Excel BI - PowerQuery Challenge 305

excel-challenges
power-query
Transpose the problem table into result table. Insert a total row. Profit = Sale - Cost
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 305

Challenge Description

Transpose the problem table into result table. Insert a total row. Profit = Sale - Cost

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path = "Power Query/300-399/305/PQ_Challenge_305.xlsx"
input = read_excel(path, range = "A1:D14")
test  = read_excel(path, range = "G1:J14")

result = input %>%
  mutate(Org = ifelse(!is.na(`Org No.`), `Org Name`, NA_character_)) %>%
  rename(Region = `Org Name`, `Org Name` = Org) %>%
  fill(`Org No.`, `Org Name`) %>%
  filter(Region != `Org Name`) %>%
  mutate(Profit = Sale - Cost) %>%
  select(`Org No` = `Org No.`, `Org Name`, Region, Profit) %>%
  group_by(`Org No`, `Org Name`) %>%
  nest() %>%
  mutate(data = map(data, ~ adorn_totals(.x, "row"))) %>%
  unnest(data) %>%
  mutate(`Org No` = ifelse(Region == "Total", "TOTAL", `Org No`)) %>%
  mutate(`Org Name` = ifelse(`Org No` == "TOTAL", NA_character_, `Org Name`),
         Region = ifelse(`Org No` == "TOTAL", NA_character_, Region)) 

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

  • Logic:

    • Reads the workbook range needed for the challenge

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

    • Aggregates or ranks values at the relevant grouping level

    • 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
import numpy as np

path = "300-399/305/PQ_Challenge_305.xlsx"
input = pd.read_excel(path, usecols="A:D", nrows=14)
test = pd.read_excel(path, usecols="G:J", nrows=14).rename(columns=lambda x: x.replace('.1', ''))

input = (
    input.assign(Org=lambda df: np.where(df['Org No.'].notna(), df['Org Name'], np.nan))
    .rename(columns={'Org Name': 'Region', 'Org': 'Org Name'})
    .pipe(lambda df: df.assign(**df[['Org No.', 'Org Name']].ffill()))
    .loc[lambda df: df['Region'] != df['Org Name']]
    .assign(Profit=lambda df: (df['Sale'] - df['Cost']).astype(int))
    .filter(['Org No.', 'Org Name', 'Region', 'Profit'])
    .rename(columns={'Org No.': 'Org No'})
)

dfs = {}
for org_name, group in input.groupby('Org No'):
    group = group.copy()
    total_row = group[['Profit']].sum().rename('Total')
    total_row['Org No'] = 'TOTAL'
    total_row['Org Name'] = np.nan
    total_row['Region'] = np.nan
    group = pd.concat([group, total_row.to_frame().T], ignore_index=True)
    dfs[org_name] = group

result = pd.concat(dfs.values(), ignore_index=True)

print(result.fillna('NaN').equals(test.fillna('NaN'))) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds helper columns that drive the final output

    • 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.