Excel BI - PowerQuery Challenge 267

excel-challenges
power-query
Amount Running Total RESULT PROBLEM Work out the running total for first 10 entries, then next 9 entries, then next 8 entries till you reach 1 entry. After that running total will be only for 1 entry for each entry.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 267

Challenge Description

Amount Running Total RESULT PROBLEM Work out the running total for first 10 entries, then next 9 entries, then next 8 entries till you reach 1 entry. After that running total will be only for 1 entry for each entry.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_267.xlsx"
input = read_excel(path, range = "A1:A62")
test  = read_excel(path, range = "C1:D62")

main = rep(1:10, 10:1)
rest_count = nrow(input) - length(main)
rest = rep(10 + 1:rest_count, 1)
groups = c(main, rest)

result = input %>%
  bind_cols(tibble(groups = groups)) %>%
  mutate(running_total = cumsum(Amount), .by = groups) %>%
  select(-groups)

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_267.xlsx"
input = pd.read_excel(path, usecols="A", nrows=62)
test = pd.read_excel(path, usecols="C:D", nrows=62)

main = [i for i in range(1, 11) for _ in range(11 - i)][:len(input)]
groups = main + list(range(11, 11 + len(input) - len(main)))

input['groups'] = groups
input['Running Total'] = input.groupby('groups')['Amount'].cumsum()
result = input.drop(columns=['groups'])

print(result['Running Total'].equals(test['Running Total'])) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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 easy to moderate:

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