Omid - Challenge 237

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 237

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/237/CH-237 Table Transformation.xlsx"
input = read_excel(path, range = "B2:E10")
test = read_excel(path, range = "H2:O6")

result = input %>%
  mutate(rn = row_number(), .by = Doc) %>%
  pivot_wider(
    names_from = rn,
    values_from = c(Code, Num),
    names_sep = "",
    names_vary = "slowest"
  )

all.equal(result, test)
#> [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
import re

path = "200-299/237/CH-237 Table Transformation.xlsx"
input = pd.read_excel(path, sheet_name=0, usecols="B:E", skiprows=1, nrows=8)
test = pd.read_excel(path, sheet_name=0, usecols="H:O", skiprows=1, nrows=4).rename(columns=lambda col: col.replace('.1', ''))

input['rn'] = input.groupby(['Doc', 'Status']).cumcount() + 1
result = input.pivot_table(
    index=['Doc', 'Status'],
    columns='rn',
    values=['Code', 'Num'],
    aggfunc='first'
)
result.columns = [f"{col[0]}{col[1]}" for col in result.columns]

def sort_key(col):
    m = re.match(r'(Code|Num)(\d+)', col)
    if m:
        return int(m.group(2)), 0 if m.group(1) == 'Code' else 1
    return (float('inf'), col)

result = result.reindex(sorted(result.columns, key=sort_key), axis=1)
result['Num1'] = result['Num1'].astype(int)
result = result.reset_index()

print(result.equals(test)) # 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

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

  • 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.