Omid - Challenge 328

data-challenges
advanced-exercises
🔰 The Level column in the question table is created by grouping rows with the same ID, which results in some repeated text values.
Published

March 24, 2026

Illustration for Omid - Challenge 328

Challenge Description

🔰 The Level column in the question table is created by grouping rows with the same ID, which results in some repeated text values.

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/328/CH-328 Text Cleaning.xlsx"
input <- read_excel(path, range = "B2:B9")
test  <- read_excel(path, range = "D2:D9")

result = input %>%
  mutate(Level = Level %>%
           str_replace_all("Ground", "Ground,") %>%
           str_replace_all(",$", "") %>%
           str_split(", ") %>%
           map_chr(~ paste(unique(.x), collapse = ", ")))

  • 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 = "300-399\\328\\CH-328 Text Cleaning.xlsx"
input = pd.read_excel(path, usecols="B", nrows = 8, skiprows = 1)
test = pd.read_excel(path, usecols="D", nrows = 8, skiprows = 1); test.columns = [col.replace('.1', '') for col in test.columns]

result = (
    input.assign(
        Level=(
            input["Level"]
            .str.replace("Ground", "Ground,", regex=False)
            .str.replace(",$", "", regex=True)
            .str.split(", ")
            .map(lambda x: ", ".join(pd.unique(pd.Series(x))))
        )
    )
)

print(result.equals(test))
# 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

    • 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.