How Can I Efficiently Filter Pandas Rows Based on Multiple Substrings, Including Special Characters?

Pandas: Efficiently Filtering Rows for Multiple Substrings

Filtering pandas dataframes based on multiple substrings can be challenging, especially when the substrings contain unusual characters. This article provides an efficient solution using a combination of regex and pandas' string matching functions.

The provided list of substrings (lst) has elements with both regular and special characters. To match them literally, we can escape these special characters using re.escape and join them using a regex pipe (|).

Now, we can efficiently check each row of the target column (col) against the pattern using str.contains:

This approach significantly outperforms the original solution, which used nested loops and multiple str.contains calls.

Performance Comparison

Using a dataset with 50,000 strings of 20 characters and 100 substrings of 5 characters, the proposed method takes approximately 1 second:

In comparison, the original approach took approximately 5 seconds on the same dataset.

Note: The timings represent worst-case scenarios where there were no matches. The proposed method will perform even better when there are matches, as it will stop checking substrings once a match is found.

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