Omid - Challenge 98

data-challenges
advanced-exercises
🔰 In the Question table, historical sales values are provided in a single cell, including the Date, Product Name, and Quantity, but in a disorganized order.
Published

March 24, 2026

Illustration for Omid - Challenge 98

Challenge Description

🔰 In the Question table, historical sales values are provided in a single cell, including the Date, Product Name, and Quantity, but in a disorganized order.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-98 Data Cleaning.xlsx"
input = read_excel(path, range = "B2:B9")
test  = read_excel(path, range = "D2:F9")

pattern_date = "(\\d{4}\\/\\d{2}\\/\\d{2})"
pattern_num = "(\\s\\d{1,2}\\s|^\\d{1,2}\\s|\\s\\d{1,2}$)"
pattern_let = "([A-Z]+)"

result = input %>%
  mutate(Description = str_remove_all(Description, ","),
         Date = str_extract(Description, pattern_date) %>% as.POSIXct(., tz = "UTC"),
         Product = str_extract(Description, pattern_let),
         Quantity = str_extract(Description, pattern_num) %>% as.numeric()) %>%
  select(-Description)

all.equal(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

path = "CH-98 Data Cleaning.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1)
test = pd.read_excel(path, usecols="D:F", skiprows=1)

input = input["Description"].str.split(", ", expand=True)
result = pd.DataFrame(columns=["Date", "Product", "Quantity"])

for i in range(len(input)):
    temp = input.iloc[i]
    date = None
    product = None
    quantity = None

    for j in range(temp.size):
        if "/" in temp[j]:
            date = temp[j]
        elif temp[j].isalpha():
            product = temp[j]
        elif temp[j].isdigit():
            quantity = temp[j]
        else:
            if any(char.isalpha() for char in temp[j]):
                product = temp[j]
            if any(char.isdigit() for char in temp[j]):
                quantity = ''.join(filter(str.isdigit, temp[j]))

    result = result._append({"Date": date, "Product": product, "Quantity": quantity}, ignore_index=True)

result["Date"] = pd.to_datetime(result["Date"])
result["Quantity"] = pd.to_numeric(result["Quantity"])
result["Product"] = result["Product"].str.strip()

print(result.equals(test))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Applies the rule iteratively until the output stabilizes

  • 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.