Omid - Challenge 47

data-challenges
advanced-exercises
🔰 The ‘Question’ table presents a list of product IDs collected from several warehouses.
Published

March 24, 2026

Illustration for Omid - Challenge 47

Challenge Description

🔰 The “Question” table presents a list of product IDs collected from several warehouses.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-047 Multiple text replaces.xlsx", range = "B2:B11")
dict  = read_excel("files/CH-047 Multiple text replaces.xlsx", range = "E2:F10") %>%
  replace_na(list(Old = " "))
test  = read_excel("files/CH-047 Multiple text replaces.xlsx", range = "J2:J11")

result = input$`Product IDs` %>%
  reduce(dict$Old, ~ str_replace_all(.x, fixed(.y), dict$New[dict$Old == .y]), .init = .) %>%
  tibble(`Product IDs` = .)

identical(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Parses the text patterns directly instead of relying on manual cleanup

  • 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

input = pd.read_excel("CH-047 Multiple text replaces.xlsx", sheet_name="Sheet1", usecols="B", skiprows=1)
dict = pd.read_excel("CH-047 Multiple text replaces.xlsx", sheet_name="Sheet1", usecols="E:F", skiprows=1).fillna(" ")
test = pd.read_excel("CH-047 Multiple text replaces.xlsx", sheet_name="Sheet1", usecols="J", skiprows=1)
test.columns = input.columns

for index, row in dict.iterrows():
    input["Product IDs"] = input["Product IDs"].str.replace(row[0], row[1])

print(input.equals(test)) # True

  • 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 core logic is clear, but the correct transformation pattern is not obvious from the raw input.

  • The challenge combines multiple reshaping, grouping, or parsing steps.