Omid - Challenge 21

data-challenges
advanced-exercises
🔰 : Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 21

Challenge Description

🔰 : Transformation!

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-021 Transformation.xlsx", range = "B2:E11")
test  = read_excel("files/CH-021 Transformation.xlsx", range = "G2:H8")

result = input %>%
  select(`Machinary code`, Product_1 = 2, Product_2 = 3, Product_3 = 4) %>%
  pivot_longer(cols = -`Machinary code`, names_to = "Product", values_to = "Value") %>%
  na.omit() %>%
  arrange(Product) %>%
  group_by(Value) %>%
  summarise(Machine = paste0(`Machinary code`, collapse = " ,")) %>%
  select(`Product Code`= Value, `Machinary Code` = Machine)

identical(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

  • 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_data = pd.read_excel("CH-021 Transformation.xlsx", usecols="B:E", skiprows=1, nrows=10)
test = pd.read_excel("CH-021 Transformation.xlsx", usecols="G:H", skiprows=1, nrows=7)

result = (
    input_data.rename(columns={input_data.columns[1]: "Product_1", input_data.columns[2]: "Product_2", input_data.columns[3]: "Product_3"})
    .melt(id_vars="Machinary code", var_name="Product", value_name="Value")
    .dropna(subset=["Value"])
    .sort_values("Product")
    .groupby("Value", as_index=False)["Machinary code"]
    .agg(lambda s: " ,".join(s.astype(str)))
    .rename(columns={"Value": "Product Code", "Machinary code": "Machinary Code"})
)

print(result.equals(test))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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 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.