Скачать или смотреть How to Drop Certain Values within a Multi-Level Index in Python Pandas

Learn how to effectively drop certain values in a multi-level index dataframe using Python Pandas while keeping only the top performers according to specified criteria.
---
This video is based on the question https://stackoverflow.com/q/63431597/ asked by the user 'bismo' ( https://stackoverflow.com/u/13132728/ ) and on the answer https://stackoverflow.com/a/63431807/ provided by the user 'Bill' ( https://stackoverflow.com/u/1609514/ ) 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: How to drop certain values within a multi-level index python pandas

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 Drop Certain Values within a Multi-Level Index in Python Pandas

If you're working with a dataset that contains player statistics, it's common to need the top performers from groups, such as NFL teams based on their receiving yards. In this guide, we’ll tackle the problem of how to retain only the top four players for each team based on their receiving yards using Python’s Pandas library.

The Problem

Assuming you have a dataframe (df) that looks somewhat like this:

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

You want to keep only the top four players by rec_yards for each team. With this in mind, let’s explore how to achieve that in a structured manner.

Solution Approach

Here’s a simple and effective approach to filter the dataframe, while organizing the index. Follow these steps:

Step 1: Set Up the Multi-Index

To start off, we need to create a multi-index that will include both the team and rec_yards columns. This will allow us to group our data meaningfully. You can do that using the following code:

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

Step 2: Create a Ranking Column

Next, we’ll create a dummy column that contains all 1s to help us calculate the rank of the players within each team.

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

Now we use cumsum() to compute a cumulative sum within each team:

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

Step 3: Filter the Top Players

To maintain only the top n players per team, we can specify n (e.g., n = 4), and then filter our dataframe:

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

Step 4: Rearranging the DataFrame

If needed, you can reset your index and rearrange the resulting dataframe to match your desired format:

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

Sample Output

After executing these steps, you should see the top player records according to yards received for each team. Here’s an example of what the output may look like:

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

Conclusion

In this post, we’ve outlined a straightforward method to drop certain values within a multi-level index in Python Pandas by retaining the top players based on their performance. This method can be adapted for various datasets, so you can refine it according to your needs. Happy coding!