Excel BI - PowerQuery Challenge 196

excel-challenges
power-query
Transpose the problem table into result table. Class will be populated under subjects and marks will be populated under headers Marks-Subject Name.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 196

Challenge Description

Transpose the problem table into result table. Class will be populated under subjects and marks will be populated under headers Marks-Subject Name.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_196.xlsx"
input = read_xlsx(path, range = "A1:C11")
test  = read_xlsx(path, range = "F1:O5")

result = input %>%
  mutate(class1 = Class) %>%
  pivot_wider(names_from = Subject, values_from = c(class1, Marks), names_sep = "-") %>%
  select(-Class) %>%
  rename_with(~str_remove(., "class1-"), starts_with("class1-")) %>%
  select(sort(names(.), decreasing = FALSE)) %>%
  select(1:3,9:10, everything()) 

identical(result, test)
#> [1] TRUE

  • Logic:

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

    • Builds helper columns that drive the final output

    • Uses direct pattern parsing where the workbook encodes logic in text

  • 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 = "PQ_Challenge_196.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=11)
test = pd.read_excel(path, usecols="F:O", nrows=4)

result = input.copy()
result["class1"] = result["Class"]
result = result.pivot_table(index=["class1"], columns=["Subject"], values=["class1", "Marks"], aggfunc="first", fill_value="")
result.columns = result.columns.map(lambda x: "-".join(x))
result = result.reset_index()
result = result.sort_index(axis=1)
result.columns = result.columns.str.replace("Class-", "")
result = result.drop(columns=["class1"])
result = result.applymap(lambda x: pd.to_numeric(x, errors="coerce", downcast="integer"))
test = test.applymap(lambda x: pd.to_numeric(x, errors="coerce", downcast="integer"))

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

  • 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.