Omid - Challenge 1

data-challenges
advanced-exercises
🔰 Our objective is to calculate the sales figures for these products between different For example, in the Question table , the cumulative sales for Product A on the dates…
Published

March 24, 2026

Illustration for Omid - Challenge 1

Challenge Description

🔰 Our objective is to calculate the sales figures for these products between different For example, in the Question table , the cumulative sales for Product A on the dates…

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-001.xlsx"
input = read_excel(path, range = "B2:E9")
test  = read_excel(path, range = "K2:N13")

result = input %>%
  pivot_longer(-Date, names_to = "Product", values_to = "Value") %>%
  drop_na() %>%
  arrange(Product, Date) %>%
  mutate(date_1 = lag(Date, 1, default = as.Date("2024-01-01")),
         diff = Value - lag(Value, 1, default = 0),
         .by = Product) %>%
  separate(Product, into = c("Product", "Type"), sep = " ") %>%
  select(From = date_1, To = Date, Product = Type, diff) %>%
  arrange(From, Product)
  
all.equal(result, test, check.attributes = FALSE)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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-001.xlsx"
input_data = pd.read_excel(path, usecols="B:E", skiprows=1, nrows=8)
test = pd.read_excel(path, usecols="K:N", skiprows=1, nrows=12)

result = (
    input_data.melt(id_vars="Date", var_name="Product", value_name="Value")
    .dropna(subset=["Value"])
    .sort_values(["Product", "Date"])
)
result["From"] = result.groupby("Product")["Date"].shift(fill_value=pd.Timestamp("2024-01-01"))
result["diff"] = result.groupby("Product")["Value"].diff().fillna(result["Value"])
result[["Product Name", "Product"]] = result["Product"].str.rsplit(" ", n=1, expand=True)
result = (
    result[["From", "Date", "Product", "diff"]]
    .rename(columns={"Date": "To"})
    .sort_values(["From", "Product"])
    .reset_index(drop=True)
)

print(result.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.