library(tidyverse)
library(readxl)
path = "files/CH-216 Column Splitting.xlsx"
input = read_excel(path, range = "B2:B7")
test = read_excel(path, range = "D2:F7")
result = input %>%
extract(ID, into = c("Prefix", "Root", "Suffix"),
regex = "^([A-Z]{2})(.*)([A-Za-z0-9]{1})$")
all.equal(result, test)
#> [1] TRUEOmid - Challenge 216
data-challenges
advanced-exercises
🔰 Question Result ID Prefix Root Suffix AB123 AB

Challenge Description
🔰 Question Result ID Prefix Root Suffix AB123 AB
Solutions
Logic:
Reads the workbook ranges needed for the challenge
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 = "CH-216 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="D:F", skiprows=1, nrows=6)
result = input["ID"].str.extract(r"^([A-Z]{2})(.*)([A-Za-z0-9]{1})$")
result.columns = ["Prefix", "Root", "Suffix"]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.