How Can I Efficiently Filter a Pandas DataFrame for Multiple Substrings, Handling Case and Special Characters?

Efficiently Filtering Pandas Dataframes for Multiple Substrings

Filtering dataframes for substrings is a common task, but it can become computationally expensive with large datasets. The challenge is further compounded when dealing with unusual characters and case-insensitive matches.

Problem:

Given a Pandas dataframe with a string column, efficiently filter rows such that the column contains at least one of a list of substrings, regardless of case and special character presence.

Inefficient Approach:

The initial approach involved iterating over each substring in the list and applying the str.contains() method with the regex=False and case=False flags. While this approach is straightforward, it can be slow for large datasets.

Efficient Approach:

A more efficient solution utilizes regular expressions to construct a pattern containing all the escaped substrings joined by a regex pipe |. This pattern is then checked against each string in the column using the str.contains() method.

import re

lst = ['kdSj;af-!?', 'aBC+dsfa?\-', 'sdKaJg|dksaf-*']
esc_lst = [re.escape(s) for s in lst]
pattern = '|'.join(esc_lst)
df[col].str.contains(pattern, case=False)

This approach performs significantly faster than the iterative one, especially for large datasets and substrings that require escaping.

Performance Evaluation:

Using a dataset with 50,000 strings and 100 substrings, the proposed method takes approximately 1 second to complete, while the iterative method takes about 5 seconds. The timing further improves if any of the substrings match the column values.

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