Omid - Challenge 100

data-challenges
advanced-exercises
🔰 Question Result Product ID 100A 100B 107A 107B 107C
Published

March 24, 2026

Illustration for Omid - Challenge 100

Challenge Description

🔰 Question Result Product ID 100A 100B 107A 107B 107C

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-100 Manage Duplicate Values.xlsx"
input = read_excel(path, range = "B2:B15")
test  = read_excel(path, range = "D2:D15")

result = input %>%
  mutate(row = row_number(), 
         n = n(), 
         .by = `Product ID`) %>%
  mutate(letter = ifelse(n > 1, LETTERS[row], "")) %>%
  select(`Product ID`, letter) %>%
  unite("Product ID", `Product ID`, letter, sep = "")

identical(result, test)
#> [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
import numpy as np

path = "CH-100 Manage Duplicate Values.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, dtype=str)
test  = pd.read_excel(path, usecols="D", skiprows=1, dtype=str)
test.columns = test.columns.str.replace('.1', '')

result = input.copy()
result['row'] = result.groupby('Product ID').cumcount()
result['nrows'] = result.groupby('Product ID')['Product ID'].transform('size')
result['letter'] = np.where(result['nrows'] > 1, result['row'].apply(lambda x: chr(x + 65)), '')
result['Product ID'] = result['Product ID'] + result['letter']
result = result[['Product ID']]

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.