Excel BI - PowerQuery Challenge 219

excel-challenges
power-query
Name Machine Device OS Roy Laptop - Windows10
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 219

Challenge Description

Name Machine Device OS Roy Laptop - Windows10

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_219.xlsx"
input = read_excel(path, range = "A1:B7")
test  = read_excel(path, range = "D1:F12")

devices = c("Laptop", "Desktop", "Mobile")

result = input %>%
  separate_rows(Machine, sep = ", ") %>%
  separate(Machine, into = c("Device", "OS"), sep = " - ",remove = FALSE) %>%
  mutate(OS = case_when(
    is.na(OS) & Device %in% devices ~ lead(OS,1),
    is.na(OS) & !Device %in% devices ~ Device,
    TRUE ~ OS),
    Device = case_when(
      !Device %in% devices ~ lag(Device,1),
      TRUE ~ Device)) %>%
  select(-Machine)

identical(result, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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
import numpy as np

path = "PQ_Challenge_219.xlsx"
input = pd.read_excel(path, usecols="A:B", nrows=6)
test = pd.read_excel(path, usecols="D:F", nrows=12).rename(columns=lambda x: x.replace(".1", "")).apply(lambda x: x.str.strip() if x.dtype == "object" else x)

devices = ["Laptop", "Desktop", "Mobile"]

input = input.assign(Machine=input["Machine"].str.split(", ")).explode("Machine")
input[["Device", "OS"]] = input["Machine"].str.split(" - ", expand=True)
input["OS"] = np.where(input["OS"].isnull(), np.where(input["Device"].isin(devices), input["OS"].shift(-1), input["Device"]), input["OS"])
input["Device"] = np.where(input["Device"].isin(devices), input["Device"], input["Device"].shift(1))
input = input.drop("Machine", axis=1).reset_index(drop=True)

print(input.equals(test)) # True

  • 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 easy to moderate:

  • The transformation rule is readable, but the final layout still requires a careful implementation.