Fuzzy Match List with Column in a Data Frame

Fuzzy matching is a technique used to find the best match between two sets of data. In this article, we will explore how to use fuzzy matching to create a new column that contains the best match from a list for each value in a given column.

Introduction

Fuzzy matching can be useful in various scenarios such as autocomplete suggestions, spell checking, and data cleaning. The fuzzywuzzy library is a Python package that provides an efficient way to perform fuzzy matching using the Levenshtein distance algorithm.

In this article, we will focus on how to use fuzzy matching to create a new column that contains the best match from a list for each value in a given column.

Prerequisites

To follow along with this article, you will need:

• Python 3.x
• Pandas library (for data manipulation)
• Fuzzywuzzy library (for fuzzy matching)

You can install the required libraries using pip:

pip install pandas fuzzywuzzy

The Problem

Let’s say we have a data frame df that contains two columns: FOO and PETS. We want to create a new column NEW_PETS that contains the best match from a list of strings (L) for each value in the PETS column.

The Code

Here is an example code snippet that demonstrates how to perform fuzzy matching:

from fuzzywuzzy import process
from fuzzywuzzy import fuzz

# Define the list of strings
L = ['ducks', 'frogs', 'doggies']

# Create a data frame with sample data
df = pd.DataFrame({
    'FOO': ['a', 'b', 'c'],
    'PETS': ['duckz', 'frags', 'doggies']
})

def fuzz_m(col, pet_list, score_t):
    # Perform fuzzy matching for each value in the column
    new_name, score = process.extractOne(col, pet_list, scorer=score_t)
    if score < 95:
        return col
    else:
        return new_name

# Apply the fuzz_m function to the PETS column and create a new column NEW_PETS
df['NEW_PETS'] = df['PETS'].apply(fuzz_m, pet_list=L, score_t=fuzz.ratio)

The Error: Tuple Index Out of Range

The original code snippet had an error where it was returning tuple instead of the desired value. This is because the fuzz_m function was only called once and its return value was broadcast into all entries of the series df['NEW_PETS'].

To fix this, we can modify the fuzz_m function to return a single value instead of a tuple.

The Corrected Code

Here is the corrected code snippet:

from fuzzywuzzy import process
from fuzzywuzzy import fuzz
import pandas as pd

# Define the list of strings
L = ['ducks', 'frogs', 'doggies']

# Create a data frame with sample data
df = pd.DataFrame({
    'FOO': ['a', 'b', 'c'],
    'PETS': ['duckz', 'frags', 'doggies']
})

def fuzz_m(col, pet_list, score_t):
    # Perform fuzzy matching for each value in the column
    new_name, score = process.extractOne(col, pet_list, scorer=score_t)
    if score < 95:
        return col
    else:
        return new_name

# Apply the fuzz_m function to the PETS column and create a new column NEW_PETS
df['NEW_PETS'] = df['PETS'].apply(lambda x: fuzz_m(x, L, fuzz.ratio))

Conclusion

In this article, we demonstrated how to use fuzzy matching to create a new column that contains the best match from a list for each value in a given column. We also discussed common errors and how to correct them.

Fuzzy matching can be useful in various scenarios such as autocomplete suggestions, spell checking, and data cleaning. The fuzzywuzzy library provides an efficient way to perform fuzzy matching using the Levenshtein distance algorithm.

Additional Tips

• Make sure to install the required libraries before running the code.
• Use the apply function with caution, as it can be slow for large datasets.
• Consider using the apply function with a lambda function to avoid creating intermediate data structures.
• Experiment with different scoring functions and parameters to achieve optimal results.

Last modified on 2024-10-01