Omid - Challenge 342

data-challenges
advanced-exercises
🔰 Question Result ID XMS 21 ID 1 ID 2 12
Published

March 24, 2026

Illustration for Omid - Challenge 342

Challenge Description

🔰 Question Result ID XMS 21 ID 1 ID 2 12

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/342/CH-342 Column Splitting.xlsx"
input <- read_excel(path, range = "B3:B8")
test <- read_excel(path, range = "F3:G8")

result = input %>%
  mutate(ID = str_replace_all(ID, "([A-Z]{2})([0-9]{2})", "\\1-\\2")) %>%
  separate_wider_delim(
    ID,
    delim = "-",
    names_sep = " ",
    too_few = "align_start"
  )

all.equal(result, test)

  • 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

excel_path = "300-399/342/CH-342 Column Splitting.xlsx"
input = pd.read_excel(excel_path, usecols="B", skiprows=2, nrows=6)
test = pd.read_excel(excel_path, usecols="F:G", skiprows=2, nrows=6)

input["ID"] = input["ID"].str.replace(r"([A-Z]{2})([0-9]{2})", r"\1-\2", regex=True)
split_cols = input["ID"].str.split("-", n=1, expand=True)
split_cols.columns = ["ID 1", "ID 2"]
input[["ID 1", "ID 2"]] = split_cols
input["ID 2"] = input["ID 2"].astype("float64")
id1_equal = input['ID 1'].eq(test['ID 1'], fill_value=None) | (input['ID 1'].isna() & test['ID 1'].isna())
id2_equal = input['ID 2'].eq(test['ID 2'], fill_value=None) | (input['ID 2'].isna() & test['ID 2'].isna())

if id1_equal.all() and id2_equal.all():
    print("All correct")

  • 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.