Excel BI - PowerQuery Challenge 353

excel-challenges
power-query
Calculate the Total 2025 Kickoff Bonus - Region-wise and Grand Total. Add Sales ‘Accelerator’: If the employee is in the Sales department, add $300 to the result after the tax is calculated.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 353

Challenge Description

Calculate the Total 2025 Kickoff Bonus - Region-wise and Grand Total. Add Sales “Accelerator”: If the employee is in the Sales department, add $300 to the result after the tax is calculated.

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/353/PQ_Challenge_353.xlsx"
input1 <- read_excel(path, range = "A1:E51")
input2 <- read_excel(path, range = "G1:H5")
test <- read_excel(path, range = "G9:H14")

result <- input1 %>%
  left_join(input2, by = "Region") %>%
  mutate(
    capped = pmin(`2024 Gross` * .05, `Max Bonus Cap`),
    prosperity_tax = case_when(
      capped < 2000 ~ 0,
      capped <= 3500 ~ (capped - 2000) * 0.10,
      TRUE ~ 150 + (capped - 3500) * .2
    ),
    total = capped - prosperity_tax + if_else(Dept == "Sales", 300, 0)
  ) %>%
  summarise(Bonus = sum(total, na.rm = TRUE), .by = Region) %>%
  arrange(Region) %>%
  janitor::adorn_totals("row", name = "Total")

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

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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 = "Power Query/300-399/353/PQ_Challenge_353.xlsx"
input1 = pd.read_excel(path, usecols="A:E", nrows=51)
input2 = pd.read_excel(path, usecols="G:H", nrows=4).rename(columns=lambda c: c.replace(".1", ""))
test = pd.read_excel(path, usecols="G:H", skiprows=8, nrows=5).rename(columns=lambda c: c.replace(".1", ""))

df = input1.merge(input2, on="Region")
df["capped"] = np.minimum(df["2024 Gross"] * 0.05, df["Max Bonus Cap"])
df["prosperity_tax"] = np.select(
    [df.capped < 2000, df.capped.between(2000, 3500), df.capped > 3500],
    [0, (df.capped - 2000) * 0.1, 150 + (df.capped - 3500) * 0.2]
)
df["total"] = df.capped - df.prosperity_tax + np.where(df.Dept == "Sales", 300, 0)
result = df.groupby("Region", as_index=False)["total"].sum().rename(columns={"total": "Bonus"}).sort_values("Region")
result = pd.concat([result, pd.DataFrame({"Region": ["Total"], "Bonus": [result.Bonus.sum()]})], ignore_index=True)
result["Bonus"] = result["Bonus"].astype(int)
print(result.equals(test))   # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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.