Omid - Challenge 265

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 265

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/265/CH-265 Table Transformation.xlsx"
input = read_excel(path, range = "B2:D9")
test  = read_excel(path, range = "F2:I8")

result = input %>%
  mutate(Region = ifelse(str_detect(Column1, "Region"), Column1, NA)) %>%
  fill(Region) %>%
  filter(!duplicated(Column1),
         Column1 != Region) %>%
  setNames(c("Product","Jan","Feb","Region")) %>%
  slice(-1) %>%
  pivot_longer(cols = c("Jan", "Feb"), 
             names_to = "Month", 
             values_to = "Value") %>%
  relocate(Region, .before = Product) %>%
  mutate(Value = as.numeric(Value))

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

    • 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

path = "200-299/265/CH-265 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:D", skiprows=1, nrows=8)
test = pd.read_excel(path, usecols="F:I", skiprows=1, nrows=6)

input['Region'] = input['Column1'].where(input['Column1'].str.contains('Region'), pd.NA)
input['Region'] = input['Region'].ffill()
input = input[~input['Column1'].duplicated()]
input = input[input['Column1'] != input['Region']]
input.columns = ["Product", "Jan", "Feb", "Region"]
input = input.iloc[1:]
result = input.melt(id_vars=['Region', 'Product'], value_vars=['Jan', 'Feb'],
                       var_name='Month', value_name='Value')
result = result[['Region', 'Product', 'Month', 'Value']]\
    .sort_values(by=['Region', 'Product', 'Month'],ascending=[True, True, False]).reset_index(drop=True)
result['Value'] = pd.to_numeric(result['Value'])

print(result.equals(test))  # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

  • 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.