How to Implement a Dynamic Search Over Multiple Rows in Pandas DataFrames

Discover how to effectively implement search functionality over multiple rows in Pandas DataFrames, using a dynamic approach to categorize issues based on descriptions.
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How to Implement a Dynamic Search Over Multiple Rows in Pandas DataFrames

In today's guide, we tackle a common problem faced by data analysts and engineers: how to implement a dynamic search function over multiple rows in a DataFrame using Python's Pandas library. You might find yourself needing to categorize text data based on specified keywords or phrases. Let's break it down step by step.

The Problem

Imagine you have a DataFrame that consists of various resolutions, each represented by columns that contain different descriptions. In many cases, these descriptions will reference issues that need to be categorized under relevant labels. A typical example might look like this:

Issue TypeA/RES/73/262A/RES/73/263Issue-PrimaryMEHRIssue-SecondaryNaNNaNDescriptionProtection of the Palestinian civilianSituation of human rights in MyanmarThe challenge arises when you need to categorize both the "Issue-Primary" and "Issue-Secondary" based on the same descriptions. The key point is that the second search must exclude the category already assigned to the primary issue to avoid duplications.

The Solution Overview

To solve this problem, we can leverage regular expressions and the powerful capabilities of the Pandas library to dynamically categorize various issues. We will follow this process:

Initialize the DataFrame and Issue Dictionary: Setup your data structure, including a dictionary that maps keywords to respective issues.

Dynamic Search Setup: Use regular expressions to search for all keywords naturally.

Categorization Logic: Ensure that the search for secondary issues does not include keywords identified in the primary issue.

Now, let's take a closer look at the individual steps involved.

Step 1: Initialize the DataFrame and Issue Dictionary

First, we need to import the necessary libraries and set up our DataFrame along with an issue dictionary that maps search terms to issue codes.

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

Step 2: Dynamic Search Setup

Next, we will use a compiled regular expression pattern to search for keywords in each description column. Here’s how you can do it:

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

Step 3: Categorization Logic

In this step, we will collect unique matched issues and update both "Issue-Primary" and "Issue-Secondary". The key is to manage the outputs effectively to prevent duplicate categorizations.

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

Complete Example Code

Here’s the full implementation:

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

Conclusion

By following the presented steps, you will develop a flexible and robust data processing solution that allows for dynamic categorization based on text descriptions in your DataFrames. This approach not only enhances your data management skills but also enables better insights and organization of textual data.

Feel free to implement this code in your projects and adapt it to meet your specific needs!