Omid - Challenge 338

data-challenges
advanced-exercises
🔰 Question Result F ID XMS 1 M AA
Published

March 24, 2026

Illustration for Omid - Challenge 338

Challenge Description

🔰 Question Result F ID XMS 1 M AA

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/338/CH-338 Column Splitting.xlsx"
input <- read_excel(path, range = "B3:B8")
test <- read_excel(path, range = "F3:K8")

add_splitter_on_char_change <- function(x) {
  stringr::str_replace_all(
    x,
    "(?<=[A-Za-z])(?=[^A-Za-z])|(?<=[^A-Za-z])(?=[A-Za-z])|
     (?<=[0-9])(?=[^0-9])|(?<=[^0-9])(?=[0-9])|
     (?<=[[:punct:]])(?=[^[:punct:]])|(?<=[^[:punct:]])(?=[[:punct:]])",
    "|"
  )
}

result = input %>%
  mutate(ID = map(ID, ~ add_splitter_on_char_change(.))) %>%
  separate_wider_delim(
    ID,
    delim = "|",
    names_sep = " ",
    too_few = "align_start"
  )

all.equal(result, 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\\338\\CH-338 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", nrows=5, skiprows=2)
test = pd.read_excel(path, usecols="F:K", nrows=5, skiprows=2)

colname = input.columns[0]

def add_splitter_on_char_change(x):
    pattern = (
        r"(?<=[A-Za-z])(?=[^A-Za-z])|(?<=[^A-Za-z])(?=[A-Za-z])|"
        r"(?<=[0-9])(?=[^0-9])|(?<=[^0-9])(?=[0-9])|"
        r"(?<=[\W_])(?=[^\W_])|(?<=[^\W_])(?=[\W_])"
    )
    return re.sub(pattern, "|", str(x))

split_cols = input[colname].apply(add_splitter_on_char_change).str.split("|")
maxlen = split_cols.map(len).max()
colnames = [f"ID_{i+1}" for i in range(maxlen)]
result = pd.DataFrame(split_cols.tolist(), columns=colnames)

print(result.equals(test)) # one field incorrect in answers provided

  • 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.