Excel BI - PowerQuery Challenge 318

excel-challenges
power-query
Row_Col C1 C2 C3 C4 R1
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 318

Challenge Description

Row_Col C1 C2 C3 C4 R1

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/300-399/318/PQ_Challenge_318.xlsx"
input = read_excel(path, sheet = 2, range = "A1:F7")
test  = read_excel(path, sheet = 2, range = "A11:E17")

result = input %>%
  pivot_longer(cols = -Alphabet, names_to = "Attribute", values_to = "Value") %>%
  select(-Attribute) %>%
  mutate(r = str_extract(Value, 'R[0-9]+'),
         c = str_extract(Value, 'C[0-9]+')) %>%
  na.omit() %>%
  arrange(r, c) %>%
  pivot_wider(names_from = c, values_from = Alphabet, id_cols = r) 

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

  • 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
import re

path = "300-399/318/PQ_Challenge_318.xlsx"
input = pd.read_excel(path, sheet_name=1, usecols="A:F", nrows=7)
test = pd.read_excel(path, sheet_name=1, usecols="A:E", skiprows=10, nrows=7)

df = input.melt(id_vars=["Alphabet"], var_name="atr", value_name="value").dropna(subset=["value"])
df[['Row', 'Col']] = df['value'].str.extract(r'(R\d+)(C\d+)')
input = df.pivot(index='Row', columns='Col', values='Alphabet').reset_index().rename_axis(None, axis=1).rename(columns={'Row': 'Row / Col'})

print(input.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • 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.