Excel BI - PowerQuery Challenge 193

excel-challenges
power-query
Transpose the problem table into result table. Here A, B means sum of A & B.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 193

Challenge Description

Transpose the problem table into result table. Here A, B means sum of A & B.

Solutions

library(tidyverse)
library(readxl)
library(unpivotr)

path = "Power Query/PQ_Challenge_193.xlsx"

input = read_xlsx(path, range = "A1:I6", col_names = FALSE)
test  = read_xlsx(path, range = "A12:F24")

result = input %>%
  as_cells() %>%
  behead("up-left", "Quarter") %>%
  behead("up", "Category") %>%
  behead("left", "Persons") %>%
  select(Persons, Quarter, Category, chr) %>%
  pivot_wider(names_from = Category, values_from = chr) %>%
  mutate(across(c(Sales, Bonus), as.numeric), 
         Total = Sales + Bonus) %>%
  pivot_longer(cols = Sales:Total, names_to = "Category", values_to = "Value") %>%
  pivot_wider(names_from = Quarter, values_from = Value) %>%
  mutate(across(c(Q1:Q4), cumsum), .by = Category) %>%
  mutate(Persons = accumulate(Persons, ~ paste(.x, .y, sep = ", "))[match(Persons, unique(Persons))], .by = Category) %>%
  mutate(Persons = ifelse(Category == "Sales", Persons, NA_character_))

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

  • Logic:

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

    • 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_193.xlsx"

input = pd.read_excel(path, sheet_name=0, usecols="A:I", nrows=6, header=None)
test = pd.read_excel(path, sheet_name=0, usecols="A:F", skiprows=11, nrows=13)

input.iloc[0] = input.iloc[0].ffill()
input.columns = input.iloc[0] + " " + input.iloc[1]
input = input.drop([0, 1])
input = input.rename(columns={"Quarters Persons": "Persons"})

for i in range(1, 5):
    input[f"Q{i} Total"] = input.filter(like=f"Q{i}").sum(axis=1)
input["Total"] = input.filter(like="Q").sum(axis=1)

input = input.melt(id_vars=["Persons"], value_vars=input.filter(like="Q").columns, var_name="Quarter", value_name="Value")
input["Value"] = pd.to_numeric(input["Value"], errors="coerce")
input[["Quarter", "Category"]] = input["Quarter"].str.split(" ", n=1, expand=True)
input["Category"] = pd.Categorical(input["Category"], categories=["Sales", "Bonus", "Total"], ordered=True)
input = input.sort_values(["Persons", "Quarter", "Category"])
input = input.pivot_table(index=["Persons", "Category"], columns="Quarter", values="Value", aggfunc="first").reset_index()
input[["Q1", "Q2", "Q3", "Q4"]] = input.groupby("Category")[["Q1", "Q2", "Q3", "Q4"]].cumsum()
input = input.rename_axis(None, axis=1)
input["Persons"] = input.groupby("Category")["Persons"].transform(lambda x: x.cumsum().str.join(", "))
input.loc[input.index % 3 != 0, "Persons"] = np.NaN
input["Category"] = input["Category"].astype(str)

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

    • Aggregates or ranks values at the relevant grouping level

    • 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.