Omid - Challenge 298

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 298

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/298/CH-298 Table Transformation.xlsx"
input = read_excel(path, range = "B2:C5")
test  = read_excel(path, range = "E2:F26")

result = input %>%
  mutate(
    Date = str_extract_all(Date, "\\d{1,2}/\\d{1,2}/\\d{4}") %>%
      map(~ {
        seqs <- mdy(.x)
        seq(seqs[1], seqs[2], by = "day")
      })) %>%
  mutate(n = map_int(Date, length),
         `Daily Sale` = `Total Sales` / n) %>%
  unnest() %>%
  select(Date, `Daily Sale`)

# Result not equal because of mistake in provided data.

  • 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 = "200-299/298/CH-298 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=3)
test = pd.read_excel(path, usecols="E:F", skiprows=1, nrows=25)

dates = input["Date"].str.extractall(r"(\d{1,2}/\d{1,2}/\d{4})")[0].unstack()
input['start'] = dates[0]
input['end'] = dates[1]
input['date_seq'] = input.apply(lambda row: pd.date_range(row['start'], row['end']).strftime('%Y-%m-%d').tolist(), axis=1)
input['seq_length'] = input.apply(lambda row: len(pd.date_range(row['start'], row['end'])), axis=1)
input['Daily Sale'] = input['Total Sales'] / input['seq_length']
input = input[['date_seq', 'Daily Sale']]
result = input.explode('date_seq').reset_index(drop=True)

# result is not equal because of mistake in data provided
# expected = pd.DataFrame({

  • 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.