Excel BI - PowerQuery Challenge 281

excel-challenges
power-query
Extract the capitals of different states. States appear after dash (‘-’) and capitals are followed by ‘(C)’.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 281

Challenge Description

Extract the capitals of different states. States appear after dash (“-”) and capitals are followed by “(C)”.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_281.xlsx"
input = read_excel(path, range = "A1:A9")
test = read_excel(path, range = "C1:D10")


# Solution 1
result = input %>%
  separate_rows(Data, sep = ", ") %>%
  filter(str_detect(Data, "\\(C\\)")) %>%
  separate(Data, into = c("State", "Capital"), sep = " \\(C\\) - ")

# Solution 2
result <- input %>%
  separate_rows(Data, sep = ", ") %>%
  extract(Data, c("State", "Capital"), "^(.*?) \\(C\\) - (.*)$") %>%
  drop_na()

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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_281.xlsx"
input = pd.read_excel(path, usecols="A", nrows=8)
test = pd.read_excel(path, usecols="C:D", nrows=10)

result = input.assign(Data=input['Data'].str.split(',')).explode('Data')
result[['Capital', 'State']] = result['Data'].str.split(r' \(C\) - ', expand=True)
result = result.drop(columns=['Data']).sort_values(by=['State'])
result = result[["State", "Capital"]].reset_index(drop=True)
result = result.dropna()


print(result)
print(test)

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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.