Скачать или смотреть Mastering Pandas groupby with Complex Aggregations for Multiple Columns

Learn how to effectively use `Pandas groupby` to find product combinations, their frequency, and aggregate revenue across transactions.
---
This video is based on the question https://stackoverflow.com/q/73530780/ asked by the user 'Iñaki Baglivo' ( https://stackoverflow.com/u/19375614/ ) and on the answer https://stackoverflow.com/a/73534637/ provided by the user 'SomeDude' ( https://stackoverflow.com/u/1410303/ ) 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 groupby with complex aggregations for multiple columns

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.
---
Mastering Pandas groupby with Complex Aggregations for Multiple Columns

In the world of data analysis, processing transaction datasets is a common challenge. Consider a scenario where you have a dataset describing various orders with products and associated revenue. Your goal is to analyze combinations of products sold together, their frequency, and total revenue generated. If you've stumbled upon this issue, you're not alone! Let’s explore how to tackle this problem effectively using Pandas in Python.

The Dataset

We start with a DataFrame representing our transactions:

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

The output looks like this:

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

The Objective

From the above dataset, we want to derive a new dataset containing all possible combinations of products sold in the same order, along with the frequency of each combination and the total revenue generated by these combinations. For instance, we want the output to resemble:

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

Solution Breakdown

To achieve this goal, we need to follow these main steps:

Step 1: Create Pairs of Products

We will leverage the itertools.combinations to create pairs of products sold in each transaction:

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

Step 2: Group Pairs and Aggregate Data

Next, we’ll aggregate the product pairs, calculating both their frequency and total revenue.

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

Understanding the Logic

Group by Order_ID and apply the find_pairs function, which returns all possible combinations of products for each Order_ID.

Explode the DataFrame to create one entry for each unique pair.

Group again by these product pairs and compute:

Frequency: The count of how many times each pair appears.

Revenue: The total revenue for each product pair. This utilizes a lambda function to aggregate revenue properly.

Output

If we run the above code, we should see the following output:

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

In this answer, we confirmed that the revenue for (Pear, Banana) is derived correctly from the grouped order ID 003, which sums to $25 (15 for Pear + 10 for Banana), showcasing how intricate aggregations can be handled effectively.

Conclusion

In this post, we delved into the powerful capabilities of Pandas for aggregating data from complex datasets. By harnessing group-by operations and combinations, we can provide insightful analyses of product pairings, helping businesses make informed decisions based on sales data. Whether you're a data scientist or a business analyst, mastering these techniques will undoubtedly enhance your data analysis toolkit.