Calculating Cumulative Sum with Conditions in Pandas Using Numpy

Learn how to calculate a cumulative sum in a pandas DataFrame based on a condition using Numpy in Python, without using loops.
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Mastering Cumulative Sums in Pandas with Conditional Logic

When working with data in Python using Pandas, you might find yourself needing to calculate cumulative sums based on specific conditions. For example, how do you compute the cumulative total of sales while considering whether a certain event occurred (like a sale being successful) and a predefined threshold? In this guide, we will explore a case where both a boolean condition in the DataFrame and a numerical threshold are crucial for our cumulative sum.

The Problem

Imagine you have a DataFrame with sales data that looks like this:

SaleIsSuccessSumSaleExpected10False102True122False21False33True62False21True33False65False115False16Task: We want to calculate the cumulative sum for the Sale column, but only start accumulating when the sum exceeds 5 and the row’s IsSuccess is True. Additionally, we want to avoid using for loops wherever possible for efficiency reasons.

The Solution

We can achieve this through a combination of cumulative sums and boolean indexing. Here’s how to do it step by step:

Step 1: Calculate the Initial Cumulative Sum

The first step is to compute a straightforward cumulative sum for the Sale column using Pandas:

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

Step 2: Identify When to Reset the Cumulative Sum

Next, we will create a condition to evaluate when the cumulative sum exceeds 5 while the IsSuccess column is True. Use the following line of code:

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

This condition returns a boolean Series that indicates when both conditions are satisfied.

Step 3: Adjust the Cumulative Sum Based on the Condition

To adjust the cumulative sum based on the condition defined above, we can create a temporary column to handle the “reset” cases. Here's how you can do this:

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

This code effectively resets the cumulative sum whenever the specified condition is met, ensuring that we maintain the correct cumulative total throughout the DataFrame.

Final Code Example

Here's how the complete solution would look in practice:

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

Output

The printed DataFrame will yield the following SumSale values, aligning perfectly with your expected results:

SaleIsSuccessSumSale10False102True122False21False33True62False21True33False65False115False16Conclusion

By applying these methods, you've successfully computed a cumulative sum in a pandas DataFrame that responds dynamically to defined conditions without resorting to cumbersome loops. This approach not only optimizes performance but also enhances readability in your data manipulation tasks. Happy coding!