How Can I Efficiently Check if a Pandas Series Contains Any Substring from a Given List?

Testing String Substring Inclusion in Pandas

Seeking an efficient method to determine if a string within a Pandas series contains any of a specified list of substrings? A query regarding this can be seen below:

Original Query:

Is there a pandas function that combines the functionality of df.isin() and df[col].str.contains()? I aim to identify all instances where a series contains any substring from a given list.

Proposed Solution:

One approach suggested in the forum employed a loop and list comprehension to check each substring within the series. However, a more concise and efficient solution exists.

Regex-Based Solution:

Leveraging the regex | operator, one can construct a regex that matches each substring in the given list. This regex can then be used with str.contains to filter the series for desired values.

import re

searchfor = ['og', 'at']
regex = '|'.join(searchfor)
df['matching_column'][df['matching_column'].str.contains(regex)]

This approach is more efficient than the iterative method and effectively achieves the desired result.

Handling Special Characters:

If the substrings contain special characters with regex significance, such as $ or ^, they should be escaped using re.escape() to ensure they are interpreted literally.

The above is the detailed content of How Can I Efficiently Check if a Pandas Series Contains Any Substring from a Given List?. For more information, please follow other related articles on the PHP Chinese website!