Excel BI - PowerQuery Challenge 316

excel-challenges
power-query
Transpose the problem table into result table where only 3 countries can come in a row. If more than 3 countries, then insert rows for every 3 countries.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 316

Challenge Description

Transpose the problem table into result table where only 3 countries can come in a row. If more than 3 countries, then insert rows for every 3 countries.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/300-399/316/PQ_Challenge_316.xlsx"
input = read_excel(path, range = "A1:C18")
test  = read_excel(path, range = "E1:H8")

result = input %>%
  fill(Continent, .direction = "down") %>%
  mutate(r = (row_number() + 2) %/% 3, .by = Continent) %>%
  unite("Country", Country, Medals, sep = "-") %>%
  mutate(nr = row_number(), .by = c(Continent, r)) %>%
  pivot_wider(names_from = nr, names_glue = "Country{nr}", values_from = Country) %>%
  select(-r) 

all.equal(result, test)
# there is a mistake in provided solution. UK is missing.

  • 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 = "300-399/316/PQ_Challenge_316.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=17)
test = pd.read_excel(path, usecols="E:H", nrows=7).rename(columns=lambda c: c.replace('.1', ''))

input['Continent'] = input['Continent'].ffill()
input['row_in_continent'] = input.groupby('Continent').cumcount() + 1
input['r'] = ((input['row_in_continent'] + 2) // 3)
input['Country'] = input['Country'] + '-' + input['Medals'].astype(str)
input['nr'] = input.groupby(['Continent', 'r']).cumcount() + 1

result = (
    input
    .pivot_table(index=['Continent', 'r'], columns='nr', values='Country', aggfunc='first')
    .reset_index()
)

result.columns = ['Continent', 'r'] + [f'Country{col}' for col in result.columns[2:]]
result = result.drop(columns='r')
result = result[result.columns[:len(test.columns)]]

print(result)

  • 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

    • 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.