Excel BI - Excel Challenge 811

excel-challenges

excel-formulas

🔰 Answer Expected Item Code Item Name Location Quantity Material Cost Installation Cost Date Item Code

Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 811

Challenge Description

🔰 Answer Expected Item Code Item Name Location Quantity Material Cost Installation Cost Date Item Code

Solutions

library(tidyverse)
library(readxl)

path = "Excel/800-899/811/811 Dublicate Text, Dates and Divide Costs.xlsx"
input = read_excel(path, range = "A2:H6")
test  = read_excel(path, range = "J2:Q13")

result = input %>%
  uncount(Quantity, .remove = F) %>%
  mutate(Item = row_number(),
         `Item Code` = as.character(Code),
         Quantity1 = Quantity/Quantity,
         `Material Cost` = `Material Cost`/Quantity,
         `Installation Cost` = `Installation Cost`/Quantity) %>%
  select(-Quantity) %>%
  rename(Quantity = Quantity1) %>%
  janitor::adorn_totals("row") %>%
  select(Item, `Item Code`, `Item Name`, Location, Quantity, `Material cost` = `Material Cost`, `Installation Cost`, Date)

# all equal. Only total row looks differently

Logic: Read the workbook ranges needed for the challenge; Derive the required intermediate columns.
Strengths: The code maps the workbook rule into a compact, reproducible pipeline.
Areas for Improvement: The solution assumes the workbook layout and selected ranges remain stable, so any structural change in the sheet would require small adjustments.
Gem: The elegant part is how little code is needed once the correct intermediate representation is chosen.

import pandas as pd
import numpy as np

path = "800-899/811/811 Dublicate Text, Dates and Divide Costs.xlsx"
input = pd.read_excel(path, usecols="A:H", skiprows=1, nrows=4)
test = pd.read_excel(path, usecols="J:Q", skiprows=1, nrows=11).rename(columns=lambda x: x.replace('.1', ''))

df = input.loc[input.index.repeat(input['Quantity'])].reset_index(drop=True)
df['Item'] = range(1, len(df) + 1)
df['Material Cost'] /= input.loc[input.index.repeat(input['Quantity']), 'Quantity'].values
df['Installation Cost'] /= input.loc[input.index.repeat(input['Quantity']), 'Quantity'].values
df['Quantity'] = 1
result = df[['Item', 'Code', 'Item Name', 'Location', 'Quantity', 'Material Cost', 'Installation Cost', 'Date']]
result = result.rename(columns={'Code': 'Item Code'})
totals = result.sum(numeric_only=True)
result.loc[len(result)] = [np.nan, np.nan, np.nan, 'Total', 
                           totals['Quantity'].astype('int64'), totals['Material Cost'].astype('int64'), 
                           totals['Installation Cost'].astype('int64'), pd.NaT]

# little bit different typing

The Python version mirrors the same workbook logic with a concise, direct implementation.

Difficulty Level

Easy / Medium

The business rule is clear, though the workbook still needs a few transformation steps to reach the expected output.