How Can I Efficiently Check for Multiple Substrings Within a Pandas DataFrame Column?

Testing Substring Presence in Pandas DataFrame Using Multiple Substrings

In pandas, combining df.isin() and df[col].str.contains() to check if a string contains any substring in a list can be tedious. This article offers an alternative solution using regular expressions and the str.contains() method.

To illustrate, consider a series s containing ['cat','hat','dog','fog','pet']. To find all elements that contain either 'og' or 'at', except 'pet', the following code can be used:

searchfor = ['og', 'at']
jointed_regex = '|'.join(searchfor)
s[s.str.contains(jointed_regex)]

The output will be:

0    cat
1    hat
2    dog
3    fog
dtype: object

By joining the substrings with a '|' character, the str.contains() method can effectively match any of the substrings within the string elements.

Handling Special Characters

Note that when dealing with substrings containing special characters, such as $ or ^, it is necessary to escape them using re.escape(). This ensures that the characters are interpreted literally during the matching process.

For example, if searchfor contains ['money', 'x^y']:

import re
safe_searchfor = [re.escape(m) for m in searchfor]
s[s.str.contains('|'.join(safe_searchfor))]

This code escapes the special characters and ensures accurate matching of the substrings.

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