How to Populate a Column in a Pandas DataFrame Based on Substrings

Discover how to add columns in a Pandas DataFrame based on specific substring existence in a text column. Learn to implement this with a step-by-step guide!
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How to Populate a Column in a Pandas DataFrame Based on Substrings

If you're working with data in Python using Pandas, you may encounter a scenario where you need to add new columns to a DataFrame based on the presence of certain substrings within an existing text column. For example, you might want to check if specific food items, like "donut" or "pizza", appear in textual descriptions. This guide will guide you through the process of achieving that in a clear and concise manner.

The Problem

Imagine you have a DataFrame that looks like this:

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

In your DataFrame, you want to derive several new columns that will indicate if specific keywords are present in the ‘text’ column. The goal is to have columns for "donut", "cookie", "penguin", and "pizza" filled with "yes" or "no" depending on whether the substring appears in the text, regardless of case sensitivity.

The Solution

To populate the DataFrame according to the described criteria, we can use the Pandas library in Python. Below are the steps and the corresponding code to accomplish this.

Step 1: Set Up Your DataFrame

First, make sure to import Pandas and set up your initial DataFrame.

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

Step 2: Define the Checking Function

Next, you need to create a function that will determine if the substring exists in the 'text' column for each row.

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

Step 3: Populate New Columns

Using the function defined above, you can now add new columns to your DataFrame for each keyword you want to check.

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

Complete Code

Here's the complete code snippet including all the steps mentioned:

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

Final Result

Once you run the code, your DataFrame will look like this:

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

Conclusion

By following these steps, you can efficiently categorize text within your DataFrame and add new columns based on substring existence seamlessly. This approach is flexible, allowing you to check for any number of keywords while ensuring that the checks are case insensitive. Happy coding!