Omid - Challenge 102

data-challenges
advanced-exercises
🔰 In the historical sales table, identify and extract the dates where the sales value on that date is greater than the sales on the previous date.
Published

March 24, 2026

Illustration for Omid - Challenge 102

Challenge Description

🔰 In the historical sales table, identify and extract the dates where the sales value on that date is greater than the sales on the previous date.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-102 Compare Rows.xlsx"
input = read_excel(path, range = "B2:C26")
test  = read_excel(path, range = "G2:G14")

result = input %>%
  filter(Sales - lag(Sales) > 0) %>%
  select(Dates = Date)

identical(result, test)
# [1] TRUE

# Second approach (no comparison gte or lte)

result = input %>%
  filter(sign(Sales - lag(Sales)) == 1) %>%
  select(Dates = Date)
# [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 numpy as np

path = "CH-102 Compare Rows.xlsx"
input = pd.read_excel(path, usecols = "B:C", skiprows = 1)
test  = pd.read_excel(path, usecols = "G", skiprows = 1, nrows = 12)

result = input.where(input["Sales"] - input["Sales"].shift(1) > 0).dropna().reset_index(drop = True)
result = result["Date"].rename("Dates")
print(result.equals(test["Dates"])) # True

# II Aproach with no comparison using > or < operators

result = input.where(np.sign(input["Sales"] - input["Sales"].shift(1)) == 1).dropna().reset_index(drop = True)
result = result["Date"].rename("Dates")
print(result.equals(test["Dates"])) # True

# be careful and call only first or second approach. :D

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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.