How Can Regular Expressions Optimize Pandas Filtering for Multiple Substrings in a Series?

Pandas Filtering Optimization for Multiple Substrings in Series

Background

Filtering large Pandas dataframes based on multiple substrings in a string column can be a computationally expensive operation. The conventional approach involves applying a mask for each substring and then reducing it using logical operations.

Proposed Approach

To enhance efficiency, we suggest leveraging regular expressions (with escaped special characters) for substring matching. By joining the escaped substrings using a regex pipe (|), we can test each substring against the string until a match is found.

Implementation

import re

# Escape special characters in substrings
esc_lst = [re.escape(s) for s in lst]

# Join escaped substrings using regex pipe
pattern = '|'.join(esc_lst)

# Filter based on concatenated pattern
df[col].str.contains(pattern, case=False)

Performance Considerations

Performance is enhanced by reducing the number of tests required per row. The method checks substrings until a match is found, eliminating unnecessary iterations.

Benchmarking

Using a sample dataframe with 50,000 strings and 100 substrings, the proposed method takes approximately one second, compared to the conventional approach's five seconds. This performance advantage would increase with a larger dataset.

Conclusion

By leveraging regular expressions with escaped special characters, we can efficiently filter Pandas dataframes for multiple substrings, significantly reducing computational overhead.

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