Omid - Challenge 322

data-challenges
advanced-exercises
🔰 Question Table Level 01 Zone 2 level 01 zone 4 Level 1 Zone 3 North Level 2 Zone 3 Level 4 Ground Zone 2 Level
Published

March 24, 2026

Illustration for Omid - Challenge 322

Challenge Description

🔰 Question Table Level 01 Zone 2 level 01 zone 4 Level 1 Zone 3 North Level 2 Zone 3 Level 4 Ground Zone 2 Level

Solutions

library(tidyverse)
library(readxl)

path = "300-399/322/CH-322 Text Cleaning.xlsx"
input = read_excel(path, range = "B2:B9")
test  = read_excel(path, range = "D2:E9")

result = input %>%
  mutate(
    Level = str_extract(Info, "(?i)level\\s*(\\d+)") %>%
      str_extract("\\d+") %>%
      parse_number() %>%
      as.character() %>%
      coalesce(str_extract(Info, "(?i)ground")),
    Zone  = str_extract(Info, "(?i)zone\\s*(\\d+)") %>%
      str_extract("\\d+") %>%
      coalesce(str_extract(Info, "(?i)\\b(North|South|East|West)\\b")) %>%
      replace_na("-")
  )

all.equal(result %>% select(-Info), test)

  • 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 = "300-399/322/CH-322 Text Cleaning.xlsx"

input = pd.read_excel(path, usecols="B", skiprows=1, nrows=8)
test = pd.read_excel(path, usecols="D:E", skiprows=1, nrows=8)

result = (
    input
    .assign(
        Level=lambda df: df["Info"]
            .str.extract(r'(?i)level\s*0*(\d+)')[0]  # Remove leading zeros
            .combine_first(df["Info"].str.extract(r'(?i)(ground)')[0])
            .apply(lambda x: int(x) if pd.notnull(x) and re.fullmatch(r'\d+', str(x)) else x),
        Zone=lambda df: df["Info"]
            .str.extract(r'(?i)zone\s*(\d+)')[0]
            .apply(lambda x: int(x) if pd.notnull(x) and re.fullmatch(r'\d+', str(x)) else x)
            .fillna(df["Info"].str.extract(r'(?i)\b(North|South|East|West)\b')[0])
            .fillna("-")
    )
    .drop(columns=["Info"])
)

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

  • 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.