Omid - Challenge 172

data-challenges
advanced-exercises
🔰 For each hour, we aim to calculate the difference between its value and the value of the previous hour.
Published

March 24, 2026

Illustration for Omid - Challenge 172

Challenge Description

🔰 For each hour, we aim to calculate the difference between its value and the value of the previous hour.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-172 Performance  Optimization.xlsx"
input = read_excel(path, range = "R2C2:R250000C3") 
test  = read_excel(path, range = "E2:F7")

tictoc::tic()
result = input %>%
  arrange(`Date Houre`) %>%
  slice_max(Value - lag(Value), n = 5) %>%
  arrange(desc(Value)) 
tictoc::toc()
# 0.01 to 0.05 sec elapsed in few attempts.

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

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • Strengths:

    • The R solution stays close to the workbook rule and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the sheet structure and source ranges remain stable.

  • Gem:

    • The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd
import time

path = "CH-172 Performance  Optimization.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=249999)
test = pd.read_excel(path, usecols="E:F", skiprows=1, nrows=5).rename(columns=lambda x: x.split('.')[0])

start_time = time.time()
input = input.sort_values(by="Date Houre").assign(Value_diff=lambda x: x['Value'] - x['Value'].shift(1))
result = input.nlargest(5, "Value_diff").sort_values(by="Value", ascending=False).reset_index(drop=True).drop(columns=["Value_diff"])
end_time = time.time()
print(f"Execution time: {end_time - start_time} seconds")
# Execution time: 0.0416 seconds
                                                            
print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • Strengths:

    • The Python version follows the same rule in a direct dataframe-oriented implementation.

  • Areas for Improvement:

    • The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.

  • Gem:

    • The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • The business rule is readable, but the workbook still requires careful implementation to reach the expected layout.