Omid - Challenge 373

data-challenges
advanced-exercises
🔰 Group 1 Group 2 Group 3 Group 4 Group 5 Grouping Starting from the top, group one or two rows at a time so that the sum of each group falls between 600 and 1200.
Published

March 24, 2026

Illustration for Omid - Challenge 373

Challenge Description

🔰 Group 1 Group 2 Group 3 Group 4 Group 5 Grouping Starting from the top, group one or two rows at a time so that the sum of each group falls between 600 and 1200.

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/373/CH-373 Custom Grouping.xlsx"
input <- read_excel(path, range = "B3:E11")
test <- read_excel(path, range = "H3:I8")

lower <- 600
upper <- 1200

result = input %>%
  mutate(
    s = `Total Sales`,
    small = s < lower,
    start = !lag(small, default = FALSE),
    grp = cumsum(start)
  ) %>%
  summarise(`Total Sales` = sum(s), .by = grp) %>%
  mutate(IDs = paste("Group", row_number())) %>%
  select(IDs, `Total Sales`)

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 = "300-399/373/CH-373 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:E", skiprows=2, nrows=9)
test = pd.read_excel(path, usecols="H:I", skiprows=2, nrows=5).rename(columns=lambda col: col.replace(".1", ""))

lower = 600
upper = 1200

input['s'] = input['Total Sales']
input['small'] = input['s'] < lower
input['start'] = ~input['small'].shift(fill_value=False)
input['grp'] = (~(input['Total Sales'] < lower).shift(fill_value=False)).cumsum()
result = input.groupby('grp', as_index=False)['Total Sales'].sum()
result['IDs'] = [f"Group {i}" for i in range(1, len(result) + 1)]
result = result[['IDs', 'Total Sales']]

print(result.equals(test))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Applies the rule iteratively until the output stabilizes

  • 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.