Omid - Challenge 8

data-challenges
advanced-exercises
🔰 Calculate Average Inventory Level!
Published

March 24, 2026

Illustration for Omid - Challenge 8

Challenge Description

🔰 Calculate Average Inventory Level!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-008.xlsx"
input = read_excel(path, range = "B2:E19")
test  = read_excel(path, range = "I2:L4")

result = input %>%
  arrange(Product, Date) %>%
  mutate(rn = row_number(), .by = Product) %>%
  complete(Date = seq(min(Date), max(Date), by = "day"),
           Product = c("A", "B", "C")) %>%
  mutate(sign = ifelse(Type == "Reduce", -1, 1),
         Month = month(Date),
         Quantity = Quantity * sign) %>%
  replace_na(list(Quantity = 0)) %>%
  ungroup() %>%
  arrange(Product, Date) %>%
  fill(rn, .direction = "down") %>%
  mutate(cumsum = cumsum(Quantity), .by = Product) %>%
  summarise(days = n_distinct(Date), .by = c(Product, rn, Month, cumsum)) %>%
  summarise(weighted_value = sum(cumsum * days)/sum(days), .by = c(Product, Month)) %>%
  pivot_wider(names_from = Product, values_from = weighted_value)

  • 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

path = "CH-008.xlsx"
input_data = pd.read_excel(path, usecols="B:E", skiprows=1, nrows=18)
test = pd.read_excel(path, usecols="I:L", skiprows=1, nrows=3)

input_data["Date"] = pd.to_datetime(input_data["Date"])
input_data = input_data.sort_values(["Product", "Date"]).reset_index(drop=True)
input_data["rn"] = input_data.groupby("Product").cumcount() + 1

full_index = pd.MultiIndex.from_product(
    [pd.date_range(input_data["Date"].min(), input_data["Date"].max(), freq="D"), ["A", "B", "C"]],
    names=["Date", "Product"],
)
result = full_index.to_frame(index=False).merge(input_data, on=["Date", "Product"], how="left")
result["sign"] = result["Type"].map({"Reduce": -1}).fillna(1)
result["Month"] = result["Date"].dt.month
result["Quantity"] = result["Quantity"] * result["sign"]
result["Quantity"] = result["Quantity"].fillna(0)
result = result.sort_values(["Product", "Date"])
result["rn"] = result.groupby("Product")["rn"].ffill()
result["cumsum"] = result.groupby("Product")["Quantity"].cumsum()
days = (
    result.groupby(["Product", "rn", "Month", "cumsum"], dropna=False)["Date"]
    .nunique()
    .reset_index(name="days")
)
result2 = (
    days.groupby(["Product", "Month"], as_index=False)
    .apply(lambda g: pd.Series({"weighted_value": (g["cumsum"] * g["days"]).sum() / g["days"].sum()}))
    .reset_index(drop=True)
    .pivot(index="Month", columns="Product", values="weighted_value")
    .reset_index(drop=True)
)

print(result2.equals(test))

  • 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.