Omid - Challenge 362

data-challenges
advanced-exercises
🔰 Grouping In the question table, calculate total sales per customer, but group all customers that appear only once into a single category named ‘Other’
Published

March 24, 2026

Illustration for Omid - Challenge 362

Challenge Description

🔰 Grouping In the question table, calculate total sales per customer, but group all customers that appear only once into a single category named ‘Other’

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/362/CH-362 Custom Grouping.xlsx"
input <- read_excel(path, range = "B3:E10")
test <- read_excel(path, range = "H3:I6")

result = input %>%
  mutate(n = n(), .by = `Customer ID`) %>%
  mutate(IDs = ifelse(n == 1, "Other", `Customer ID`)) %>%
  summarise(Sales = sum(`Total Sales`), .by = IDs)

all.equal(result, test)
# [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

path = "300-399/362/CH-362 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:E", skiprows=2, nrows=8)
test = pd.read_excel(path, usecols="H:I", skiprows=2, nrows=3)

input['Customer ID'] = input['Customer ID'].where(input['Customer ID'].duplicated(keep=False), 'Other')
total_sales = input.groupby('Customer ID', as_index=False)['Total Sales'].sum()
total_sales.columns = ['IDs', 'Sales']

print(total_sales.equals(test))
# True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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.