Omid - Challenge 122

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 122

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-122 Table Transformation.xlsx"
input = read_excel(path, range = "C2:E27")
test  = read_excel(path, range = "G2:J17") %>%
  mutate(Date = format(Date, "%d/%m/%Y"))

block_size = 5

result = input %>%
  mutate(row_id = row_number(),
         block_id = (row_id - 1) %/% block_size + 1) %>%
  summarise(
    date = Date[2],
    region = Date[1],
    fruits = list(Description[3:block_size]),
    values = list(Qty[3:block_size]),
    .by = block_id
  ) %>%
  unnest(c(fruits, values)) %>%
  arrange(date, block_id, desc(values)) %>%
  select(-block_id)

names(result) = names(test)

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

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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

path = "CH-122 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="C:E", skiprows=1, nrows=26)
test = pd.read_excel(path, usecols="G:J", skiprows=1, nrows=15).rename(columns=lambda x: x.replace('.1', ''))

block_size = 5

input['row_id'] = input.index + 1
input['block_id'] = (input['row_id'] - 1) // block_size + 1

result = input.groupby('block_id').apply(
    lambda x: pd.DataFrame({
        'date': [x['Date'].iloc[1]] * (block_size - 2),
        'region': [x['Date'].iloc[0]] * (block_size - 2),
        'fruits': x['Description'].iloc[2:block_size].tolist(),
        'values': x['Qty'].iloc[2:block_size].tolist()
    })
).reset_index(level=1, drop=True)

result = result.sort_values(by=['date', 'block_id', 'values'], ascending=[True, True, False]).reset_index(drop=True)
result = result.assign(date=pd.to_datetime(result['date']), values=result['values'].astype("int64"))

result.columns = test.columns

print(result.equals(test))
# True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.