How Can I Efficiently Filter a Pandas Series for Multiple Substrings?

Efficient Pandas Filtering for Multiple Substrings in a Series

Determining whether a series contains any of several substrings is a common task in data analysis. While using logical or to combine individual str.contains operations offers a straightforward solution, it can be inefficient for long substrings lists and large dataframes.

To optimize this task, consider adopting a regular expression (regex) approach. By wrapping the substrings in a regex pattern, we can leverage pandas' efficient string matching functions. Specifically, after escaping any special characters in the substrings, we can construct a regex pattern by joining the substrings using the pipe character (|):

import re

esc_lst = [re.escape(s) for s in lst]
pattern = '|'.join(esc_lst)

With this pattern, we can filter the series using str.contains and case-insensitive matching:

df[col].str.contains(pattern, case=False)

This approach offers improved performance, especially for large dataframes. Consider the following example:

from random import randint, seed

seed(321)

# 100 substrings of 5 characters
lst = [''.join([chr(randint(0, 256)) for _ in range(5)]) for _ in range(100)]

# 50000 strings of 20 characters
strings = [''.join([chr(randint(0, 256)) for _ in range(20)]) for _ in range(50000)]

col = pd.Series(strings)
esc_lst = [re.escape(s) for s in lst]
pattern = '|'.join(esc_lst)

Using this optimized approach, the filtering operation takes approximately 1 second for 50,000 rows and 100 substrings, significantly faster than the method described in the original question. The performance difference becomes even more pronounced for larger dataframes and substrings lists.

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