Скачать или смотреть How to Compare Rows in a Pandas DataFrame and Retrieve Values on Change

Discover how to compare rows in a Pandas DataFrame using a for loop and get specific column values when changes occur. Learn the step-by-step process to achieve the desired output.
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Comparing Rows in Pandas: How to Retrieve Values on Change

When working with data in Python, especially using the Pandas library, it’s common to encounter scenarios where you need to compare values between rows. This can become particularly complex if you’re also looking to extract specific information when certain conditions are met, such as when paths change in a dataset. In this post, we’ll guide you through a straightforward method to compare rows in a Pandas DataFrame and retrieve values accordingly.

The Problem: Comparing Rows

Suppose you have a DataFrame that includes file paths, names, and timestamps, similar to the following structure:

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

You want to compare each row with the next row and, when the full path changes while the name remains the same, extract the corresponding time and the folder to which it has moved.

Desired Output

Given the condition stated above, your output should look like this:

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

The Solution: Step-by-Step Guide

Initialization: Begin by determining where the full path has changed compared to the previous row, while also ensuring the name remains the same.

Logic Implementation: Utilize a combination of boolean indexing and string manipulation to extract the necessary information.

Step-by-step Code

Here’s how to implement this in code:

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

Explanation of the Code

m1: Checks if the current full path is not equal to the previous row's full path.

m2: Ensures that the name is the same as the previous row's name.

folder: Extracts the deepest folder name from the full path.

time_when_path_changed: Uses the np.where function to concatenate the time and folder information only when both conditions (m1 and m2) are true; otherwise, it returns an empty string.

Conclusion

By following this guide, you’ll be able to effectively compare rows in your Pandas DataFrame and retrieve specific column values on changes. This approach is not just efficient but also easy to implement, allowing you to handle similar scenarios with ease in your data analysis tasks. Happy coding!