Crispo - Excel Challenge 46 2025

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

November 16, 2025

Illustration for Crispo - Excel Challenge 46 2025

Challenge Description

Easy Sunday Excel Challenge

⭐ Easy Sunday Excel Challenge

Solutions

library(tidyverse)
library(readxl)

# path <- "path/to/your/file.xlsx" 
# input <- read_excel(path, range = "")
# test  <- read_excel(path, range = "")
# to be replaced when file willbe provided

df <- tribble(
  ~Stock,                                        ~Seller,
  "Spoon Area-2 - Kitchen Item",                 "Aiden",
  "Forks - Dining Item : Silver Colour",         "Adrian",
  "Sofa: Recliner Area-5 - Lounge Item",         "Ericka",
  "King-Bed:Wide - Bedroom Item :Black-Lable",   "Emma"
)
test = tribble(
  ~`Item & Seller`,
  "Kitchen Item: Aiden",        
  "Dining Item: Adrian",        
  "Lounge Item: Ericka",        
  "Bedroom Item: Emma"  
)

result <- df %>%
  mutate(
    Item = str_extract(Stock, "([[:alnum:]_]+)\\s*Item"
  )) %>%
  transmute(`Item & Seller` = glue::glue("{Item}: {Seller}")) %>% 
  mutate(`Item & Seller` = as.character(`Item & Seller`))

all.equal(result, test, check.attributes = FALSE)

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds the intermediate helper columns that drive the final answer

    • Uses direct text-pattern extraction instead of manual cleanup

  • 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
df = pd.DataFrame({
    "Stock": [
        "Spoon Area-2 - Kitchen Item",
        "Forks - Dining Item : Silver Colour",
        "Sofa: Recliner Area-5 - Lounge Item",
        "King-Bed:Wide - Bedroom Item :Black-Lable"
    ],
    "Seller": ["Aiden", "Adrian", "Ericka", "Emma"]
})

test = pd.DataFrame({
    "Item & Seller": [
        "Kitchen Item: Aiden",
        "Dining Item: Adrian",
        "Lounge Item: Ericka",
        "Bedroom Item: Emma"
    ]
})

df["Item"] = df["Stock"].str.extract(r"([A-Za-z0-9\-]+) Item")[0] + " Item"
result = pd.DataFrame({
    "Item & Seller": df["Item"] + ": " + df["Seller"]
})

print(result.equals(test)) # True

  • Logic:

    • Applies the workbook rule directly and shapes the expected output

  • 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 moderate:

  • It combines familiar Excel-style logic with at least one non-trivial reshape, grouping, or parsing step.

  • The answer depends on getting the output layout exactly right.