Omid - Challenge 96

data-challenges
advanced-exercises
🔰 In the Question table, daily sales information is provided.
Published

March 24, 2026

Illustration for Omid - Challenge 96

Challenge Description

🔰 In the Question table, daily sales information is provided.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-96 Top Products.xlsx"
input = read_excel(path, range = "B2:D20")
test  = read_excel(path, range = "I2:K11")

result = input %>%
  mutate(Month = month(Date)) %>%
  summarise(Quantity = sum(Quantity), .by = c(Product, Month)) %>%
  mutate(Rank = dense_rank(desc(Quantity)), .by = Month) %>%
  mutate(Product = ifelse(Rank < 3, Product, "Other")) %>%
  arrange(Month, Rank) %>%
  summarise(Quantity = sum(Quantity), .by = c(Product, Month)) %>%
  mutate(`% of Month sales` = Quantity / sum(Quantity), .by = Month) %>%
  select(Month, Product, `% of Month sales`)

identical(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

  • 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

path = "CH-96 Top Products.xlsx"
input = pd.read_excel(path, skiprows=1, usecols="B:D", nrows=19)
test = pd.read_excel(path, skiprows=1, usecols="I:K", nrows=9)\
    .rename(columns={"Product.1":"Product", "% of Month sales":"Percent"})\
    .sort_values(["Month","Product"])\
    .reset_index(drop=True)

result = input.assign(Month=input['Date'].dt.month) \
    .groupby(['Product', 'Month']) \
    .agg(Quantity=('Quantity', 'sum')) \
    .reset_index() \
    .assign(Rank=lambda x: x.groupby('Month')['Quantity'].rank(method='dense', ascending=False)) \
    .assign(Product=lambda x: x.apply(lambda row: row['Product'] if row['Rank'] < 3 else 'Other', axis=1)) \
    .sort_values(['Month', 'Rank']) \
    .groupby(['Product', 'Month']) \
    .agg(Quantity=('Quantity', 'sum')) \
    .sort_values(["Month","Product"])\
    .reset_index() \
    .assign(Percent=lambda x: x['Quantity'] / x.groupby('Month')['Quantity'].transform('sum')) \
    .loc[:, ['Month', 'Product', 'Percent']]

print(all(result == test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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.