contains each of the five team names from the first DataFrame along with the team that most closely matches from the second DataFrame.

The team_match column shows the team name from the second DataFrame that most closely matched the team name from the first DataFrame.

Note #1: By default, get_close_matches() returns the three closest matches. However, by using the [0] at the end of the lambda function we were able to only return the closest team name match.

Note #2: You can find the complete documentation for the get_close_matches() function here.

Additional Resources

The following tutorials explain how to perform other common tasks in pandas:

How to Merge Pandas DataFrames on Multiple Columns
How to Merge Two Pandas DataFrames on Index
Pandas Join vs. Merge: What’s the Difference?

3 Replies to “How to Perform Fuzzy Matching in Pandas (With Example)”

  1. Neat, liked it! It will be good to fetch other columns of the source data frame. Often we need the row number and other columns. my goal is to generate a good concordance table. Thanks, Zach. You made my day!

  2. Zach,
    In this scenario both df1 and df2 are of the same size. What if one of the dataframes is larger then the other ie there are multiple lines that can match from each dataframe?
    Thank you for the great article.

Leave a Reply

Your email address will not be published. Required fields are marked *