How to Merge Columns in a Dataframe Using Pandas

Описание: Discover a simple solution to merge columns in a Pandas dataframe, ensuring default values are retained unless specified otherwise.
---
This video is based on the question https://stackoverflow.com/q/62291535/ asked by the user 'Russell Comer' ( https://stackoverflow.com/u/8560481/ ) and on the answer https://stackoverflow.com/a/62294086/ provided by the user 'Caleb Courtney' ( https://stackoverflow.com/u/6480402/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Merging columns within a dataframe with pandas

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Merging Columns in a Dataframe with Pandas: A Simple Guide

When working with data, it's common to encounter situations where you need to merge columns within a single dataframe. A frequent scenario involves retaining the default value from one column unless it is empty, in which case, you would like to use the value from another column. This guide explains how you can efficiently accomplish this using the Pandas library in Python.

The Problem Explained

Imagine you have a dataframe with two columns, A and B, structured like this:

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

You want to create a new column that retains the values from column A but replaces them with values from column B whenever they are empty (i.e., NaN). The desired result of merging these columns is as follows:

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

The Solution

To achieve this, you can utilize the fillna() method from the Pandas library, which fills the NaN values in the specified column with the values from another column. Here's how you can do it step-by-step:

Step 1: Import the Pandas Library

First, you'll need to import the Pandas library in your Python script:

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

Step 2: Create the Dataframe

Next, create your dataframe with the columns A and B populated with the appropriate data:

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

Step 3: Merge the Columns

Now, you can merge the columns by ensuring that you fill in the NaN values in column A using the values from column B:

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

Step 4: View the Result

Lastly, to confirm that the operation was successful, you can display the updated dataframe:

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

The output will now look like this:

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

Conclusion

Merging columns in a dataframe can be straightforward when you utilize Pandas effectively. By following these simple steps, you can ensure that your data is clean and properly formatted, with default values retained unless specified otherwise. This approach not only helps maintain data integrity but also makes data analysis more accurate and efficient.

If you have any further questions or need additional assistance with Pandas, feel free to explore more resources or ask for help!