Excel BI - PowerQuery Challenge 298

excel-challenges
power-query
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Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 298

Challenge Description

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Solutions

library(tidyverse)
library(readxl)

path = "Power Query/200-299/298/PQ_Challenge_298.xlsx"
input = read_excel(path, range = "A1:E5")
test  = read_excel(path, range = "G1:I15")

in1 = input %>%
  select(Company = 1, Subtype = 2, Price = 3) %>%
  mutate(Type = "Software")
in2 = input %>%
  select(Company = 1, Subtype = 4, Price = 5) %>%
  mutate(Type = "Hardware")

result = bind_rows(in1, in2) %>%
  filter(!is.na(Subtype)) %>%
  mutate(across(everything(), as.character)) %>%
  mutate(Order = row_number()) %>%
  pivot_longer(cols = -c(Company, Order), names_to = "Classification", values_to = "Value") %>%
  arrange(desc(Company) , Order,desc(Classification)) %>%
  mutate(cclass = row_number(),
         Classification = ifelse(Classification == "Subtype", "Sub type", Classification),
         .by = c("Company", "Value")) %>%
  filter(cclass == 1, !is.na(Value)) %>%
  select(-Order, -cclass) 

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

    • 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 = "200-299/298/PQ_Challenge_298.xlsx"
input = pd.read_excel(path, usecols="A:E", nrows=5)
test = pd.read_excel(path, usecols="G:I", nrows=15)

in1 = input.iloc[:, [0,1,2]].copy()
in1.columns = ['Company', 'Subtype', 'Price']
in1['Type'] = 'Software'

in2 = input.iloc[:, [0,3,4]].copy()
in2.columns = ['Company', 'Subtype', 'Price']
in2['Type'] = 'Hardware'

result = pd.concat([in1, in2], ignore_index=True)
result = result[result['Subtype'].notna()]
result['Order'] = range(1, len(result)+1)
result = result.melt(id_vars=['Company','Order'], var_name='Classification', value_name='Value')
result = result.sort_values(['Company', 'Order', 'Classification'], ascending=[False, True, False])
result['Classification'] = result['Classification'].replace({'Subtype': 'Sub type'})
result['cclass'] = result.groupby(['Company','Value']).cumcount()+1
result = result[(result['cclass']==1) & (result['Value'].notna())]
result = result.drop(['Order','cclass'], axis=1).reset_index(drop=True)

print(result.equals(test))
#> [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

  • 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.