Скачать или смотреть Create a Running Total for Multiple Columns in Python Using Pandas

Learn how to compute a `running total` for multiple columns in a dataset using Python with Pandas. We'll guide you through a step-by-step process for group calculations by ID and date.
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Creating a Running Total for Multiple Columns Using Python

If you're working with datasets in Python, especially using libraries like Pandas, calculating a running total across multiple columns can be a common requirement. This post will guide you through the challenge of summing multiple columns and creating new columns with running totals, all while grouping by unique identifiers such as ID and date.

Understanding the Problem

Let's consider a dataset as follows:

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

Your goal is to create new columns (final, finalt1, and finalt2) that will represent the running totals based on the initial values (start) and the cumulative sums of totals (total, t1, and t2). For instance:

For ID a in row q1, the initial final value is start + total = 50 + 5 = 55.

For ID a in row q2, the running total updates to 55 + 2 = 57.

Desired Output Format:

The revised data needs to look like this after calculations:

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

Step-by-Step Solution

Now, let's dive into solving this problem using the Python Pandas library.

Step 1: Set Up Your Environment

First, ensure you have the necessary libraries installed. You can do this via pip if you haven't already:

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

Step 2: Import Necessary Libraries

Start your Python script or Jupyter notebook with the following imports:

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

Step 3: Create Your DataFrame

You can load your dataset into a Pandas DataFrame. Here’s a simple way to create your example DataFrame:

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

Step 4: Calculate the Running Totals

To generate your new columns, utilize the groupby function available in Pandas. Here’s how to do it:

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

Summary of the Logic

This approach works by:

Using the groupby function to group your data by the id column.

Applying the cumulative sum (cumsum) to the specified columns within each group to create running totals.

Conclusion

The outlined steps effectively help you create a running total across multiple columns while ensuring the data remains grouped by identification. This method not only streamlines your calculations but also preserves the structure required for further analysis.

So whether you’re analyzing sales, tracking inventory, or monitoring performance metrics, the methods discussed here can significantly enhance your data processing tasks in Python.

Now give it a try with your own dataset, and observe how easily you can manipulate and analyze data using Pandas!