Excel BI - PowerQuery Challenge 243

excel-challenges
power-query
Group Group1 Group2 Group3
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 243

Challenge Description

Group Group1 Group2 Group3

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_243.xlsx"
input = read_excel(path, range = "A1:B10")
test  = read_excel(path, range = "D1:G12")

result = input %>%
  mutate(Group_2 = paste0("Group", row_number()), .by = `Emp ID`) %>%
  arrange(`Emp ID`) %>%
  separate_rows(Group, sep = ", ") %>%
  mutate(x = row_number(), .by = c(`Emp ID`, Group_2)) %>%
  pivot_wider(names_from = Group_2, values_from = Group) %>%
  select(-x)

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 = "PQ_Challenge_243.xlsx"
input = pd.read_excel(path,usecols="A:B", nrows=9)
test = pd.read_excel(path, usecols="D:G", nrows=12).rename(columns=lambda x: x.split('.')[0])

input['Group_2'] = input.groupby('Emp ID').cumcount().add(1).astype(str).radd('Group')
input = input.sort_values('Emp ID').assign(Group=input['Group'].str.split(', ')).explode('Group')
input['x'] = input.groupby(['Emp ID', 'Group_2']).cumcount().add(1)

pivot_table = input.pivot_table(index=['Emp ID', 'x'], columns='Group_2', values='Group', aggfunc='first')
pivot_table = pivot_table.reset_index().drop(columns='x').rename_axis(None, axis=1)

print(pivot_table.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

    • Builds helper columns that drive the final output

  • 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.