Excel BI - PowerQuery Challenge 217

excel-challenges
power-query
Transpose the table as shown by showing the amount paid each month. Amount paid = Amt * number appearing the column. Also show row and column totals.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 217

Challenge Description

Transpose the table as shown by showing the amount paid each month. Amount paid = Amt * number appearing the column. Also show row and column totals.

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path = "Power Query/PQ_Challenge_217.xlsx"
input = read_excel(path, range = "A1:H5")
test  = read_excel(path, range = "J1:O8")

result = input %>%
  mutate(across(3:8, ~ . * Amt)) %>%
  select(-Amt) %>% 
  t() %>%
  as.data.frame() %>%
  row_to_names(1) %>%
  rownames_to_column(var = "Month")  %>%
  mutate(across(-Month, ~ as.numeric(.))) %>%
  adorn_totals(c("row", "col")) 

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

path = "PQ_Challenge_217.xlsx"
input = pd.read_excel(path, usecols="A:H", nrows = 4)
test  = pd.read_excel(path, usecols="J:O", nrows = 7)

input.iloc[:, 2:8] = input.iloc[:, 2:8].apply(lambda x: x * input["Amt"])
input = input.drop(columns=["Amt"])

input = input.T
input.columns = input.iloc[0]
input = input.drop(input.index[0])

input["Total"] = input.sum(axis=1)
input.loc["Total"] = input.sum()
input = input.reset_index().rename(columns={"index": "Month"}).rename_axis(None, axis=1)

print(all(input == test))   # True

  • Logic:

    • Reads the workbook range needed for the challenge

  • 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.