Excel BI - Excel Challenge 745

excel-challenges
excel-formulas
🔰 Answer Expected Company Dept Revenue Cost Total Revenue Total Cost Total Profit MSFT Sales
Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 745

Challenge Description

🔰 Answer Expected Company Dept Revenue Cost Total Revenue Total Cost Total Profit MSFT Sales

Solutions

library(tidyverse)
library(readxl)

path = "Excel/700-799/745/745 Financial Pivot.xlsx"
input = read_excel(path, range = "A2:D16")
test  = read_excel(path, range = "F2:I6")

result = input %>%
  fill(Company) %>%
  summarise(`Total Revenue` = sum(Revenue, na.rm = TRUE),
            `Total Cost` = sum(Cost, na.rm = TRUE),
            .by = Company) %>%
  mutate(`Total Profit` = `Total Revenue` - `Total Cost`) %>%
  janitor::adorn_totals("row", name = "Grand Total") %>% 
  as_tibble() 

all.equal(result, test, check.attributes = FALSE)
# [1] TRUE
  • Logic: Read the workbook ranges needed for the challenge; Derive the required intermediate columns; Aggregate or rank the data at the required grouping level; Reshape the result into the workbook output format.
  • Strengths: The reshaping step mirrors the workbook output closely instead of forcing extra post-processing.
  • Areas for Improvement: The solution assumes the workbook layout and selected ranges remain stable, so any structural change in the sheet would require small adjustments.
  • Gem: The last reshape turns a raw transformation into something that already looks like a report.
import pandas as pd

path = "700-799/745/745 Financial Pivot.xlsx"
input = pd.read_excel(path, usecols="A:D", skiprows=1, nrows=15)
test = pd.read_excel(path, usecols="F:I", skiprows=1, nrows=4).rename(columns=lambda c: c.replace('.1', ''))

input['Company'] = input['Company'].ffill()
result = input.groupby('Company', as_index=False)[['Revenue', 'Cost']].sum()
result.columns = ['Company', 'Total Revenue', 'Total Cost']
result['Total Profit'] = result['Total Revenue'] - result['Total Cost']
result = result.sort_values('Total Profit', ascending=True, ignore_index=True)
result.loc[len(result)] = ['Grand Total'] + result.iloc[:, 1:].sum().tolist()

print(result.equals(test)) # True

The Python version follows the same grouped logic and keeps the transformation explicit in a dataframe pipeline.

Difficulty Level

Medium

The individual steps are manageable, but the correct transformation pattern is not obvious from the raw data.