Crispo - Excel Challenge 38 2024

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

September 22, 2024

Illustration for Crispo - Excel Challenge 38 2024

Challenge Description

Easy Sunday Excel Challenge

⭐ SUMMARY Accounting, Budgeting, Accounts Payable, Accounts Receivable, Treasury Sales Operations, Account Management, Business Development ⭐Summarise the budget per Department

Solutions

library(tidyverse)
library(readxl)

path = "files/Excel Challenge September 22nd.xlsx"
input1 = read_excel(path, range = "B3:C11") %>% janitor::clean_names()
input2 = read_excel(path, range = "E3:F12") %>% janitor::clean_names()
test  = read_excel(path, range = "H3:I12")

lookup = input2 %>%
  separate_rows(sub_dept, sep = ", ") %>%
  left_join(input1, by = "sub_dept") %>%
  replace_na(list(x000 = 0)) %>%
  summarise(total = sum(x000), .by = department)

all.equal(test$total, lookup$Total, check.attributes = FALSE)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the correct grouping level

  • Strengths:

    • The R solution stays compact and mirrors the workbook logic closely.

  • Areas for Improvement:

    • The code assumes the workbook layout and named ranges remain stable.

  • Gem:

    • The best part of the solution is choosing a tidy intermediate shape before producing the final answer.
import pandas as pd

path = "files/Excel Challenge September 22nd.xlsx"

input1 = pd.read_excel(path, usecols="B:C", skiprows=2, nrows=8, names=["sub_dept", "money"])
input2 = pd.read_excel(path, usecols="E:F", skiprows=2, nrows=9, names=["department", "sub_dept"])
test = pd.read_excel(path, usecols="H:I", skiprows=2, nrows=9, names=["department", "total"])\
    .sort_values(by=['total', "department"], ascending=False)\
    .reset_index(drop=True)

lookup = (input2.assign(sub_dept=input2['sub_dept'].str.split(', '))
          .explode('sub_dept')
          .merge(input1, on='sub_dept', how='left')
          .groupby('department', as_index=False)['money'].sum()
          .sort_values(by=['money', 'department'], ascending=False)
          .reset_index(drop=True))
lookup['money'] = lookup['money'].astype('int64')
lookup.columns = test.columns

print(lookup.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the correct grouping level

    • Builds the intermediate helper columns that drive the final answer

  • Strengths:

    • The Python version keeps the same rule in a direct pandas-oriented workflow.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the stated challenge instead of adding unnecessary complexity.

Difficulty Level

This task is easy to moderate:

  • The business rule is readable, but the workbook still needs a few careful transformation steps.