Omid - Challenge 308

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 308

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/300-399/308/CH-308 Table Transformation.xlsx"
input = read_excel(path, range = "B2:B3")
test  = read_excel(path, range = "B6:C29")

result = input %>%
  mutate(all = str_extract_all(Info, "\\d{4}/\\d{1}/\\d{1,2}/\\d{2}")) %>%
  unnest(all) %>%
  select(-Info) %>%
  separate(all, into = c("Year", "Month", "Day", "Sale"), sep = "/", convert = TRUE) %>%
  mutate(Date = as.POSIXct(sprintf("%04d-%02d-%02d", Year, Month, Day), tz = "UTC")) %>%
  select(Date, Sale)

all.equal(result, test, check.attributes = FALSE)

  • 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

path = "300-399/308/CH-308 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=2)
test = pd.read_excel(path, usecols="B:C", skiprows=5, nrows=24)

all_extracted = sum(
    input['Info'].apply(
        lambda x: re.findall(r"\d{4}/\d{1}/\d{1,2}/\d{2}", str(x))
    ).tolist(),
    []
)
split = pd.DataFrame(
    [x.split('/') for x in all_extracted],
    columns=['Year', 'Month', 'Day', 'Sale']
).astype(int)
split['Date'] = pd.to_datetime(split[['Year', 'Month', 'Day']])
result = split[['Date', 'Sale']]

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

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

    • 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.