Crispo - Excel Challenge 24 2024

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

June 16, 2024

Illustration for Crispo - Excel Challenge 24 2024

Challenge Description

Easy Sunday Excel Challenge

⭐ Date & Orders Month Closing Order Easy Sunday Excel Challenge ⭐Get the last order for each Month marks for Legacy solutions or PowerQuery Solution

Solutions

library(tidyverse)
library(readxl)

path = "files/Excel Challenge 16th June.xlsx"
input = read_excel(path, range = "B2:B22")
test  = read_excel(path, range = "D2:E5")

result = input %>%
  as.list() %>%
  unlist() %>%
  matrix(ncol = 2, byrow = TRUE) %>%
  as_tibble() %>%
  setNames(c("Date", "Value")) %>%
  mutate(Date = as.Date(Date, origin = "1899-12-30"),
         Month = month(Date)) %>%
  summarise(`Closing Order` = last(Value), .by = Month) 

identical(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
import numpy as np

path = "files/Excel Challenge 16th June.xlsx"
input = pd.read_excel(path, skiprows=1, usecols="B")
test = pd.read_excel(path, skiprows=1, usecols="D:E", nrows = 3)

result = pd.DataFrame(input.values.reshape(-1, 2), columns=['A', 'B'])
result['month'] = result['A'].dt.month
result = result.groupby('month').tail(1)
result = result[['month', 'B']].reset_index(drop=True)
result = result.astype('int64')

result.columns = test.columns

print(result.equals(test)) # True

  • 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 easy to moderate:

  • The business rule is readable, but the workbook still needs a few careful transformation steps.