Omid - Challenge 349

data-challenges
advanced-exercises
🔰 Question Result ID XMS AA 21 ID 1 ID 2
Published

March 24, 2026

Illustration for Omid - Challenge 349

Challenge Description

🔰 Question Result ID XMS AA 21 ID 1 ID 2

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/349/CH-349 Column Splitting.xlsx"
input <- read_excel(path, range = "B3:B8")
test <- read_excel(path, range = "F3:K8")

result = input %>%
  mutate(
    ID = str_replace_all(
      ID,
      "(?<=[A-Za-z])(?=[^A-Za-z])|(?<=[0-9])(?=[^0-9])|(?<=[^A-Za-z0-9])(?=[A-Za-z0-9])",
      "|"
    )
  ) %>%
  separate_wider_delim(
    ID,
    delim = "|",
    names_sep = " ",
    too_few = "align_start"
  )

all.equal(result, test)
# Difference in first row

  • 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 = "300-399/349/CH-349 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=2, nrows=5)
test = pd.read_excel(path, usecols="F:K", skiprows=2, nrows=56)

result = (
    input
      .assign( 
          ID=input["ID"].str.replace(
              r"(?<=[A-Za-z])(?=[^A-Za-z])|"
              r"(?<=[0-9])(?=[^0-9])|"
              r"(?<=[^A-Za-z0-9])(?=[A-Za-z0-9])",
              "|",
              regex=True
          )
      ))

split_cols = result["ID"].str.split("|", expand=True)
total_cols = split_cols.shape[1]
split_cols.columns = [f"ID {i+1}" for i in range(total_cols)]

# difference in first row.

  • 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.