String Matching with Partial Strings in Pandas

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Introduction

Pandas, a powerful library for data manipulation and analysis, can be used to efficiently handle various types of data. One common task in pandas is string matching, where you want to check if a value in one column exists in another column. However, what if you’re dealing with partial strings? In this article, we’ll explore how to achieve this using pandas.

Problem Description

The original question presents a scenario where we have two columns: col1 and col2. The values in col2 are strings that may or may not exist in col1, which contains country names. We want to create a new column, compare, that indicates whether each value in col2 is present in col1. However, instead of using pandas’ built-in string matching functions, we need a custom solution.

Desired Outcome

The desired outcome is to have the values from col1 associated with each match in col2, rather than just indicating the presence or absence of a match. This requires us to rethink our approach and leverage some advanced techniques for string matching.

A “Pandas-Less” Approach

The Problem with Built-in Functions

The question mentions that using pandas’ built-in functions, such as str.contains(), doesn’t quite solve the problem because it returns a boolean value indicating whether any part of the partial string exists in the column. This approach does not provide the desired association between matching values.

A Custom Solution: Levenshtein Distance

To tackle this challenge, we can use the Levenshtein distance algorithm, which measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into another. This approach allows us to find partial matches and compute the distance between strings.

Defining the compare_cols Function

def compare_cols(match_col, partial_col):
    series = []
    for partial_str in partial_col:
        min_distance = float('inf')
        match_str = None
        for match_str in match_col:
            # Calculate Levenshtein distance
            dist = levenshtein_distance(partial_str, match_str)
            if dist < min_distance:
                min_distance = dist
                match_str = match_str
        series.append(match_str if min_distance <= 2 else None)  # Adjusted threshold to consider matches of length >1
    return series

The levenshtein_distance Function

def levenshtein_distance(a, b):
    m, n = len(a), len(b)
    dp = [[0] * (n + 1) for _ in range(m + 1)]
    
    # Initialize first row and column
    for i in range(m + 1):
        dp[i][0] = i
    for j in range(n + 1):
        dp[0][j] = j
    
    # Fill the rest of the table using the recurrence relation
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i][j] = min(dp[i - 1][j] + 1,      # Deletion
                           dp[i][j - 1] + 1,      # Insertion
                           dp[i - 1][j - 1] + cost)  # Substitution or no operation
    
    return dp[m][n]

Applying the compare_cols Function

df = pd.DataFrame([['Mexico', 'Chile'], ['Nicaragua', 'Nica'], ['Colombia', 'Mex']], columns=["col1", "col2"])

# Create a new column with matching values using the custom function
df["match"] = compare_cols(df["col1"], df["col2"])

Conclusion

In this article, we explored how to achieve string matching with partial strings in pandas. We discussed the challenges of using built-in functions and introduced a custom solution based on the Levenshtein distance algorithm. By adjusting the threshold for match length, we can consider matches of length greater than 1, effectively associating the value from col1 with each match in col2. This approach provides a robust way to handle partial string matching in pandas dataframes.

Additional Considerations

While this solution addresses the specific challenge presented in the question, it’s essential to consider additional aspects when working with strings and partial matches:

• String normalization: Normalizing strings by converting them to lowercase or removing special characters can improve match accuracy.
• Regular expressions: Regular expressions (regex) offer a powerful way to perform complex string matching. However, they may require more advanced knowledge of regex patterns.
• Distance metrics: The Levenshtein distance is just one of many distance metrics used for string similarity. Other metrics, such as Jaro-Winkler or Soundex, might be more suitable depending on the specific application.

By considering these additional aspects and selecting the appropriate approach, you can develop robust solutions for handling partial strings in pandas dataframes.

Last modified on 2023-09-22