Omid - Challenge 29

data-challenges
advanced-exercises
🔰 Question Result A B C Product Quantity Date
Published

March 24, 2026

Illustration for Omid - Challenge 29

Challenge Description

🔰 Question Result A B C Product Quantity Date

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-029 Identifying Customers Staple Products.xlsx", range = "B2:E36")
test  = read_excel("files/CH-029 Identifying Customers Staple Products.xlsx", range = "I2:J6")

result = input %>%
  summarise(total_quantity = sum(Quantity), .by = c("Customer ID", "Product")) %>%
  group_by(`Customer ID`) %>%
  mutate(rank = rank(-total_quantity),
         lowest_rank = min(rank)) %>%
  filter(rank == lowest_rank) %>%
  summarise(`Most Purchased PRODUCT` = paste0(sort(Product), collapse = ","))

identical(result$`Most Purchased PRODUCT`, test$`Most Purchased PRODUCT`)
# [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

input = pd.read_excel("CH-029 Identifying Customers Staple Products.xlsx", sheet_name="Sheet1", usecols="B:E", skiprows=1)
test = pd.read_excel("CH-029 Identifying Customers Staple Products.xlsx", sheet_name="Sheet1", usecols="I:J", skiprows=1, nrows = 4)

result = input.groupby(["Customer ID", "Product"]).agg(total_quantity=("Quantity", "sum")).reset_index()
result["rank"] = result.groupby("Customer ID")["total_quantity"].rank(ascending=False)
result["lowest_rank"] = result.groupby("Customer ID")["rank"].transform("min")
result = result[result["rank"] == result["lowest_rank"]]
result = result.groupby("Customer ID").agg({"Product": lambda x: ",".join(sorted(x))}).reset_index()
result = result.rename(columns={"Product": "Most Purchased PRODUCT"})

print(result["Most Purchased PRODUCT"].equals(test["Most Purchased PRODUCT"])) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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 core logic is clear, but the correct transformation pattern is not obvious from the raw input.

  • The challenge combines multiple reshaping, grouping, or parsing steps.