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Learn how to efficiently combine duplicate columns in a DataFrame using Pandas while ignoring NaN values. This step-by-step guide will simplify your data wrangling tasks.
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How to Combine Two Columns in a DataFrame with Pandas

When working with data, especially in the context of data analysis, it's common to encounter situations where you have duplicate columns with the same name. In the case of a DataFrame in Pandas, this can occur when two columns contain similar information, but one of them has NaN values. In this post, we'll tackle the problem of combining these columns efficiently into a single, clean column.

The Problem

Imagine you have a DataFrame that represents some information, including two ID columns:

Date...IDID12/12/2019...DE1253NaN12/12/2018...eg562rNaN12/12/2021...gse233NaN12/12/2019...NaNwefg3212/11/2010...NaNrte42212/10/2021...NaN3fdes4As seen in the table, it contains two columns with the name ID. The goal is to merge these columns into a single column while excluding all NaN values, thus creating a cleaner output.

The desired output should look like this:

Date...ID12/12/2019...DE125312/12/2018...eg562r12/12/2021...gse23312/12/2019...wefg3212/11/2010...rte42212/10/2021...3fdes4The Solution

To tackle this problem, you can take advantage of the powerful groupby method in Pandas. Here’s how to combine the two columns step-by-step.

Step 1: Grouping Columns

Pandas allows you to group columns by their labels. In this case, since both columns have the same name (ID), you can use the following code snippet:

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

Step 2: Explanation of the Code

df.groupby(level=0, axis=1): This command groups the DataFrame columns based on their names (level 0) along the specified axis (1 for columns).

.first(): This function returns the first non-null value from each group. Thus, for every pair of duplicated columns, it will keep the value from the first column if it exists or the second column's value.

Step 3: Implementing it All Together

Here is a complete example that combines everything we've discussed:

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

Conclusion

With just a simple command using groupby, you can effectively combine duplicate columns in a Pandas DataFrame while ignoring NaN values. This helps simplify your data, making it cleaner and easier to analyze. Try implementing this in your own DataFrames whenever you face the issue of duplicated columns!

Now you're equipped with the knowledge to tackle this common data wrangling task seamlessly.