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

Testing Substring Presence in Strings Using Pandas DataFrame

When working with string data in Python's Pandas library, you may encounter the need to determine if a string contains any substring from a given list. While there are various functions that check for substring presence, such as df.isin() and df[col].str.contains(), using them in combination can be somewhat complex.

Suppose we have a Pandas Series s containing strings like "cat," "hat," "dog," "fog," and "pet," and we want to identify all strings that include either "og" or "at."

One solution is to employ a regex pattern that matches any substring in the list using the "|" character. For instance, by joining the substrings in searchfor using "|," we create a regex:

>>> searchfor = ['og', 'at']
>>> regex_pattern = '|'.join(searchfor)
>>> s[s.str.contains(regex_pattern)]
0    cat
1    hat
2    dog
3    fog
dtype: object

This approach effectively finds all strings in s that contain either "og" or "at." It is a concise and efficient method.

However, if the substrings in searchfor contain special characters like "$" or "^," it is crucial to escape them using re.escape() to ensure literal matching. For example:

>>> import re
>>> matches = ['$money', 'x^y']
>>> safe_matches = [re.escape(m) for m in matches]
>>> regex_pattern = '|'.join(safe_matches)
>>> s[s.str.contains(regex_pattern)]
0    cat
1    hat
2    dog
3    fog
dtype: object

By escaping the special characters, we ensure that they match each character literally when used with str.contains. This approach provides a robust solution for substring detection in Pandas Series.

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