Omid - Challenge 66

data-challenges
advanced-exercises
🔰 Convert the Question table with the merge headers into the result table.
Published

March 24, 2026

Illustration for Omid - Challenge 66

Challenge Description

🔰 Convert the Question table with the merge headers into the result table.

Solutions

library(tidyverse)
library(readxl)
library(unpivotr)

input = read_excel("files/CH-066 Merged cells.xlsx", range = "B2:G8", col_names = F)
test  = read_excel("files/CH-066 Merged cells.xlsx", range = "I2:L17")

result = input %>% 
  as_cells() %>%
  behead("up-left", "scenario") %>%
  behead("up", "Year") %>%
  behead("left", "Department") %>%
  select(Department, Year, scenario, dbl, chr) %>%
  mutate(value = case_when(
    !is.na(dbl) ~ dbl,
    !is.na(chr) ~ as.numeric(chr),
    TRUE ~ NA_real_
  ),
  Year = as.numeric(Year)) %>%
  select(-dbl, -chr) %>%
  pivot_wider(names_from = scenario, values_from = value)

identical(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

input = pd.read_excel("CH-066 Merged cells.xlsx", skiprows=1, usecols="B:G", header=None, nrows=7)
test = pd.read_excel("CH-066 Merged cells.xlsx", skiprows=1, usecols="I:L", nrows=16)
test.columns = test.columns.str.replace('.1', '')

input = input.transpose()
input[0] = input[0].fillna(method='ffill')
input.columns = input.iloc[0]
input = input[1:]
input = input.rename(columns={'Department': 'Scenario'})
input = input.rename(columns={input.columns[1]: 'Year'})

for i in range(1, len(input.columns)):
    input[input.columns[i]] = pd.to_numeric(input[input.columns[i]], errors='coerce')

input = pd.melt(input, id_vars=['Scenario', 'Year'], var_name='Department', value_name='Value')
input = input.pivot_table(index=['Department', 'Year'], columns='Scenario', values='Value').reset_index()
input = input.sort_values(['Year', 'Department']).reset_index(drop=True)
input["Budget"] = input["Budget"].astype('int64')
input.columns.name = None

test = test.sort_values(['Year', 'Department']).reset_index(drop=True)

print(input.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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.