How to Conditionally Create a New Column in a DataFrame Using Python pandas

Learn how to create a new column in a DataFrame based on conditions from another column value using Python's `pandas`. This guide provides step-by-step guidance and example code.
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How to Conditionally Create a New Column in a DataFrame Using Python pandas

Managing data in Python, especially with the pandas library, often involves the necessity to customize and enhance your DataFrame structures. One common task is the conditional creation of a new column based on values from existing ones.

In this guide, we will explore a practical example of this scenario. We have a DataFrame containing clinical data, and we want to add a new column to this DataFrame based on certain conditions applied to other columns.

The Problem

Consider a DataFrame that looks like this:

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

In this DataFrame:

CUI identifies the concept.

CODE represents a code associated with the concept.

SAB indicates the source of the concept, where we want to add a new column MSH_ID derived from the CODE column.

The goal is to populate the MSH_ID column with the value from the CODE column when the SAB column has the value MSH.

The Solution

To achieve this, we can utilize the capabilities of the pandas library. Below are the steps to conditionally add the new column.

Step 1: Basic Setup

First, ensure you have the pandas library installed. You can install it via pip if you haven't already:

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

Step 2: Create the DataFrame

Here’s an example of how to create this DataFrame in Python:

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

Step 3: Creating the MSH_ID Column

To make your MSH_ID column conditional based on the values in SAB, you will first need to find the value of CODE for the rows where SAB is MSH, and then fill this value into the MSH_ID for all corresponding CUI values.

Here’s how you can do this:

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

Step 4: Review the Output

After running the above code, your DataFrame will look like this:

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

Key Takeaways

Conditionally setting values: The ability to conditionally set a new column based on existing values in pandas is invaluable in data manipulation.

Using groupby: Grouping your DataFrame can simplify accessing values based on conditions across rows.

Now you can seamlessly add new columns to enhance your data analysis!

Happy coding!