library(tidyverse)
library(readxl)
path = "files/CH-226 Column Splitting.xlsx"
input = read_excel(path, range = "B2:B7")
test = read_excel(path, range = "D2:F7")
result = input %>% separate(ID, into = c("ID1", "ID2", "ID3"), sep = "(?<=[A-Za-z])(?=[A-Za-z])")Omid - Challenge 226
data-challenges
advanced-exercises
🔰 Question Result ID ID.1 ID.2 ID.3 AB123 CDX8Y

Challenge Description
🔰 Question Result ID ID.1 ID.2 ID.3 AB123 CDX8Y
Solutions
Logic:
- Reads the workbook ranges needed for the challenge
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
path = "CH-226 Column Splitting.xlsx"
input_data = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test_data = pd.read_excel(path, usecols="D:F", skiprows=1, nrows=6)
input_data[["ID.1", "ID.2", "ID.3"]] = input_data["ID"].str.split(r"(?<=[A-Za-z])(?=[A-Za-z])", expand=True)
input_data = input_data.drop(columns=["ID"])
print(input_data)Logic:
- Reads the workbook ranges needed for the challenge
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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.