Excel BI - PowerQuery Challenge 266

excel-challenges
power-query
Summarise the table as shown with a total row after each sales person’s data.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 266

Challenge Description

Summarise the table as shown with a total row after each sales person’s data.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_266.xlsx"
input = read_excel(path, range = "A1:C20")
test  = read_excel(path, range = "E1:I18")

R1 = input %>%
  summarise(`Total Orders` = n(),
            `First Order Date` = min(Date),
            `Last Order Date` = max(Date),
            .by = c(`Sales Person`, Item))

R2 = input %>%
  summarise(`Total Orders` = n(),
            `First Order Date` = min(Date),
            `Last Order Date` = max(Date),
            Item = NA,
            .by = `Sales Person`) %>%
  mutate(`Sales Person` = paste0(`Sales Person`, " Total"))

result = bind_rows(R1, R2) %>%
  arrange(`Sales Person`, Item) %>%
  select(`Sales Person`, Item, `Total Orders`, `First Order Date`, `Last Order Date`)

all.equal(result, test, check.attributes = FALSE)
# [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
import numpy as np

path = "PQ_Challenge_266.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=20)
test = pd.read_excel(path, usecols="E:I", nrows=17).rename(columns=lambda col: col.split('.')[0])

R1 = input.groupby(['Sales Person', 'Item']).agg(
    Total_Orders=('Date', 'count'),
    First_Order_Date=('Date', 'min'),
    Last_Order_Date=('Date', 'max')
).reset_index()

R2 = input.groupby('Sales Person').agg(
    Total_Orders=('Date', 'count'),
    First_Order_Date=('Date', 'min'),
    Last_Order_Date=('Date', 'max')
).reset_index()
R2['Item'] = np.NaN
R2['Sales Person'] = R2['Sales Person'] + " Total"

result = pd.concat([R1, R2]).sort_values(by=['Sales Person', 'Item']).reset_index(drop=True)
result = result[['Sales Person', 'Item', 'Total_Orders', 'First_Order_Date', 'Last_Order_Date']]
result.columns = result.columns.str.replace('_', ' ')

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.