Excel BI - PowerQuery Challenge 335

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

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 335

Challenge Description

Transpose the table as shown.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/300-399/335/PQ_Challenge_335.xlsx"
input = read_excel(path, range = "A1:F7")
test  = read_excel(path, range = "A12:I15")

result = input %>%
  fill(Fruits) %>%
  pivot_longer(-c(Fruits, Quarters), names_to = "Quarter") %>%
  arrange(Fruits, Quarter) %>%
  pivot_wider(names_from = c(Quarters, Quarter), values_from = value,
              names_glue = "{Quarters}-{Quarter}", names_vary = "slowest")

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

  • 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 re

path = "300-399/335/PQ_Challenge_335.xlsx"
input = pd.read_excel(path, sheet_name=None, header=0)
input = pd.read_excel(path, usecols="A:F", nrows=7)
test = pd.read_excel(path, usecols="A:I", skiprows=11, nrows=4)

input['Fruits'] = input['Fruits'].ffill()
result = input.set_index(['Fruits', 'Quarters']).unstack().sort_index(axis=1, level=1)
result.columns = [f"{q}-{col}" for col, q in result.columns]
result = result.reset_index()
cols = result.columns.tolist()
result = result[['Fruits'] + sorted([c for c in cols if c != 'Fruits'], key=lambda x: x.split('-')[::-1])]
result.columns.name = None

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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.