Omid - Challenge 51

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

March 24, 2026

Illustration for Omid - Challenge 51

Challenge Description

🔰 Question Result A B C Product Quantity Date

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-051 Purchasing together.xlsx", range = "B2:F26")
test= read_excel("files/CH-051 Purchasing together.xlsx", range = "J2:K7")

result = input %>%
  select(`Invoice ID`, Product, Quantity) %>%
  pivot_wider(names_from = Product, values_from = Quantity, values_fill = list(Quantity = 0)) %>%
  mutate(`A,B` = ifelse(A>0 & B>0, T, F),
         `A,C` = ifelse(A>0 & C>0, T, F),
         `C,D` = ifelse(C>0 & D>0, T, F),
         `A,B,C` = ifelse(A>0 & B>0 & C>0, T, F),
         `A,B,C,D` = ifelse(A>0 & B>0 & C>0 & D>0, T, F)) %>%
  select(`Invoice ID`, `A,B`, `A,C`, `C,D`, `A,B,C`, `A,B,C,D`) %>%
  pivot_longer(cols = -`Invoice ID`, names_to = "Products", values_to = "Purchased") %>%
  filter(Purchased == T) %>% 
  summarise(Count = n() %>% as.numeric(), .by = Products)

identical(result, test)  
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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-051 Purchasing together.xlsx", sheet_name="Sheet1", usecols="B:F", skiprows=1)
test = pd.read_excel("CH-051 Purchasing together.xlsx", sheet_name="Sheet1", usecols="J:K", skiprows=1, nrows = 5)
test = test.sort_values(by = "Products").reset_index(drop = True)

result = input[['Invoice ID', 'Product', 'Quantity']].pivot_table(index='Invoice ID', columns='Product', values='Quantity', fill_value=0)
result['A,B'] = (result['A'] > 0) & (result['B'] > 0)
result['A,C'] = (result['A'] > 0) & (result['C'] > 0)
result['C,D'] = (result['C'] > 0) & (result['D'] > 0)
result['A,B,C'] = (result['A'] > 0) & (result['B'] > 0) & (result['C'] > 0)
result['A,B,C,D'] = (result['A'] > 0) & (result['B'] > 0) & (result['C'] > 0) & (result['D'] > 0)
result = result[['A,B', 'A,C', 'C,D', 'A,B,C', 'A,B,C,D']].reset_index() 
result.columns.name = None
result = result.melt(id_vars='Invoice ID', var_name='Products', value_name='Purchased Together')
result = result[result['Purchased Together'] == True]
result = result.groupby('Products').size().reset_index(name='Count')

print(test.equals(result)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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.