Omid - Challenge 156

data-challenges
advanced-exercises
🔰 Question Result ID ID.1 ID.2 ID.3 RSN MX
Published

March 24, 2026

Illustration for Omid - Challenge 156

Challenge Description

🔰 Question Result ID ID.1 ID.2 ID.3 RSN MX

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-156 Column Splitting.xlsx"
input = read_excel(path, range = "B2:B8")
test  = read_excel(path, range = "D2:F8")

result = input %>%
  mutate(ID.1 = ifelse(nchar(ID) %% 2 == 0, 
                       substr(ID, 1, nchar(ID)/2), 
                       substr(ID, 1, nchar(ID)/2)),
         ID.2 = ifelse(nchar(ID) %% 2 == 0, 
                       substr(ID, nchar(ID)/2 + 1, nchar(ID)), 
                       substr(ID, nchar(ID)/2 + 1, nchar(ID)/2 + 1)),
         ID.3 = ifelse(nchar(ID) %% 2 == 0, 
                       NA, 
                       substr(ID, nchar(ID)/2 + 2, nchar(ID)))) %>%
    select(-ID)

all.equal(result, test, check.attributes = FALSE)
# only one discrepancy from original solution

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • 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-156 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=7)
test = pd.read_excel(path, usecols="D:F", skiprows=1, nrows=7).fillna('')

def split_id(id):
    n = len(id)
    mid = n // 2
    if n % 2 == 0:
        id1, id2, id3 = id[:mid], id[mid:], None
    else:
        id1, id2, id3 = id[:mid], id[mid:mid + 1], id[mid + 1:]
    return pd.Series([id1, id2, id3])

result = input['ID'].apply(split_id).fillna('')
result.columns = ['ID.1', 'ID.2', 'ID.3']

# identical except one field wrong in given solution

  • 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.