Omid - Challenge 242

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 242

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/242/CH-242 Table Transformation.xlsx"
input = read_excel(path, range = "B2:B14")
test = read_excel(path, range = "D2:F7") %>% arrange(Product)

result = tibble(
  Product = input$`Column 1`[seq(1, nrow(input), 2)],
  Price = input$`Column 1`[seq(2, nrow(input), 2)]
) %>%
  separate(Product, into = c("Product", "unit"), sep = " (?=[^ ]+$)") %>%
  separate_rows(Product, sep = ", ") %>%
  mutate(
    unit = trimws(unit),
    Product = trimws(Product),
    Price = as.integer(Price)
  ) %>%
  arrange(Product) %>%
  pivot_wider(names_from = unit, values_from = Price) %>%
  select(Code, Product, Price)

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

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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 = "200-299/242/CH-242 Table Transformation.xlsx"
input_df = pd.read_excel(path, usecols="B", skiprows=1, nrows=13)
test = pd.read_excel(path, usecols="D:F", skiprows=1, nrows=5).sort_values("Product").reset_index(drop=True)

df = pd.DataFrame({
    'Product': input_df.iloc[::2, 0].reset_index(drop=True),
    'Price': input_df.iloc[1::2, 0].reset_index(drop=True)
})
df[['Product', 'unit']] = df['Product'].str.rsplit(' ', n=1, expand=True)
df = df.assign(Product=df['Product'].str.split(', ')).explode('Product')
df[['Product', 'unit']] = df[['Product', 'unit']].apply(lambda x: x.str.strip())
df['Price'] = df['Price'].astype(int)
result = df.sort_values('Product').pivot(index='Product', columns='unit', values='Price').reset_index()
result.columns.name = None
result = result[['Code', 'Product', 'Price']]

print(result.equals(test))
# True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Builds the intermediate columns that drive the final result

  • 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.