How to Remove Duplicates from Two DataFrames Using Pandas

Learn how to effectively remove duplicate rows from two pandas DataFrames with different columns. This guide will walk you through the process step by step.
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Removing Duplicates from Two DataFrames in Pandas

When working with data in Python, particularly using the Pandas library, you might encounter situations where you need to clean your data by removing duplicate rows based on specific conditions. This challenge becomes more complex when dealing with multiple DataFrames that have different column structures. In this post, we will explore a solution for removing duplicates from a major DataFrame based on the values in another DataFrame.

Understanding the Problem

Let’s say you have two DataFrames:

Major DataFrame (df): This is the main dataset you’re working with, containing multiple columns, including column1, column2, and column3.

Match DataFrame (dfmatch): This DataFrame is smaller and only contains the columns (column1, column2, column3) that you want to use to identify duplicate rows in the major DataFrame.

Your goal is to delete rows from the major DataFrame where the values in column1, column2, and column3 match with the values in the corresponding columns of the match DataFrame. Additionally, you want to retain other columns in the major DataFrame (extra1, extra2) in your final output.

The Challenge

The original code you may have written might not provide the expected results; instead of getting the remaining rows, it only returns column headers. Here’s what you might have used:

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

The Solution

Instead of using pd.concat and drop_duplicates, a more effective method is to use pd.merge() with the indicator option. This method allows you to easily identify which rows exist in both DataFrames and then filter them out. Here’s how to implement this:

Merge the DataFrames: Use pd.merge() to join the major DataFrame with the match DataFrame. This will create a new DataFrame that contains an additional column indicating the origin of each row.

Filter Rows: Use the indicator column created during the merge to filter out rows that exist in both DataFrames.

Drop the Indicator Column: Finally, drop the indicator column to clean up the DataFrame.

Step-by-Step Implementation

Here’s how the solution can be implemented in code:

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

Conclusion

By utilizing pd.merge() with the indicator parameter, you can efficiently remove duplicates based on multiple columns across two DataFrames. This approach is straightforward and ensures that you retain the necessary columns in your final output.

If you encounter any issues or have further questions, feel free to reach out in the comments!