Using Previous Row to Calculate Sum of Current Row

Introduction

In this article, we will explore a common problem in data analysis where we need to calculate the cumulative sum of a column based on previous values. We will use Python and its popular pandas library to solve this problem.

Background

When working with data, it’s often necessary to perform calculations that involve previous or next values in a dataset. One such calculation is the cumulative sum, which adds up all the values up to a certain point. In our case, we want to calculate the new column that sums these expenses each month.

The Problem

The problem presented in the Stack Overflow post asks us to create a new column that calculates the sum of expenses for each month. However, the issue arises when trying to use built-in pandas functions like cumsum() and .rolling(). These functions don’t seem to work as expected in this case.

Solution

To solve this problem, we can use the following approach:

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame
data = {'ID': ['134', '134','134','135','135','135'], 
        'Year': [2020, 2020, 2021, 2020, 2020, 2021],
        'Month': [11, 12, 1, 11, 12, 1], 
        'Amount': [-199, -50, 40, -365, -23, 400]}

df = pd.DataFrame(data)

Calculating Cumulative Sum

We can calculate the cumulative sum of each row by using the cumsum() function. However, we need to group the data by ID first and then apply the cumulative sum to each group.

# Group the data by 'ID' and apply cumsum()
df["NewColumn"] = df.groupby("ID")["Amount"].cumsum() + 100

This will give us the desired output with a new column that calculates the sum of expenses for each month.

Expected Output

The expected output would be:

Note that the output is different from the expected output in the Stack Overflow post because we used a cumulative sum instead of an absolute difference.

Conclusion

In this article, we have explored a common problem in data analysis where we need to calculate the cumulative sum of a column based on previous values. We used Python and its popular pandas library to solve this problem by grouping the data by ID and applying the cumulative sum to each group.

Last modified on 2024-07-25