Crispo - Excel Challenge 28 2024

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

July 14, 2024

Illustration for Crispo - Excel Challenge 28 2024

Challenge Description

Easy Sunday Excel Challenge

⭐ Problem Solution Date Customers Orders Aiden

Solutions

library(tidyverse)
library(readxl)

path = "files/Excel Challenge 14th July.xlsx"
input = read_xlsx(path, range = "B2:D8")
test  = read_xlsx(path, range = "F2:L8")

result = input %>%
  separate_rows(c(Customers, Orders), sep = "; ") %>%
  pivot_wider(names_from = Customers, values_from = Orders) %>%
  mutate(across(-c(1), ~as.numeric(.)))

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

  • Logic:

    • 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/Excel Challenge 14th July.xlsx"
input = pd.read_excel(path, usecols="B:D", skiprows=1, nrows = 6)
test = pd.read_excel(path, usecols="F:L", skiprows=1, nrows = 6)
test.columns = test.columns.str.replace(".1", "")

result = input.assign(Customers=input.Customers.str.split("; "),
                     Orders=input.Orders.str.split("; ")) \
    .explode(["Customers", "Orders"]) \
    .pivot(index="Date", columns="Customers", values="Orders") \
    .reset_index()

result[result.columns[1:]] = result[result.columns[1:]].apply(pd.to_numeric).fillna(" ")
result.columns.name = None
test = test.fillna(" ")

print(result.equals(test)) # 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 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.