Скачать или смотреть How to Merge Two Columns in Pandas When One Contains NaN Values

Learn how to efficiently merge two columns in a Pandas DataFrame, while handling NaN values seamlessly. This step-by-step guide will help you achieve the desired output with ease.
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This video is based on the question https://stackoverflow.com/q/69799253/ asked by the user 'Katty_one' ( https://stackoverflow.com/u/17228186/ ) and on the answer https://stackoverflow.com/a/69799266/ provided by the user 'Bruno Mello' ( https://stackoverflow.com/u/10260243/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Merging Two Columns When Column1 is NaN

When working with data in Pandas, it's common to encounter missing values represented as NaN (Not a Number). This can pose a challenge when you want to consolidate two data columns, particularly if one of those columns contains NaN values. If you find yourself needing to merge two columns into one, while bringing values from a secondary column into the main one where it's missing, you've come to the right place!

Problem Breakdown

Let's take a look at a simple example to understand this better. Suppose you have a DataFrame that looks like this:

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In this example, we want to create a new column called Value1 that fills in the gaps of value1 with corresponding values from value2 whenever value1 is NaN. Our expected output should look like this:

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Solution Overview

To achieve this, you can follow these simple steps in Python using the Pandas library, which is an essential tool for data manipulation. Here’s a straightforward method to merge the two columns according to the conditions outlined.

Steps to Merge Columns

Import Pandas Library: Ensure that you have Pandas installed in your Python environment.

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Create DataFrame: Set up the initial DataFrame with your data.

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Your DataFrame now contains the sample data as follows:

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Merge Values with Handling NaNs: Here’s the crux of the solution: You assign value1 to Value1, and then replace NaNs in Value1 with values from value2.

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Check Your Output: Finally, you can print the DataFrame to verify that the operation was successful.

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This should yield the desired output:

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Conclusion

By following the above method, you can conveniently merge two columns in your Pandas DataFrame, filling missing values as needed. This technique is particularly useful in data analysis workflows where maintaining complete datasets is crucial.

Next time you encounter NaN values while working with your data, remember this straightforward approach to clean and transform your DataFrame effectively!