Omid - Challenge 15

data-challenges
advanced-exercises
🔰 : Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 15

Challenge Description

🔰 : Transformation!

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-015.xlsx", range = "B2:E12")
test  = read_excel("files/CH-015.xlsx", range = "G2:S6")

result = input %>%
  group_by(`Product Code`) %>%
  mutate(nr = row_number()) %>%
  pivot_wider(names_from = nr, 
              values_from = c(`Ship Date`, `Po number`, `Po Quantity`), 
              names_sort = FALSE, 
              names_sep = " ") %>%
  ungroup() %>%
  select(Products = `Product Code`, ends_with("1"), ends_with("2"), ends_with("3"), ends_with("4"))

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

    • 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

input_data = pd.read_excel("CH-015.xlsx", usecols="B:E", skiprows=1, nrows=11)
test = pd.read_excel("CH-015.xlsx", usecols="G:S", skiprows=1, nrows=5)

input_data["nr"] = input_data.groupby("Product Code").cumcount() + 1
result = input_data.pivot(index="Product Code", columns="nr", values=["Ship Date", "Po number", "Po Quantity"])
result.columns = [f"{a} {b}" for a, b in result.columns]
keep = [c for c in result.columns if c.endswith("1") or c.endswith("2") or c.endswith("3") or c.endswith("4")]
result = result.reset_index()[["Product Code"] + keep].rename(columns={"Product Code": "Products"})

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

    • 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.