library(tidyverse)
library(readxl)
path = "files/CH-125 Pad middle.xlsx"
input = read_excel(path, range = "B2:B9")
test = read_excel(path, range = "D2:D9")
innix_pad = function(string) {
letters = str_extract(string, "[A-Z]+")
numbers = str_extract(string, "[0-9]+")
pad_num = 6 - nchar(letters)
return(paste0(letters, str_pad(numbers, pad_num, side = "left", pad = "0")))
}
result = input %>%
mutate(ID = map_chr(ID, innix_pad))
all.equal(result, test)
#> [1] TRUEOmid - Challenge 125
data-challenges
advanced-exercises
🔰 Result Question ID M5 MN1 PQ145 RS802 FF201

Challenge Description
🔰 Result Question ID M5 MN1 PQ145 RS802 FF201
Solutions
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 re
import pandas as pd
path = "CH-125 Pad middle.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=8)
test = pd.read_excel(path, usecols="D", skiprows=1, nrows=8).rename(columns=lambda x: x.replace(".1", ""))
def innix_pad(string):
letters, numbers = re.findall(r"[A-Z]+|[0-9]+", string)
return f"{letters}{numbers.zfill(6 - len(letters))}"
input = input.map(innix_pad)
print(input.equals(test)) # TrueLogic:
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.