How to Aggregate Rows in a DataFrame by Grouping IDs in Pandas

Описание: Discover how to efficiently aggregate rows in a Pandas DataFrame based on ID numbers, and prioritize specific types using Python!
---
This video is based on the question https://stackoverflow.com/q/65676327/ asked by the user 'big_soapy' ( https://stackoverflow.com/u/13123861/ ) and on the answer https://stackoverflow.com/a/65676360/ provided by the user 'perl' ( https://stackoverflow.com/u/6792743/ ) 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: Aggregating rows with same id number and inputting column value based on aggregation

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 Aggregate Rows in a DataFrame by Grouping IDs in Pandas

When working with data, we often encounter situations where we need to aggregate rows based on unique identifiers. In this guide, we'll tackle a common problem: how to aggregate rows in a Pandas DataFrame, specifically focusing on unique ID numbers and prioritizing certain values based on type.

The Problem Statement

Let's consider a scenario where we have the following DataFrame:

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

The DataFrame looks like this:

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

In this DataFrame, we see that ID 1123 has both Red and Black Type, while ID 9788 also has a mix of Red and Black. Our goal is to create a new DataFrame where each ID maps to its type based on the following rules:

If an ID has both Red and Black, it should be recorded as Red.

If an ID only has Black, then it should remain Black.

Expected Output

After processing, the DataFrame should look like this:

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

The Solution

Step 1: Utilizing groupby and max

To achieve our goal, we can leverage the powerful groupby function in Pandas, in conjunction with the max() method. Here's how:

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

Explanation

groupby('ID', as_index=False): This groups the DataFrame by the ID column and ensures that the original index is not retained.

['Type'].max(): This takes the maximum value of the Type column within each group. Since Red is considered greater than Black, IDs with both types will return Red.

Result

When we execute the above code, we get the following output:

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

Step 2: Handling More Types

If your data contains more types and you want to define a specific order for prioritization, you can convert the Type column to an ordered categorical type before performing the aggregation. Here’s how you can do that:

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

Explanation of the Update

We use pd.Categorical to define an order: Black < Green < Blue < Red.

The same groupby operation with max() will then respect this order, giving precedence to Red over Black and other colors in future operations.

Final Thoughts

By leveraging Pandas' grouping capabilities and categorical types, we can efficiently aggregate DataFrame rows based on IDs while prioritizing specific values. This method not only simplifies your DataFrame manipulation tasks but also enhances the clarity and usability of your data.

Feel free to explore more on how to manipulate DataFrames with Pandas as it opens up numerous possibilities for data analysis!