Omid - Challenge 55

data-challenges
advanced-exercises
🔰 In this challenge, we want to calculate the net quantity of items sent per product each year by neglecting the returns products.
Published

March 24, 2026

Illustration for Omid - Challenge 55

Challenge Description

🔰 In this challenge, we want to calculate the net quantity of items sent per product each year by neglecting the returns products.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-055 Warehouse Management.xlsx", range = "B2:E19")
test  = read_excel("files/CH-055 Warehouse Management.xlsx", range = "H2:K5")

result = input %>%
  mutate(Year = year(Date)) %>%
  summarise(Year = min(Year),
            Quantity = sum(Quantity),
            .by = c("Order No", "Product")) %>%
  select(-`Order No`) %>%
  pivot_wider(names_from  = Year, values_from = Quantity, values_fn = sum) %>%
  mutate(across(everything(), ~ifelse(. == 0, NA, .))) %>%
  arrange(Product)

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
import numpy as np

input = pd.read_excel("CH-055 Warehouse Management.xlsx", usecols="B:E", skiprows=1)
test = pd.read_excel("CH-055 Warehouse Management.xlsx", usecols="H:K", skiprows=1, nrows= 3)

result = input.copy()
result["Year"] = result["Date"].dt.year
result = result.groupby(["Order No", "Product"]).agg({"Yea                                                                                              2r": "min","Quantity": "sum"}).reset_index()
result.drop(columns=["Order No"], inplace=True)
result = result.pivot_table(index="Product", columns="Year", values="Quantity", aggfunc="sum", fill_value=0).reset_index()

test.columns = result.columns
result.iloc[:, 1:] = result.iloc[:, 1:].astype(float)
result.iloc[:, 1:] = result.iloc[:, 1:].replace(0, np.NaN)

print(result.equals(test))  # 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.