Excel BI - PowerQuery Challenge 294

excel-challenges
power-query
Group Group C - Item3 - 12 Group A - Item2 - 59 Group C - Item2 - 46 Group B - Item2 - 54 Group A - Item1 - 89
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 294

Challenge Description

Group Group C - Item3 - 12 Group A - Item2 - 59 Group C - Item2 - 46 Group B - Item2 - 54 Group A - Item1 - 89

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/200-299/294/PQ_Challenge_294.xlsx"
input = read_excel(path, range = "A1:A12")
test = read_excel(path, range = "C1:F4")

result = input %>%
  filter(str_detect(Data, "Group")) %>%
  separate_wider_delim(Data, " - ", names = c("Group", "Item", "Value")) %>%
  type_convert() %>%
  mutate(Group = str_remove(Group, "Group ")) %>%
  pivot_wider(names_from = Item, values_from = Value, values_fn = sum) %>%
  select(Group, sort(names(.))) %>%
  arrange(Group)

all.equal(result, test, check.attributes = FALSE)
# wrong expected result.

  • 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

    • 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 = "200-299/294/PQ_Challenge_294.xlsx"
input = pd.read_excel(path, usecols="A", nrows=12, names=["Data"])
test = pd.read_excel(path, usecols="C:F", nrows=3)

result = (
    input[input["Data"].str.contains("Group", na=False)]
    .Data.str.split(" - ", expand=True)
    .rename(columns={0: "Group", 1: "Item", 2: "Value"})
    .assign(
        Group=lambda df: df["Group"].str.replace("Group ", "", regex=False),
        Value=lambda df: pd.to_numeric(df["Value"], errors="coerce")
    )
    .pivot_table(index="Group", columns="Item", values="Value", aggfunc="sum")
    .reset_index()
)

result = result[["Group"] + sorted([col for col in result.columns if col != "Group"])]
result = result.sort_values("Group").reset_index(drop=True)
result.columns.name = None

# Expected output - incorrect

  • 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

    • 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.