Omid - Challenge 327

data-challenges
advanced-exercises
🔰 Question Table Level Zone Ground North Info Result Table Upper Ground
Published

March 24, 2026

Illustration for Omid - Challenge 327

Challenge Description

🔰 Question Table Level Zone Ground North Info Result Table Upper Ground

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/327/CH-327 Column Splitting.xlsx"
input <- read_excel(path, range = "B2:B9")
test  <- read_excel(path, range = "D2:E9")

levels = c("Upper Ground", "Ground", "Under Ground")
Zones = c("West", "East", "North", "South", "South East", "North West")

result = input %>%
  rowwise() %>%
  mutate(
    Level = str_extract_all(Info, paste0(levels, collapse = "|")),
    Zone = str_extract_all(Info, paste0(Zones, collapse = "|")),
    Zone = if (length(Zone) > 1) paste(Zone, collapse = " ") else as.character(Zone)
  ) %>%
  ungroup() %>%
  unnest(Level) %>%
  select(-Info)

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
import re

path = "300-399/327/CH-327 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", nrows = 8, skiprows = 1)
test = pd.read_excel(path, usecols= "D:E", nrows = 8, skiprows = 1)

level_pattern = "|".join(map(re.escape, (levels := ["Upper Ground", "Ground", "Under Ground"])))
zone_pattern = "|".join(map(re.escape, (levels :=  ["West", "East", "North", "South", "South East", "North West"])))

df = input.copy()
df["Level"] = df["Info"].apply(lambda info: re.findall(level_pattern, str(info)))
df["Zone"] = df["Info"].apply(
    lambda info: (
        " ".join(zones) if len((zones := re.findall(zone_pattern, str(info)))) > 1
        else zones[0] if zones
        else None
    )
)
df = df.explode("Level")
df = df.drop(columns=["Info"])

print(df.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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.