Excel BI - PowerQuery Challenge 319

excel-challenges
power-query
Transpose the table as shown. Transpose the table as shown
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 319

Challenge Description

Transpose the table as shown. Transpose the table as shown

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/300-399/319/PQ_Challenge_319.xlsx"
input = read_excel(path, sheet = 2,  range = "A1:I4")
test  = read_excel(path, sheet = 2, range = "A9:F18")

result = input %>%
  pivot_longer(-Fruits,
               names_to = c("Q", ".value"),
               names_sep = "-") %>%
  mutate(Total = Price * Quantity) %>%
  pivot_longer(-c(Fruits, Q),
               names_to = "Quarters",
               values_to = "Values") %>%
  pivot_wider(names_from = Q,
              values_from = Values) %>%
  mutate(Fruits = ifelse(row_number() == 1, Fruits, NA_character_), .by = Fruits)

all.equal(result, test)
# > [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 = "300-399/319/PQ_Challenge_319.xlsx"
input = pd.read_excel(path, sheet_name=1, usecols="A:I", nrows=4)
test = pd.read_excel(path, sheet_name=1, usecols="A:F", skiprows=8, nrows=10)

input_long = input.melt(id_vars='Fruits', var_name='Q_value')
input_long[['Q', 'Type']] = input_long['Q_value'].str.split('-', expand=True)
input_wide = input_long.pivot(index=['Fruits', 'Q'], columns='Type', values='value').reset_index()
input_wide[['Price', 'Quantity']] = input_wide[['Price', 'Quantity']].apply(pd.to_numeric)
input_wide['Total'] = input_wide['Price'] * input_wide['Quantity']
result_long = input_wide.melt(id_vars=['Fruits', 'Q'], value_vars=['Price', 'Quantity', 'Total'],
                              var_name='Quarters', value_name='Values')
result = result_long.pivot(index=['Fruits', 'Quarters'], columns='Q', values='Values').reset_index()
result['Fruits'] = result.groupby('Fruits')['Fruits'].transform(lambda x: [x.iloc[0]] + [None]*(len(x)-1))

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

    • 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.