Omid - Challenge 64

data-challenges
advanced-exercises
đŸ”° In the Question table, historical sales values are provided in a single cell, including the Date, Product Name, and Quantity, with a default value of 1 for missing quant…
Published

March 24, 2026

Illustration for Omid - Challenge 64

Challenge Description

đŸ”° In the Question table, historical sales values are provided in a single cell, including the Date, Product Name, and Quantity, with a default value of 1 for missing quant…

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-064 Text Cleaning.xlsx", range = "B2:B9")
test  = read_excel("files/CH-064 Text Cleaning.xlsx", range = "D2:F14")
test$Date = as.character(as.Date(test$Date))

result = input %>%
  mutate(date = str_split_fixed(Description, ", ", 2),
         Date = date[,1],
         Product = date[,2]) %>%
  select(Date, Product) %>%
  separate_longer_delim(Product, delim = ", ") %>%
  separate(Product, into = c("Product", "Quantity"), sep = " ") %>%
  mutate(Quantity = as.numeric(Quantity),
         Date = str_replace_all(Date, "/", "-")) %>%
  replace_na(list(Quantity = 1))

identical(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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

input = pd.read_excel("CH-064 Text Cleaning.xlsx", usecols="B", skiprows=1, nrows = 7)
test = pd.read_excel("CH-064 Text Cleaning.xlsx", usecols="D:F", skiprows = 1, nrows = 12)
test['Date'] = test['Date'].astype(str)

result = input.copy()
result[['Date', 'Product']] = result['Description'].str.split(", ", n=1, expand=True)
result['Product'] = result['Product'].str.split(", ")
result = result.explode('Product')
result[['Product', 'Quantity']] = result['Product'].str.split(" ", n=1, expand=True)
result['Quantity'] = pd.to_numeric(result['Quantity'])
result['Date'] = result['Date'].str.replace("/", "-")
result['Quantity'].fillna(1, inplace=True)
result['Quantity'] = result['Quantity'].astype('int64')
result.drop('Description', axis=1, inplace=True)a
result = result.reset_index(drop=True)

print(test.equals(result)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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.