Скачать или смотреть How to Filter Multiple Columns in Python for Specific Keywords

Discover how to effectively filter multiple columns in Python using a list of keywords with regex and boolean indexing in your data analysis.
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Filtering Multiple Columns with List in Python

Data analysis often requires us to sift through large datasets to extract relevant information. As you learn Python, you may encounter challenges like filtering data in a DataFrame based on specific criteria. In this post, we will tackle the problem of filtering rows in a DataFrame where certain columns contain specific keywords.

The Challenge

Imagine you have an Excel file with multiple columns, structured like this:

AA.1BCabc Insurance123INSUR1.01def INSUR456insurance2.05ghi gift789gift10.05jkl pte ltd101gas1.89From this dataset, you want to filter rows for columns A and B that contain the words "insurance" or "gift". You might initially think of using a standard filtering method for one column, like this:

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

However, what if you want to check multiple columns against multiple keywords? That's where things get a bit tricky.

The Solution

To efficiently filter the DataFrame based on a list of words across multiple columns, you can employ regular expressions (regex) alongside boolean indexing. Here's how you can achieve this:

Step-by-Step Guide

Import Required Libraries:
Begin by importing the necessary libraries. You'll primarily need re for regex operations.

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

Define Your List of Keywords:
Create a list that includes all the keywords you want to filter by.

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

Create a Regex Pattern:
Use map and re.escape to safely escape any special characters in your keywords and then join them into a single regex pattern.

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

Apply Boolean Indexing:
You will use the apply method on the specified columns and check for matches against your regex pattern. The check will return True for rows that contain any of the specified keywords.

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

Filter the DataFrame:
Finally, use the mask to filter your original DataFrame.

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

Example Output

Running the above code snippet would yield a DataFrame df2 that retains only the rows where either column A or B contains the specified keywords:

AA.1BCabc Insurance123INSUR1.01def INSUR456insurance2.05ghi gift789gift10.05Summary

In this guide, we explored how to filter multiple columns in a pandas DataFrame for specific keywords using Python. By leveraging regular expressions and boolean indexing, you can efficiently extract the information you need from your datasets.

Key Points:

Use map(re.escape, lst) to ensure your keywords are safely converted for regex.

The any() function allows you to filter rows based on matches in any of the specified columns. Change this to all() if you need all columns to match.

Feel free to integrate this approach into your own data analysis projects, and happy coding!