Omid - Challenge 336

data-challenges
advanced-exercises
🔰 Question Result F ID XMS128 F1M810 MMKN 12AA21
Published

March 24, 2026

Illustration for Omid - Challenge 336

Challenge Description

🔰 Question Result F ID XMS128 F1M810 MMKN 12AA21

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/336/CH-336 Column Splitting .xlsx"
input <- read_excel(path, range = "B2:B7")
test <- read_excel(path, range = "F2:I7")

result <- input %>%
  mutate(
    ID = str_replace_all(ID, "(?<=\\D)(?=\\d)|(?<=\\d)(?=\\D)", "|")
  ) %>%
  separate_wider_delim(
    ID,
    delim = "|",
    names_sep = " ",
    too_few = "align_start"
  )

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 numpy as np
import re

path = "300-399/336/CH-336 Column Splitting .xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=6, dtype=str)
test = pd.read_excel(path, usecols="F:I", skiprows=1, nrows=6, dtype=str)

def insert_pipe(val):
    if pd.isna(val):
        return val
    return re.sub(r'(?<=\D)(?=\d)|(?<=\d)(?=\D)', '|', str(val))

input["ID"] = input["ID"].apply(insert_pipe)

split_cols = (
    input["ID"]
    .str.split("|", expand=True)
    .replace(["None", None], np.nan)
)
split_cols.columns = [f"ID {i+1}" for i in range(split_cols.shape[1])]

input = input.drop(columns=["ID"])

result = pd.concat([input, split_cols], axis=1)

print(result.equals(test)) # True

  • 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.