Excel BI - PowerQuery Challenge 339

excel-challenges
power-query
Pivot the table as shown.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 339

Challenge Description

Pivot the table as shown.

Solutions

library(tidyverse)
library(readxl)

excel_path <- "Power Query/300-399/339/PQ_Challenge_339.xlsx"
input = read_excel(excel_path, range = "A1:A7")
test  = read_excel(excel_path, range = "C1:F7")

result = input %>%
  mutate(
    Country = str_extract(Data, "^[A-Z][a-z]+\\s+([A-Z][a-z]+)?") %>% trimws(),
    `Time Zone` = str_extract(Data, '(?<=\\().+?(?=\\))'),
    Latitude = as.numeric(str_extract(Data, "(?<=LAT )-?[\\d.]+")),
    Longitude = as.numeric(str_extract(Data, "(?<=LONG )-?[\\d.]+"))
  ) %>%
  select(-Data)
  
all.equal(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds helper columns that drive the final output

    • 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
import re

excel_path = "Power Query/300-399/339/PQ_Challenge_339.xlsx"
input = pd.read_excel(excel_path, usecols="A:A", nrows=7)
test = pd.read_excel(excel_path, usecols="C:F", nrows=7)

result = pd.DataFrame({
    "Country": input["Data"].map(
        lambda s: (re.match(r"^[A-Z][a-z]+(?:\s+[A-Z][a-z]+)?", s).group(0).strip() if re.match(r"^[A-Z][a-z]+(?:\s+[A-Z][a-z]+)?", s) else None)
    ),
    "Time Zone": input["Data"].map(
        lambda s: (re.search(r"\((.+?)\)", s).group(1) if re.search(r"\((.+?)\)", s) else None)
    ),
    "Latitude": input["Data"].map(
        lambda s: (float(re.search(r"LAT (-?[\d.]+)", s).group(1)) if re.search(r"LAT (-?[\d.]+)", s) else None)
    ),
    "Longitude": input["Data"].map(
        lambda s: (float(re.search(r"LONG (-?[\d.]+)", s).group(1)) if re.search(r"LONG (-?[\d.]+)", s) else None)
    ),
})

print(result.equals(test))

  • Logic:

    • Reads the workbook range needed for the challenge

    • Uses direct pattern parsing where the workbook encodes logic in text

  • 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.