Text Matching with Partial Matches and Leftover Texts in Pandas DataFrames

In this article, we’ll explore how to match text lists against free-hand text in pandas data frames. We’ll cover the basics of text matching, including partial matches, leftover texts, and provide a step-by-step guide on how to implement this functionality using Python.

Introduction

Text matching is an essential task in natural language processing (NLP) and computer vision applications. When dealing with free-hand text, it can be challenging to accurately match the text against predefined lists or keywords. In this article, we’ll focus on two common use cases:

1. Matching a list of strings against a column of text in a pandas DataFrame.
2. Returning “partial” and “full match” texts along with the leftover texts in another column.

We’ll explore the basics of text matching, provide an example code implementation, and discuss performance considerations.

Text Matching Basics

Text matching involves comparing two or more strings to determine if one string contains a specific pattern, keyword, or phrase from the other. There are different types of text matches:

• Exact Match: The first word must be the same in both strings.
• Partial Match: At least one character must match between the two words.

Creating a Text Matching Function

To implement the text matching functionality, we’ll create a Python function that takes a list of keywords and a string to match against. We’ll use a combination of filtering, partitioning, and string manipulation techniques to achieve this.

Partitioning Strings

One common technique for text matching is to partition the string into substrings based on specific criteria. In our example, we’ll partition the input string by splitting it into individual words.

def partition(string):
    """
    Partitions a string into two lists: one containing matched keywords and the other containing non-matched characters.
    
    Args:
        string (str): The input string to match against the keyword list.
        
    Returns:
        tuple: Two lists, where the first contains the matched words and the second contains the leftover characters.
    """
    # Split the input string into individual words
    words = string.split()
    
    # Define a function to check if a word is part of the keyword list
    def has_keyword(word):
        return any([set(word).issubset(keyword) for keyword in keywords])
    
    # Partition the input string based on the matching criteria
    result = reduce(lambda x, y: (x[0]+[y], x[1]) if has_keyword(y) else (x[0], x[1]+[y]), words, ([], []))
    
    # Return the matched and non-matched substrings as lists
    return [' '.join(sl) for sl in result]

Implementing Text Matching with Pandas

To apply this text matching functionality to a pandas DataFrame, we can utilize the apply method to apply our function to each string value in the “text” column.

import pandas as pd

# Create an example DataFrame with free-hand text columns
df = pd.DataFrame({'text': ['apple aple xyz def', 'pythn jef', 'asdf', 'skjd aple']})

# Define our keyword list (in this case, just two keywords)
keywords = ['apple', 'python']

# Apply the text matching function to each string value in the "text" column
df['Match'] = df['text'].apply(lambda x: partition(x)[0])
df['Not_Match'] = df['text'].apply(lambda x: partition(x)[1])

# Print the resulting DataFrame with matched and non-matched columns
print(df)

Performance Considerations

When dealing with large datasets, text matching can become computationally expensive. To improve performance:

• Optimize Your Keyword List: Use a well-curated keyword list to minimize false positives.
• Use Regular Expressions (regex): If possible, use regex patterns for more efficient matching.
• Utilize Multithreading or Multiprocessing: If your dataset is large enough, consider utilizing multithreading or multiprocessing techniques to speed up the text matching process.

Conclusion

In this article, we’ve explored how to match text lists against free-hand text in pandas DataFrames. We’ve covered the basics of text matching and provided a step-by-step guide on implementing this functionality using Python. By understanding these concepts and adapting our implementation to your specific use case, you can improve the accuracy and efficiency of your text matching operations.

Note that there are many variations of this algorithm and they all need to be adjusted according to needs.

Last modified on 2023-08-20