Crispo - Excel Challenge 32 2024

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

August 11, 2024

Illustration for Crispo - Excel Challenge 32 2024

Challenge Description

Easy Sunday Excel Challenge

⭐ ⭐Sort Cookies into 2 groups: with Duplicate Price & with Unique prices

Solutions

library(tidyverse)
library(readxl)

path = "files/Excel Challenge 11th August.xlsx"
input = read_excel(path, range = "B2:C14")
test  = read_excel(path, range = "E2:F9") 
dup_test =  pull(test, `Duplicate Price`) %>% sort()
uniq_test = pull(test, `Unique Price`) %>% sort()

result = input %>%
  mutate(unique = n(), .by = Price) %>%
  mutate(uniqueness = if_else(unique == 1, "Unique Price", "Duplicata Price")) %>%
  select(Cookies, uniqueness)

dup_result = filter(result, uniqueness == "Duplicata Price") %>% pull(Cookies) %>% sort()
uniq_result = filter(result, uniqueness == "Unique Price") %>% pull(Cookies) %>% sort()

identical(dup_test, dup_result) && identical(uniq_test, uniq_result)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds the intermediate helper columns that drive the final answer

  • 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 11th August.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1)
test  = pd.read_excel(path, usecols="E:F", skiprows=1, nrows = 7)

input['Count'] = input.groupby('Price')['Price'].transform('count')

dupes = input["Cookies"][input['Count'] > 1].reset_index(drop=True)
unique = input["Cookies"][input['Count'] == 1].reset_index(drop=True)

print(sorted(dupes) == sorted(test["Duplicate Price"]) and sorted(unique) == sorted(test["Unique Price"][0:5]))

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the correct grouping level

  • 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.