Omid - Challenge 236

data-challenges
advanced-exercises
🔰 Question Result ID ID.1 ID.2 ID.3 ABC123 ABC
Published

March 24, 2026

Illustration for Omid - Challenge 236

Challenge Description

🔰 Question Result ID ID.1 ID.2 ID.3 ABC123 ABC

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/236/CH-236 Column Splitting.xlsx"
input = read_excel(path, range = "B2:B7")
test = read_excel(path, range = "D2:F7")
test[is.na(test)] <- ""

split_id <- function(s) {
  if (str_detect(s, "^[A-Za-z]{3}")) {
    list(
      str_sub(s, 1, 3),
      str_sub(s, 4, 6),
      str_sub(s, 7, str_length(s))
    )
  } else {
    list(s, "", "")
  }
}

result = input %>%
  mutate(`ID.` = map(ID, split_id)) %>%
  unnest_wider(`ID.`, names_sep = "") %>%
  select(-c(1))

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 = "200-299/236/CH-236 Column Splitting.xlsx"
input_df = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test_df = pd.read_excel(path, usecols="D:F", skiprows=1, nrows=6).fillna("")

def split_id(s):
    s = str(s)
    if re.match(r"^[A-Za-z]{3}", s):
        return [s[:3], s[3:6], s[6:]]
    else:
        return [s, "", ""]

result = input_df['ID'].apply(split_id).apply(pd.Series)
result.columns = ['ID.1', 'ID.2', 'ID.3']

print(result.equals(test_df)) # 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.