Efficiently Filter DataFrames with partial string matching in Python's Pandas

Learn how to filter a DataFrame in Python's Pandas using partial string matching efficiently without requiring exact matches of complete row names.
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How to Filter DataFrames Based on Partial String Matching in Pandas

Working with DataFrames in Python's Pandas can often present various challenges, especially when you're dealing with large datasets containing numerous categories. One common problem you might encounter is the need to filter your DataFrame for rows that contain specific substrings within a column, rather than needing an exact match. In this guide, we'll explore this issue in detail and outline a clear solution to help you achieve your filtering goals efficiently.

The Problem: Filtering DataFrames

Imagine you have a DataFrame that includes a wide range of bank categories, like this:

[[See Video to Reveal this Text or Code Snippet]]

You want to filter this DataFrame to include only certain entries based on partial string matches. For example, you may want to extract rows that include concepts such as 'Совкомбанк' or 'Тинькофф'. However, you don't want to pass the entire row name, which can be tedious and inefficient.

After attempting the method using df[column_name].isin(values), you discover it doesn't yield the results you need because isin() only checks for exact matches. So, what's the best approach?

Step-by-Step Implementation

Define Your Substring Matches: Start by creating a list of keywords that you would like to match. In this case:

[[See Video to Reveal this Text or Code Snippet]]

[[See Video to Reveal this Text or Code Snippet]]

Here, column_name should be replaced with the actual name of the column you are searching through.

The join() function constructs a regex pattern that searches for any of the keywords specified in match_strs.

Example Code

To put it all together, here’s a full example of how you can perform this task in your code:

[[See Video to Reveal this Text or Code Snippet]]

Output

After running the above code, the output will be as expected:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Now, you have a powerful method at your disposal to streamline your DataFrame filtering process in Pandas! Happy coding!