Скачать или смотреть How to Drop Duplicates with Priority in Pandas

Discover the step-by-step guide to effectively drop duplicates in your Pandas DataFrame while prioritizing rows with complete data entries.
---
This video is based on the question https://stackoverflow.com/q/69723248/ asked by the user 'Drsaud' ( https://stackoverflow.com/u/11037428/ ) and on the answer https://stackoverflow.com/a/69734264/ provided by the user 'Drsaud' ( https://stackoverflow.com/u/11037428/ ) 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 duplicate with priority in 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 Duplicates with Priority in Pandas

When working with data in Python, particularly with the Pandas library, you may often encounter situations where duplicate entries exist in your DataFrame. However, in many cases, it's not just about removing duplicates; sometimes you want to retain specific rows based on certain criteria. For instance, you may want to keep entries that have complete information while dropping those that do not.

In this guide, we'll explore a common problem: how to drop duplicates by giving priority to certain rows that contain both phone numbers and email addresses in a Pandas DataFrame. Let’s walk through the solution step by step.

Understanding the Problem

Suppose you have a DataFrame structured like this:

IDPhoneEmail00010234+ null0001nulla@ .com00010234+ a@ .comIn this scenario, you want to:

Retain the third entry because it has both a phone number and an email address.

Ensure that if an ID has no complete data (like the absence of both phone and email), one of the entries can still remain.

The ultimate goal is to remove duplicates while prioritizing the presence of both attributes.

The Solution

To solve this problem, we can follow a structured approach by categorizing entries based on the availability of phone and email data, then concatenating these categories before dropping the duplicates.

Step 1: Prepare DataFrames Based on Priority

Filter DataFrames:

Create separate DataFrames based on the availability of phone numbers and email addresses:

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

Step 2: Concatenate DataFrames

Combine the DataFrames:

Start with the DataFrame that has both Phone and Email, then add those that have only one of them:

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

Step 3: Drop Duplicates

Remove Duplicates:

Finally, drop the duplicates based on the ID column, ensuring that the first occurrence is kept:

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

Conclusion

By following these steps, you can effectively manage and manipulate your data to drop duplicates based on specified criteria in Pandas. This method allows you to maintain the integrity of your data, ensuring that you keep the most complete entries.

Whether you're just starting with Python and Pandas or looking to refine your data handling skills, this approach will help you prioritize the rows that matter most in your analysis. Don't hesitate to use this technique in your own projects!