Omid - Challenge 364

data-challenges
advanced-exercises
🔰 Group IDs Grouping Create all possible product groups such that the sum of prices in each group is equl to 10 or 12.
Published

March 24, 2026

Illustration for Omid - Challenge 364

Challenge Description

🔰 Group IDs Grouping Create all possible product groups such that the sum of prices in each group is equl to 10 or 12.

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/364/CH-364 Custom Grouping.xlsx"
input <- read_excel(path, range = "B3:C8")
test <- read_excel(path, range = "F3:G7")

input_combinations <- map(
  2:nrow(input),
  ~ combn(input$Price, .x, simplify = FALSE)
) %>%
  unlist(recursive = FALSE) %>%
  keep(~ sum(.x) %in% c(10, 13)) %>%
  map(~ paste(LETTERS[.x], collapse = ",")) %>%
  unlist() %>%
  sort()

# one combination is wrong in test table. should be ACDE, not ABDE (ABDE sum is 12)

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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
from itertools import combinations

path = "300-399/364/CH-364 Custom Grouping.xlsx"
df = pd.read_excel(path, usecols="B:C", skiprows=2, nrows=6)
test = pd.read_excel(path, usecols="F:G", skiprows=2, nrows=5)

prices = df['Price'].tolist()

input_combinations = []
for r in range(2, len(prices) + 1):
    for comb in combinations(range(len(prices)), r):
        s = sum(prices[i] for i in comb if pd.notna(prices[i]))
        if s in {10, 13}:
            letters = ",".join(chr(65 + i) for i in comb)
            input_combinations.append(letters)

input_combinations.sort()

# Note: One combination in the test table is incorrect. It should be ACDE, not ABDE (ABDE sum is 12).

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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.