Скачать или смотреть How to Groupby Column Value and Retain Specific Rows in Pandas DataFrame

Discover how to effectively group and filter values in Pandas DataFrame by retaining specific rows based on conditional checks.
---
This video is based on the question https://stackoverflow.com/q/67066214/ asked by the user 'Alison LT' ( https://stackoverflow.com/u/7237305/ ) and on the answer https://stackoverflow.com/a/67066274/ provided by the user 'Andrej Kesely' ( https://stackoverflow.com/u/10035985/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Groupby column value and keep row based on another column value

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Groupby Column Value and Retain Specific Rows in Pandas DataFrame

When working with large datasets in Python, you often need to manipulate data efficiently. A common task is to remove duplicates based on one column while maintaining particular rows based on the value of another column. In this guide, we’ll explore how to achieve this using the powerful Pandas library. Let's dive into the scenario to learn the solution.

The Problem: Managing DataFrame Rows

Let’s say you have a DataFrame with the following structure:

IDCode01AB00102AB00202BC12302CD57603AB44403CD33204BC43404CD894In this DataFrame, we have IDs that may appear multiple times, each with an associated code. The goal is to deduplicate the ID column while retaining rows based on the Code. Specifically, we want to keep the row where the Code starts with "BC" if it exists. If there’s no "BC" code, we’ll keep the first row associated with that ID.

Desired Output

The resulting DataFrame should look like this:

IDCode01AB00102BC12303AB44404BC434The Solution: Step by Step

Now, let’s break down the solution to achieve this using Pandas.

Step 1: Create a Temporary Column

First, we need to create a temporary column that indicates whether the Code starts with "BC". This will help us sort our DataFrame later.

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

Step 2: Sort the DataFrame

Next, we sort the DataFrame by ID and the newly created temporary column. By sorting in this manner, the rows with "BC" codes will appear before those without when grouped by ID.

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

Step 3: Group and Take the First Entry

Using the groupby function, we can now group by ID and select the first entry of each group. This will automatically select either the row with the "BC" code or the first row if no "BC" code exists.

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

Step 4: Output the Result

Finally, we can print the resulting DataFrame that matches our desired output.

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

This code will produce the following DataFrame:

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

Conclusion

By following the steps outlined above, you can efficiently filter and group your DataFrame in Python. The approach demonstrated here uses sorting, a temporary column, and the groupby function to achieve the desired outcome. With just a few lines of code, you can manipulate your datasets in a way that meets specific requirements. Don't hesitate to leverage these powerful tools in your data manipulation tasks!

Feel free to leave any questions or comments below if you need further clarification on this topic.