Omid - Challenge 310

data-challenges
advanced-exercises
🔰 Question Result 5,6,7 5,6,9 5,7,9 3,6,4 3,6,7 3,6,9
Published

March 24, 2026

Illustration for Omid - Challenge 310

Challenge Description

🔰 Question Result 5,6,7 5,6,9 5,7,9 3,6,4 3,6,7 3,6,9

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/300-399/310/CH-310 Numbers Combination.xlsx", range = "B1:B10")
df = expand_grid(i1 = input$Question, i2 = input$Question, i3 = input$Question)
ind = input %>% mutate(ind = row_number())

result = df %>%
  left_join(ind, by = c("i1" = "Question")) %>%
  left_join(ind, by = c("i2" = "Question"), suffix = c("", "2")) %>%
  left_join(ind, by = c("i3" = "Question"), suffix = c("", "3")) %>%
  filter(i2 > i1, i3 > i2, ind < ind2, ind2 < ind3) %>%
  transmute(Result = paste(i1, i2, i3, sep = ",")) %>%
  arrange(Result) %>%
  distinct()

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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
from itertools import combinations

input = pd.read_excel("300-399/310/CH-310 Numbers Combination.xlsx", usecols="B", nrows=9)
input = input.rename(columns={input.columns[0]: "Question"})
questions = input["Question"].tolist()

combos = list(combinations(questions, 3))
result = pd.DataFrame(combos, columns=["i1", "i2", "i3"])

result = result[(result["i1"] < result["i2"]) & (result["i2"] < result["i3"])]

result["Result"] = result.apply(lambda row: f"{row['i1']},{row['i2']},{row['i3']}", axis=1)
result = result[["Result"]].sort_values("Result").drop_duplicates().reset_index(drop=True)
print(result)

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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.