Omid - Challenge 228

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 228

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/228/CH-228 Table Transformation.xlsx"
input = read_excel(path, range = "B2:H5")
test = read_excel(path, range = "F8:H26")

result = input %>%
  pivot_longer(cols = -Product, names_to = "Date", values_to = "Price") %>%
  replace_na(list(Price = 0)) %>%
  mutate(Date = str_extract(Date, "\\d{1,2}/\\d{2}/\\d{4}") %>% dmy()) %>%
  mutate(Price = cumsum(Price), .by = Product) %>%
  select(Date, Product, Price)

  • 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

    • Parses the text patterns directly instead of relying on manual cleanup

  • 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 re
from datetime import datetime

path = "200-299/228/Ch-228 Table Transformation.xlsx"

input = pd.read_excel(path, skiprows=1, usecols="B:H", nrows = 3)
test = pd.read_excel(path, skiprows=7, usecols="F:H", nrows=18)
test.columns = ["Date", "Product", "Price"]
test["Date"] = test["Date"].apply(
    lambda x: datetime.strptime(re.search(r"\d{1,2}/\d{2}/\d{4}", str(x)).group(), "%d/%m/%Y")
    if re.search(r"\d{1,2}/\d{2}/\d{4}", str(x)) else x
)

input_long = input.melt(id_vars=["Product"], var_name="Date", value_name="Price")
input_long["Date"] = input_long["Date"].apply(
    lambda x: datetime.strptime(re.search(r"\d{1,2}/\d{2}/\d{4}", str(x)).group(), "%d/%m/%Y")
    if re.search(r"\d{1,2}/\d{2}/\d{4}", str(x)) else x
)
input_long = input_long.sort_values(by=["Product", "Date"]).fillna(0).reset_index(drop=True)
input_long["Price"] = input_long.groupby("Product")["Price"].cumsum().astype("int64")
input_long = input_long[["Date", "Product", "Price"]]

print(input_long['Price'].equals(test['Price'])) # 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

    • Parses the text patterns directly instead of relying on manual cleanup

  • 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.