Omid - Challenge 297

data-challenges
advanced-exercises
🔰 Group Challenge 297: Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 297

Challenge Description

🔰 Group Challenge 297: Custom Grouping!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/297/CH-297 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C19")
test  = read_excel(path, range = "G2:H8")

give_triangular = function(x) {
  k = ceiling((sqrt(8 * x + 1) - 1) / 2)
  k * (k + 1) / 2
}
t = give_triangular(nrow(input))
seq = rep(1:t, times = 1:t)[1:nrow(input)] 

result = input %>%
  mutate(Group = seq) %>%
  summarise(`Total Sales` = sum(Sales), .by = Group)

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
import numpy as np

path = "200-299/297/CH-297 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=17)
test = pd.read_excel(path, usecols="G:H", skiprows=1, nrows=6)

def give_triangular(x):
    k = np.ceil((np.sqrt(8 * x + 1) - 1) / 2)
    return k * (k + 1) / 2

t = int(give_triangular(len(input)))
seq = np.repeat(np.arange(1, t + 1), np.arange(1, t + 1))[:len(input)]

input['Group'] = seq
result = input.groupby('Group', as_index=False)['Sales'].sum().rename(columns={'Sales': 'Total Sales'})

print(result.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.