Crispo - Excel Challenge 36 2025

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

September 7, 2025

Illustration for Crispo - Excel Challenge 36 2025

Challenge Description

Easy Sunday Excel Challenge

⭐ ⭐Filter Customers Missing all Data

Solutions

library(tidyverse)
library(readxl)

path = "files/2025-09-07/Challenge 57.xlsx"
input = read_excel(path, range = "B2:D8")
test  = read_excel(path, range = "F2:K5")

result = input %>%
  separate_longer_delim(`Assigned Staff`, delim = ", ") %>%
  mutate(rn = row_number(), .by = `Assigned Staff`) %>%
  select(-Activity) %>%
  na.omit() %>%
  pivot_wider(names_from = `Assigned Staff`, values_from = `Activity Code`) %>%
  select(colnames(test))

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

    • 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/2025-09-07/Challenge 57.xlsx"
input = pd.read_excel(path, usecols="B:D", skiprows=1, nrows=6)
test  = pd.read_excel(path, usecols="F:K", skiprows=1, nrows=3)

input_long = (
    input.assign(**{'Assigned Staff': input['Assigned Staff'].str.split(', ')})
    .explode('Assigned Staff')
    .dropna(subset=['Assigned Staff'])
)
input_long['rn'] = input_long.groupby('Assigned Staff').cumcount() + 1
result = (
    input_long.pivot(index='rn', columns='Assigned Staff', values='Activity Code')
    .reindex(columns=test.columns)
    .reset_index(drop=True)
)
result.columns.name = None

print(result.equals(test)) # 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 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.