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Learn how to efficiently drop duplicate rows in a Pandas DataFrame where values are switched between two columns. Explore a simple solution with code examples!
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How to Drop Duplicate Rows in Pandas DataFrame with Switched Column Values

When working with data, we often encounter situations where we need to clean up our dataset by removing duplicate entries. A common scenario arises in a Pandas DataFrame where two rows appear identical except for the values in two specific columns being switched. This can be confusing and lead to inaccuracies if not handled properly.

In this guide, we’ll illustrate this problem using a sample DataFrame and show you how to effectively remove these "switched" duplicates using Python’s Pandas library.

The Problem: Identifying Duplicate Rows

Consider the following table (DataFrame):

NUM1NUM2DATE1DATE2USED4710. 9. 200216. 11. 20025,37410. 9. 200216. 11. 20025,391010. 9. 200216. 11. 20023,110910. 9. 200216. 11. 20023,1In this DataFrame, notice that the first two rows and the last two rows are duplicates; however, the values in NUM1 and NUM2 are switched. Dropping rows solely based on the last three columns wouldn't suffice because it would affect other valid records with different NUM1 and NUM2 values.

Desired Outcome

We want to refine our DataFrame to look like this:

NUM1NUM2DATE1DATE2USED4710. 9. 200216. 11. 20025,391010. 9. 200216. 11. 20023,1The Solution: Using frozenset for Unique Identification

A straightforward approach to achieve this is by using the frozenset function. This allows us to treat the values in NUM1 and NUM2 as a set, giving us the ability to identify duplicates regardless of their order.

Here’s how to implement this solution step-by-step:

Step 1: Import the Required Libraries

First, ensure you have pandas installed and import it in your Python script.

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Step 2: Create DataFrame

Next, create your DataFrame using the sample data provided.

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Step 3: Remove Duplicate Rows

Use the apply function alongside frozenset to identify and remove duplicate rows based on the first two columns.

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Step 4: View the Result

After executing the removal, your DataFrame out will contain only unique rows based on your criteria.

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This will give you the desired output:

NUM1NUM2DATE1DATE2USED4710. 9. 200216. 11. 20025,391010. 9. 200216. 11. 20023,1Conclusion

By using frozenset in conjunction with the apply function, we can efficiently eliminate rows with switched duplicate values in specific columns of a Pandas DataFrame. This method not only keeps our data clean but also ensures that we do not mistakenly remove valid data.

We hope this solution helps you deal with similar issues in your datasets! If you have any questions or further challenges, feel free to leave a comment below. Happy coding!