Crispo - Excel Challenge 07 2025

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

February 16, 2025

Illustration for Crispo - Excel Challenge 07 2025

Challenge Description

Easy Sunday Excel Challenge

⭐ Groups ⭐Group the staff and insert the group name after last count ⭐The end of a group is always before No. 1 ⭐The last group has only 1 staff GROUP 1 GROUP 2

Solutions

library(tidyverse)
library(readxl)

path = "files/Ex-Challenge 07 2025.xlsx"
input = read_excel(path, range = "B3:B14")
test  = read_excel(path, range = "D3:D18")

result = input %>%
  mutate(group = cumsum(c(1, diff(Staff) < 0)),
         Staff  = as.character(Staff)) %>%
  group_by(group) %>%
  group_split() %>%
  imap_dfr(~ {
    dynamic_row <- tibble(
      Staff = paste("GROUP", .y),
      group = unique(.x$group)
    )
    bind_rows(.x, dynamic_row)
  }) %>%
  select(Groups = Staff)

all.equal(result, test)
# [1] 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 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/Ex-Challenge 07 2025.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=2, nrows=11)
test = pd.read_excel(path, usecols="D", skiprows=2, nrows=15).astype(str)

input['group'] = (input['Staff'].diff() < 0).cumsum() + 1
input['Staff'] = input['Staff'].astype(str)

result = input.groupby('group', group_keys=False).apply(
    lambda x: x._append(pd.DataFrame({'Staff': [f"GROUP {x.name}"]}))
).reset_index(drop=True)[['Staff']].rename(columns={'Staff': 'Groups'})

print(result.equals(test))

  • 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 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.