Excel BI - PowerQuery Challenge 172

excel-challenges
power-query
Item Amount Agent1 Agent2 Commission % Share %
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 172

Challenge Description

Item Amount Agent1 Agent2 Commission % Share %

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_172.xlsx", range = "A1:F10") %>% 
  janitor::clean_names()
test  = read_excel("Power Query/PQ_Challenge_172.xlsx", range = "H1:I6")

r1 = input %>%
  mutate(share_percent = ifelse(is.na(share_percent), "100", share_percent)) %>%
  separate_rows(share_percent, sep = ", ") %>%
  group_by(item) %>%
  mutate(nr = row_number(), 
         Agent = ifelse(nr == 1, agent1, agent2)) %>%
  ungroup() %>%
  mutate(Commission = amount * as.numeric(share_percent) / 100 * commission_percent / 100)

top = r1 %>%
  group_by(Agent) %>%
  summarise(Commission = sum(Commission) %>% round(0))

total = top %>%
  summarise(Commission = sum(Commission))

total$Agent = "Total"
result = select(total, Agent, Commission) %>%
  bind_rows(top) %>%
  arrange(Agent)

identical(result, test)
# [1] TRUE

  • 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

input = pd.read_excel("PQ_Challenge_172.xlsx", usecols="A:F", nrows = 10)
test = pd.read_excel("PQ_Challenge_172.xlsx", usecols="H:I", nrows = 5)

r1 = input.copy()
r1["share_percent"] = r1["Share %"].fillna("100")
r1 = r1.assign(share_percent=r1["share_percent"].str.split(", ")).explode("share_percent")
r1["nr"] = r1.groupby("Item").cumcount() + 1
r1["Agent"] = r1.apply(lambda x: x["Agent1"] if x["nr"] == 1 else x["Agent2"], axis=1)
r1["Commission"] = r1["Amount"] * r1["share_percent"].astype(float) / 100 * r1["Commission %"] / 100

top = r1.groupby("Agent").agg(Commission=("Commission", "sum")).round(0).astype(int).reset_index()
total = top.agg(Commission=("Commission", "sum")).reset_index()
total["Agent"] = "Total"

result = pd.concat([total[["Agent", "Commission"]], top[["Agent", "Commission"]]]).sort_values("Agent").reset_index(drop=True)

print(result.equals(test)) # True

  • 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 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.