Excel BI - PowerQuery Challenge 290

excel-challenges
power-query
Generate the result table where Profit = Revenue - Cost - Tax. Profit needs to be calculated if profit is not given.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 290

Challenge Description

Generate the result table where Profit = Revenue - Cost - Tax. Profit needs to be calculated if profit is not given.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/200-299/290/PQ_Challenge_290.xlsx"
input = read_excel(path, range = "A1:B12")
test = read_excel(path, range = "D1:H4")

result = input %>%
  mutate(group = cumsum(Column1 == "Company")) %>%
  pivot_wider(names_from = "Column1", values_from = "Column2") %>%
  select(Company, Revenue, Cost, Tax, Profit) %>%
  mutate(across(-Company, as.integer)) %>%
  mutate(
    Tax = ifelse(is.na(Tax), 0, Tax),
    Profit = ifelse(is.na(Profit), (Revenue - Cost - Tax), Profit)
  )

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • Builds helper columns that drive the final output

  • Strengths:

    • The R solution stays close to the workbook logic and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the workbook layout and selected ranges remain stable.

  • Gem:

    • The best part of the solution is choosing the right intermediate shape before formatting the final output.
import pandas as pd

path = "200-299/290/PQ_Challenge_290.xlsx"
input = pd.read_excel(path, usecols="A:B", nrows=12)
test = pd.read_excel(path, usecols="D:H", nrows=3)

input['group'] = (input['Column1'] == 'Company').cumsum()
result = input.pivot(index='group', columns='Column1', values='Column2').reset_index(drop=True)
result['Tax'] = result['Tax'].fillna(0).astype(int)
result['Profit'] = result.apply(
    lambda r: r['Revenue'] - r['Cost'] - r['Tax'] if pd.isna(r['Profit']) else r['Profit'], axis=1
).astype(int)
result = result[['Company', 'Revenue', 'Cost', 'Tax', 'Profit']]
result.columns.name = None
for col in ['Revenue', 'Cost']:
    result[col] = result[col].astype(int)
print(result.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • Applies the rule iteratively until the output is complete

  • Strengths:

    • The Python version follows the same workbook rule in a direct pandas-oriented implementation.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the source challenge instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • It combines reshaping, grouping, or parsing steps that are common in Power Query style problems.

  • The main challenge is reproducing the workbook output structure exactly.