How to Iterate a Function Over DataFrame Rows in Python with Pandas

Discover how to effectively use Pandas to compute residual customer purchases through iterative functions and overcome common errors.
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Introduction

If you are working on a customer lifetime value model, you may find yourself needing to analyze data at the individual customer level. In this post, we'll tackle a specific challenge: how to iterate a function over each row of a Pandas DataFrame until a certain condition is met. This process becomes crucial when estimating quantities like the Residual Customer Purchases (RCP) for customers using the lifetimes library.

Let's break down the problem, explore a solution, and help you effectively implement it in your model.

The Problem Setup

You are currently utilizing the lifetimes library, which provides a method called conditional_expected_number_of_purchases_up_to_time. This method predicts the number of expected purchases for customers over a specified timeframe.

Here is your DataFrame structure:

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

Each row represents individual customer data. You want to approximate the total number of remaining purchases over their lifetime, which you defined as Residual Customer Purchases (RCP).

The Approach to Calculate RCP

You have defined two functions:

Incremental RCP Calculation: This function calculates the difference in expected purchases between two time periods.

Total RCP Approximation: This function iterates the incremental RCP until the change falls below a specific tolerance level.

Your initial attempts are producing a runtime error:

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

This error suggests that your conditional statements are not functioning as expected due to operating on a Pandas Series instead of individual scalar values.

The Solution

Step 1: Modify Your Current Functions

To resolve the issue, you need to refactor how you calculate RCP. Instead of attempting to apply the total RCP function directly on the DataFrame, create a new function to process individual rows iteratively. Here’s how you can do that:

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

Step 2: Apply Your New Function

Once you have the iterated_RCP function defined, you can apply this function to each row of your DataFrame. Here’s how to do that:

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

Conclusion

By redefining your total RCP function to iterate over individual rows, you can effectively avoid the pitfalls of ambiguous Series comparisons. This approach allows you to extract meaningful insights about customer purchasing behavior, thereby enhancing your customer lifetime value model.

Now, you're well-equipped to integrate this method into your analysis. Happy coding, and may your customer insights lead to fruitful outcomes!