Omid - Challenge 109

data-challenges
advanced-exercises
🔰 Group Challenge 109: Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 109

Challenge Description

🔰 Group Challenge 109: Custom Grouping!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-109 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C26")
test  = read_excel(path, range = "G2:H10")
  

result = input %>%
  mutate(group = cumsum(Sales < lag(Sales, default = first(Sales))),
         Date = format(Date, "%m/%d/%Y")) %>%
  summarise(range = paste0(min(Date),"-",max(Date)),
            `Total Sales` = sum(Sales),
            .by = group) 

identical(result$`Total Sales`, test$`Total Sales`)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

  • Strengths:

    • The R solution stays close to the workbook rule and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the sheet structure and source ranges remain stable.

  • Gem:

    • The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd
import numpy as np

path = "CH-109 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1)
test  = pd.read_excel(path, usecols="G:H", skiprows=1, nrows = 8)

result = input.assign(group = input['Sales'].lt(input['Sales'].shift().fillna(input['Sales'].iloc[0])).cumsum(),
                      Date = input['Date'].dt.strftime("%m/%d/%Y")).groupby('group').agg(range=('Date', lambda x: f"{x.min()}-{x.max()}"),
                                                                                       Total_Sales=('Sales', 'sum')).reset_index()

print(result["Total_Sales"].equals(test["Total Sales"])) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

  • Strengths:

    • The Python version follows the same rule in a direct dataframe-oriented implementation.

  • Areas for Improvement:

    • The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.

  • Gem:

    • The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • The business rule is readable, but the workbook still requires careful implementation to reach the expected layout.