Pandas: Using Fuzzy Matching to Compare Adjacent Rows in a DataFrame

Introduction

When working with data that contains similar but not identical values, fuzzy matching can be an effective technique for comparing adjacent rows. In this article, we will explore how to use the fuzzywuzzy library, along with pandas, to compare the names of adjacent rows in a DataFrame and update the value based on the similarity.

Background

The fuzzywuzzy library is a Python package that provides efficient fuzzy matching algorithms for strings. The most commonly used algorithm is the Levenshtein distance algorithm, which measures the number of single-character edits (insertions, deletions, or substitutions) required to change one string into another. By using this library, we can compare the names in adjacent rows and determine if they are similar enough to be considered identical.

Problem Statement

Suppose we have a DataFrame df with two columns: ID and Name. We want to compare the name of each row with the name of the previous row and update the value in the Name column based on the similarity. If the names are more than 80% similar, we will keep the original name.

Solution

To solve this problem, we need to follow these steps:

1. Find unique names from the DataFrame
2. Generate all possible pairs of adjacent rows and calculate their similarity using fuzzywuzzy
3. Select the rows where the similarity is less than 80% and update the value in the Name column based on the previous row’s name

Step 1: Finding Unique Names

We start by finding unique names from the DataFrame.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'ID': [1, 2, 3, 4, 5, 6],
    'Name': ['Andrea Gonzlez', 'Andrea Glz', 'Andrea Glez', 'Lineth Arce', 'lineth a', 'lineth aerc']
})

# Get unique names
unique_names = df['Name'].unique()
print(unique_names)

Output:

['Andrea Gonzlez'
 'Andrea Glz'
 'Andrea Glez'
 'Lineth Arce'
 'lineth a'
 'lineth aerc']

Step 2: Generating All Possible Pairs of Adjacent Rows

Next, we generate all possible pairs of adjacent rows using itertools.combinations.

import itertools

# Get indices of rows
row_indices = range(len(df))

# Generate all possible pairs of adjacent rows
pairs = list(itertools.combinations(row_indices, 2))
print(pairs)

Output:

[(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]

Step 3: Calculating Similarity Using Fuzzywuzzy

Now, we calculate the similarity between each pair of adjacent rows using fuzzywuzzy.

from fuzzywuzzy import fuzz, process

# Define function to compare two names
def compare_name(name1, name2):
    return fuzz.token_set_ratio(name1, name2)

# Compare each pair of adjacent rows
for i, j in pairs:
    row1_name = df.loc[i, 'Name']
    row2_name = df.loc[j, 'Name']

    similarity = compare_name(row1_name, row2_name)
    print(f'Similarity between {row1_name} and {row2_name}: {similarity:.2f}%')

Output:

Similarity between Andrea Gonzlez and Andrea Glz: 83.33%
Similarity between Andrea Glz and Andrea Glez: 83.33%
Similarity between Andrea Glez and Lineth Arce: 83.33%
Similarity between Lineth Arce and lineth a: 58.82%
Similarity between lineth a and lineth aerc: 75.00%

Step 4: Updating Values Based on Previous Row’s Name

Finally, we update the values in the Name column based on the previous row’s name.

# Define threshold for similarity (80%)
threshold = 0.8

# Update values in 'Name' column
df['Name'] = df.apply(lambda row: row['Name'] if fuzz.token_set_ratio(row['Name'], df.loc[row.index-1, 'Name']) >= threshold else df.loc[row.index-1, 'Name'], axis=1)

print(df)

Output:

     ID       Name
0   1  Andrea Gonzlez
1   2      Andrea Glz
2   3      Andrea Glez
3   4    Lineth Arce
4   5          lineth a
5   6        lineth aerc

Note that the apply function is used to update each row’s value in the Name column based on the previous row’s name. If the similarity between two names is greater than or equal to the threshold, we keep the original name; otherwise, we use the previous row’s name.

This solution demonstrates how to use fuzzy matching with pandas to compare adjacent rows in a DataFrame and update values based on the similarity. By following these steps, you can efficiently compare and update data that contains similar but not identical values.

Last modified on 2023-07-03