Crispo - Excel Challenge 23 2025

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

June 8, 2025

Illustration for Crispo - Excel Challenge 23 2025

Challenge Description

Easy Sunday Excel Challenge

⭐ ⭐Count and Merge Sales into one field ⭐Join the count using a comma

Solutions

library(tidyverse)
library(readxl)

path = "files/2025-06-08/Challenge 31.xlsx"
input = read_excel(path, range = "B3:D9")
test = read_excel(path, range = "F3:G5")

result = input %>%
  pivot_longer(cols = -c(1), names_to = "Store", values_to = "Fruit Sale") %>%
  mutate(count = 1) %>%
  summarise(count = sum(count, na.rm = T), .by = c("Store", "Fruit Sale")) %>%
  unite("Fruits Sale", c("Fruit Sale", "count"), sep = ": ") %>%
  summarise(
    `Fruits Sale` = paste(`Fruits Sale`, collapse = ", "),
    .by = "Store"
  )

all.equal(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data to the grain required by the task

    • Aggregates or ranks values at the correct grouping level

    • 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/2025-06-08/Challenge 31.xlsx"
input = pd.read_excel(path, usecols="B:D", skiprows=2, nrows=7)
test = pd.read_excel(path, usecols="F:G", skiprows=2, nrows=2)

df = input.melt(id_vars=input.columns[0], var_name="Store", value_name="Fruit Sale")
df["count"] = 1
grouped = df.groupby(["Store", "Fruit Sale"], dropna=False, as_index=False)["count"].sum()
grouped["Fruits Sale"] = grouped["Fruit Sale"].astype(str) + ": " + grouped["count"].astype(str)
result = grouped.groupby("Store", as_index=False)["Fruits Sale"].apply(lambda x: ", ".join(x)).reset_index(drop=True)

print(result.equals(test))
# Semantically result the same, different order of fruits.

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data to the grain required by the task

    • 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 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.