Excel BI - PowerQuery Challenge 302

excel-challenges
power-query
Transpose the given data from Problem Table into Result table as shown
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 302

Challenge Description

Transpose the given data from Problem Table into Result table as shown

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/300-399/302/PQ_Challenge_302.xlsx"
input = read_excel(path, range = "A1:C12")
test  = read_excel(path, range = "E1:F13")

result = input %>%
  fill(Country, State) %>%
  summarise(Cities = paste0(Cities, collapse = ", "), .by = c("Country", "State")) %>%
  pivot_longer(everything()) %>%
  mutate(no = row_number(), .by = c("name", "value")) %>%
  filter(no == 1) %>%
  select(Data1 = name, Data2 = value) 

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

    • Aggregates or ranks values at the relevant grouping level

    • 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 = "300-399/302/PQ_Challenge_302.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=12)
test = pd.read_excel(path, usecols="E:F", nrows=13)

input[['Country', 'State']] = input[['Country', 'State']].ffill()
input['Country_State_Order'] = input.groupby(['Country', 'State'], sort=False).ngroup()

result = (
    input
    .groupby(['Country', 'State', 'Country_State_Order'], as_index=False)
    .agg({'Cities': lambda x: ', '.join(x.dropna().astype(str))})
    .sort_values('Country_State_Order')
    .reset_index(drop=True)
)

dfs = []
for idx, row in result.iterrows():
    df = pd.DataFrame({
        'Data1': result.columns,
        'Data2': row.values
    })
    dfs.append(df)

final_df = pd.concat(dfs, ignore_index=True)
final_df = final_df[final_df['Data1'] != 'Country_State_Order'].drop_duplicates().reset_index(drop=True)

print(final_df.equals(test))

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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.