Excel BI - PowerQuery Challenge 285

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

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 285

Challenge Description

Transpose the table as shown.

Solutions

library(tidyverse)
library(readxl)
library(unpivotr)
library(tidyxl)

path = "Power Query/200-299/285/PQ_Challenge_285.xlsx"
test = read_excel(path, range = "A10:F16")

input = xlsx_cells(path)

cells_subset <- input %>%
  filter(row >= 1 & row <= 5, col >= 1 & col <= 9)

result = cells_subset %>%
  behead("up", "Quarter") %>%
  behead("up-left", "Measure") %>%
  behead("left", "Fruits") %>%
  select(Quarter, Measure, Fruits, numeric) %>%
  fill(Quarter, .direction = "down") %>%
  pivot_wider(names_from = Quarter, values_from = numeric) %>%
  select(Fruits, Quarters = Measure, everything()) %>%
  arrange(Fruits) %>%
  mutate(Fruits = ifelse(Quarters == "Quantity", NA, Fruits))

all.equal(result, test)
# 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

test = pd.read_excel("PQ_Challenge_285.xlsx", usecols="A:F", skiprows=9, nrows=7)
input_data = pd.read_excel("PQ_Challenge_285.xlsx", header=None, nrows=5, usecols="A:I")

quarters = input_data.iloc[0, 1:].ffill()
measures = input_data.iloc[1, 1:]
rows = []
for r in range(2, input_data.shape[0]):
    fruit = input_data.iloc[r, 0]
    for c in range(1, input_data.shape[1]):
        rows.append({
            "Quarter": quarters.iloc[c - 1],
            "Measure": measures.iloc[c - 1],
            "Fruits": fruit,
            "numeric": input_data.iloc[r, c],
        })
result = pd.DataFrame(rows)
result = result.pivot_table(index=["Fruits", "Measure"], columns="Quarter", values="numeric", aggfunc="first").reset_index()
result = result.rename(columns={"Measure": "Quarters"})
result = result.sort_values("Fruits").reset_index(drop=True)
result["Fruits"] = result["Fruits"].where(result["Quarters"] != "Quantity")

print(result.equals(test))

  • 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.