How to Create a New Column in Pandas DataFrame Using a Defined Function: An In-depth Guide

Discover the efficient ways to create a new column in your Pandas DataFrame using a defined function to calculate percentiles.
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Creating a New Column in a Pandas DataFrame Using a Defined Function

When working with data in Python, particularly with Pandas, one common task is to manipulate DataFrames to extract insights. In this guide, we will tackle an interesting problem: how to create a new column in a DataFrame that displays a range of percentiles for multiple columns. Let’s dive in!

The Problem Statement

Assume you have a DataFrame df that looks like this:

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

You want to calculate the percentiles ranging from the 25th to the 50th percentile in increments of 5 for each of the columns in the DataFrame. The desired outcome would look like this:

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

You might think about looping through rows and calculating the values discretely, but there’s a more efficient way to achieve this—let’s explore it!

Step-by-step Solution

1. Modify the get_percentile Function

First, we need to adjust the get_percentile function to work effectively with a Pandas Series, which represents a single column in our DataFrame.

Here’s the modified version:

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

Now, the function takes in a Series (a column of the DataFrame) along with the desired percentile value. It sorts the values in the Series and retrieves the corresponding percentile based on the index calculated.

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

3. The Output

This will give us the resulting DataFrame with the expected percentiles displayed nicely:

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

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

Built-in Functionality: Utilizes Pandas' optimized methods, ensuring efficiency.

Less Code: Simplifies your code and reduces the chance of errors.

Direct Value Retrieval: Eliminates the need for custom sorting and index calculations.

Conclusion

With these techniques, you can easily create a new column in your Pandas DataFrame that showcases percentiles for various columns. Whether you choose to modify a custom function or utilize Pandas’ built-in methods, the key is to structure your approach effectively.

Feel free to experiment with different datasets and percentiles to enrich your data analysis practice! Happy coding!