Скачать или смотреть How to Select Top 2 Rows by Absolute Value Across Groups in Pandas

Discover how to efficiently select the top entries based on absolute values in a Pandas DataFrame. This step-by-step guide simplifies the process for Python users.
---
This video is based on the question https://stackoverflow.com/q/63267791/ asked by the user 'Zanam' ( https://stackoverflow.com/u/1243255/ ) and on the answer https://stackoverflow.com/a/63267867/ provided by the user 'ansev' ( https://stackoverflow.com/u/11884237/ ) 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: Pandas select top 2 from each group based on absolute 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 Select Top 2 Rows by Absolute Value Across Groups in Pandas

When working with data in Python using Pandas, you might find yourself in a situation where you want to filter a dataset to retrieve specific entries based on certain criteria. A common task is to select the top entries from groups based on absolute values. In this guide, we will walk you through a practical example to demonstrate how to achieve this.

Understanding the Problem

Consider the following DataFrame:

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

Objective

Our goal is to select the top 2 entries from each group defined by Col1 and Col2, based on the absolute values of Val1. We aim to filter the data such that after processing, we will have the following result:

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

Step-by-Step Solution

To achieve this, we can utilize the Pandas library in Python, which provides powerful data manipulation capabilities. Here’s a straightforward approach to solve our problem:

Step 1: Import Pandas

First, ensure you have Pandas installed and import it into your Python environment.

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

Step 2: Create the DataFrame

Next, we will create the DataFrame from the data provided:

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

Step 3: Select the Top 2 Rows

Now, it’s time to filter the DataFrame. We can achieve this by using a combination of groupby(), abs(), and rank(). Here’s the code that accomplishes the task:

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

Explanation of the Code

abs(): This function computes the absolute values of the Val1 column.

groupby(): This method groups the DataFrame by Col1 and Col2 to segment the data accordingly.

rank(): It assigns ranks to the absolute values in descending order (higher absolute values get a lower rank number).

le(2): This method filters the ranks to include only the top 2 entries (ranks less than or equal to 2).

loc[]: Finally, we use loc[] to select the rows from the original DataFrame that meet our criteria.

Result

When you execute the code, you will obtain the desired DataFrame as follows:

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

Conclusion

Selecting the top entries from a Pandas DataFrame based on absolute values can be accomplished easily using built-in methods such as groupby(), abs(), and rank(). This technique is particularly useful in data analysis where you need to focus on significant values within groups. By following the steps outlined above, you can effectively filter your data to gain insights quickly. Happy coding!