How to Merge Two DataFrames in Pandas Without Duplicate Columns

Learn how to merge two pandas DataFrames while keeping the existing data and avoiding duplicate column names. This guide covers everything you need to know!
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How to Merge Two DataFrames in Pandas Without Duplicate Columns

Merging two DataFrames in Python's Pandas library can be a common task, especially when dealing with clean data representations. However, complications can arise when both DataFrames have overlapping column names. One such situation involves merging two DataFrames where the left DataFrame (df) contains duplicate columns relative to the right DataFrame (df2). In this post, we’ll walk through a specific case and how to achieve the desired output without generating unnecessary duplicates in column names.

The Problem Scenario

Suppose we have the following two DataFrames:

Left DataFrame (df)

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

Right DataFrame (df2)

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The goal is to merge these two DataFrames such that for matching values of ser and no, the values in df2 overwrite those in df without creating duplicate columns (like c_x and c_y). The desired output would look like this:

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

The Solution Explained

To achieve the desired result, we can follow these steps:

Step 1: Merge DataFrames Selectively

Instead of merging all columns, we first merge only the ser and no columns from the left DataFrame (df) with the entire right DataFrame (df2). This prevents the creation of duplicate column names.

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Step 2: Align the Index

The merge operation can alter the index of the DataFrame. To ensure that the resulting merged DataFrame retains the original index of df, we use the set_axis method:

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Step 3: Fill Missing Values with Existing Data

The next critical step is to replace any missing values resulting from the merge with the original data from df. We can achieve this using the fillna method:

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

Complete Code Example

Here’s the full code that incorporates all these steps into a single block for clarity:

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

When you run this code, you will get the desired DataFrame without duplicate columns, where the values from df2 successfully overwrite the corresponding values in df for matched keys.

Conclusion

Merging DataFrames in pandas can be straightforward once you understand the techniques to manage overlapping column names. By selectively merging columns, aligning indices, and filling in missing data, you can efficiently manipulate your DataFrames to achieve the intended structure.

We hope this guide provides clarity and helps streamline your data manipulation tasks in Python’s Pandas library!

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