Rolling Sum with Prior Grouping Values

In this article, we will explore how to calculate a rolling sum with prior grouping values using pandas in Python. This involves taking the last value from each prior grouping when calculating the sum for a specific window.

Introduction

The problem at hand is to create a function that can sum or average data according to specific indexing over a rolling window. The given example illustrates this requirement, where we need to calculate the sum of values in a rolling period, taking into account the last value from each prior grouping level (L0).

Solution Overview

To solve this problem, we will use pandas’ groupby function, which allows us to group data by one or more columns and perform various operations on the grouped data. We will also utilize the rolling_sum function provided by pandas, which calculates the sum of a rolling window.

Step 1: Prepare Data

First, let’s prepare our dataset with the required structure:

import pandas as pd

text = """DateL1   DateL2 Value   Sum 
12/31/2011  1/25/2012   1321    
3/31/2012   4/25/2012   1457    
6/30/2012   7/25/2012   2056    
9/30/2012   10/26/2012  3461    8295
12/31/2012  1/24/2013   2317    9291
3/31/2013   4/24/2013   2008    9842
6/30/2013   7/24/2013   1885    9671
6/30/2013   7/27/2013   1600    9386
9/30/2013   10/29/2013  1955    7880
9/30/2013   11/01/2013  1400    7325
12/31/2013  1/28/2014   1985    6993
12/31/2013  1/30/2014   1985    6993
3/31/2014   4/24/2014   1382    6367
3/31/2014   4/25/2014   1200    6185
6/30/2014   7/23/2014   2378    6963
9/30/2014   10/21/2014  3826    9389
3/31/2015   4/28/2015   2369    9773
3/31/2015   4/30/2015   2369    9773"""

from io import BytesIO

df = pd.read_csv(BytesIO(text), delim_whitespace=True, parse_dates=[0], index_col=0)

Step 2: Calculate Rolling Sum with Prior Grouping Values

Next, we can calculate the rolling sum using groupby and last functions:

s1 = pd.rolling_sum(df.groupby(df.index, sort=False).Value.last(), 4)

However, this approach is slow because it calculates the last value for each group individually.

Step 3: Improve Performance Using Transform

A more efficient way to calculate the rolling sum with prior grouping values is by using the transform function:

def f(s):
    return s - s.iat[-1]

s2 = df.groupby(df.index, sort=False).Value.transform(f).fillna(0)

This approach uses groupby and transform, which are faster than using rolling_sum. The transform function applies the specified function to each group in a way that returns an array of transformed values.

Step 4: Combine Results

Finally, we can combine the results from s1 and s2:

print s1 + s2

This will return the total sum for each window, taking into account the last value from each prior grouping level.

The final answer is:

Note: The final answer is not a simple number but rather an array of values representing the rolling sum with prior grouping values.

Last modified on 2023-07-11