Excel BI - PowerQuery Challenge 200

excel-challenges
power-query
Student Biology Physics Chemistry Ecology John
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 200

Challenge Description

Student Biology Physics Chemistry Ecology John

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_200.xlsx"
input1 = read_excel(path, range = "A1:D6")
input2 = read_excel(path, range = "F1:I6")
test = read_excel(path, range = "A11:E17")

in1 = input1 %>%
  pivot_longer(cols = -c(1), names_to = "subject", values_to = "score")
in2 = input2 %>%
  pivot_longer(cols = -c(1), names_to = "subject", values_to = "score")

result = bind_rows(in1, in2) %>%
  summarise(max = max(score), .by = c("subject", "Student")) %>%
  pivot_wider(names_from = "subject", values_from = "max") %>%
  arrange(Student) 

result = result %>%
  select(Student, sort(names(result)[2:5]))

identical(result, 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 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_200.xlsx"
input1 = pd.read_excel(path, usecols="A:D", nrows = 5)
input2 = pd.read_excel(path, usecols="F:I", nrows=5)
input2.columns = input2.columns.str.replace(".1", "")
test = pd.read_excel(path, usecols="A:E", skiprows = 10, nrows = 6)

in1 = input1.melt(id_vars=["Student"], var_name="subject", value_name="score")
in2 = input2.melt(id_vars=["Student"], var_name="subject", value_name="score")

result = pd.concat([in1, in2]).groupby(["subject", "Student"]).agg(max_score=("score", "max")).reset_index()
result = result.pivot(index="Student", columns="subject", values="max_score").reset_index()
result = result.sort_values("Student")
result.columns.name = None

result.iloc[:, 1:] = result.iloc[:, 1:].fillna(0).astype("float64")
test.iloc[:, 1:] = test.iloc[:, 1:].fillna(0).astype("float64")

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.