Omid - Challenge 273

data-challenges
advanced-exercises
🔰 Question Result 2023 2024 2025 Product ID MN-11 MN-12
Published

March 24, 2026

Illustration for Omid - Challenge 273

Challenge Description

🔰 Question Result 2023 2024 2025 Product ID MN-11 MN-12

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/273/CH-273 Advanced Sorting.xlsx"
input = read_excel(path, range = "B2:E9")
test  = read_excel(path, range = "G2:J9")

result = input %>%
  rowwise() %>%
  mutate(highest_value = max(c_across(starts_with("2"))),
         max_col = names(.)[which.max(c_across(starts_with("2")))+1]) %>%
  arrange(desc(highest_value), desc(max_col)) %>%
  select(-c(highest_value, max_col)) 

all.equal(result, test, check.attributes = FALSE) 
# > [1] TRUE

  • 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

path = "200-299/273/CH-273 Advanced Sorting.xlsx"
input = pd.read_excel(path,usecols="B:E", skiprows = 1, nrows=8)
test = pd.read_excel(path, usecols="G:J", skiprows=1, nrows=8).rename(columns=lambda col: str(col).replace('.1', ''))

cols_2 = [col for col in input.columns if str(col).startswith('2')]
input = (
    input.assign(
        RowMax=input[cols_2].max(axis=1),
        RowMaxPos=input[cols_2].idxmax(axis=1).map(lambda col: input.columns.get_loc(col) + 1)
    )
    .sort_values(['RowMax', 'RowMaxPos'], ascending=[False, False])
    .drop(['RowMax', 'RowMaxPos'], axis=1)
    .reset_index(drop=True)
)

print(input.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

    • 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.