Скачать или смотреть How to Groupby Multiple Columns in Pandas and Keep Other Column Values

Learn how to effectively use Pandas to group by multiple columns, sum specific values, and retain first occurrences of other columns in your DataFrame.
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How to Groupby Multiple Columns in Pandas and Keep Other Column Values

When working with large datasets in Pandas, you might encounter a situation where you want to group your data by multiple columns. At the same time, you need to sum specific values while keeping the first occurrence of the remaining columns. Let’s explore how to achieve this efficiently.

The Challenge: Grouping and Summing with Retained Values

Imagine you have a DataFrame with 100 columns. You need to perform a groupby operation on two specific columns while summing the values of two other columns. Furthermore, you want to preserve the first occurrence of data from the other 96 columns.

This situation poses a challenge: how can you aggregate the data properly without losing key information?

Solution Overview

Instead of looking for a complex one-liner solution, breaking down the task into two separate operations and then joining the results is often clearer and more manageable. Here’s how to do this effectively:

Step-by-Step Breakdown

Prepare Your DataFrame: Begin by setting up your initial DataFrame. This example uses sample data for illustration purposes.

Group by the Desired Columns: Utilize the groupby() function to aggregate your data based on the chosen columns.

Aggregate Specifically: Use the agg() function to sum the specified columns, while retaining the first occurrence of other columns like order.

Combine the Results: Finally, concatenate the results to reflect the aggregate sums along with the retained values.

Code Implementation

Here's a minimal example demonstrating the entire process:

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

Output Interpretation

When you run the code above, the output will be:

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

max_speed and height: The sums of the respective columns for each class are displayed.

order: The first occurrence is preserved, ensuring that valuable information isn't lost during the procedure.

Conclusion

By following this structured approach, you can effectively manage the complexity of grouping data while retaining necessary details. This method provides a clear pathway to aggregate specific columns and manage large datasets in a more organized manner.

Feel free to adapt this method to your specific DataFrame and explore how it enhances your data manipulation tasks!