Скачать или смотреть How to Remove Duplicate Rows in a Pandas DataFrame While Keeping the Mean Value of One Column

Learn how to effectively remove duplicate rows in a Pandas DataFrame while calculating the mean value of a specific column. This guide offers step-by-step instructions and code examples to simplify the process.
---
This video is based on the question https://stackoverflow.com/q/73572580/ asked by the user 'Shourov' ( https://stackoverflow.com/u/16631074/ ) and on the answer https://stackoverflow.com/a/73572671/ provided by the user 'RealRageDontQuit' ( https://stackoverflow.com/u/10177402/ ) 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 remove duplicate rows by keeping one mean column in pandas dataframe?

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 Remove Duplicate Rows in a Pandas DataFrame While Keeping the Mean Value of One Column

When working with datasets in Python's Pandas library, it's not uncommon to encounter duplicate rows. This can cause issues, especially if you're interested in performing analyses based on unique entries. One challenge arises when you want to remove duplicates but also retain some aggregated information, such as the mean of a particular column. In this guide, we'll explore how to eliminate duplicate rows in a Pandas DataFrame while keeping the mean value of one of the columns.

Problem Overview

Consider a dataset with three columns (A, B, and C) as shown below:

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

In this dataset, you will notice that:

Rows 0 and 1 have the same values in columns A and B but different values in column C.

Rows 4 and 5 also share the same values in columns A and B with different values in column C.

The goal is to remove the duplicates based on columns A and B and calculate the mean value for column C for those duplicates.

Solution

Step 1: Create the DataFrame

First, let’s create the DataFrame in Python using Pandas. Here’s the code to initialize our example data:

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

Step 2: Group by and Calculate the Mean

To remove duplicates and keep the mean of column C, we need to group by the columns A and B, then calculate the mean of column C. Here’s how you can do it:

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

groupby(['A', 'B']): This groups the DataFrame by columns A and B, treating these combinations as unique keys.

.mean(): This computes the mean value of column C for each unique group.

.reset_index(): This resets the index of the DataFrame to give you a clean index in the resultant DataFrame.

Final Output

After executing the above code, your new DataFrame (df2) will look like this:

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

Conclusion

In just a few lines of code, you can effectively remove duplicates in a Pandas DataFrame while retaining the mean values of a specified column. This method is essential for any data analysis where you need to maintain the integrity of your data while ensuring unique entries.

By following the steps outlined in this guide, you can apply similar techniques to your datasets, enabling clearer insights and more accurate analyses. Happy coding!