Omid - Challenge 136

data-challenges
advanced-exercises
🔰 Challenge 136: Column Splitting!
Published

March 24, 2026

Illustration for Omid - Challenge 136

Challenge Description

🔰 Challenge 136: Column Splitting!

Solutions

library(tidyverse)
library(readxl)
library(charcuterie)

path = "files/CH-136 Column Splitting.xlsx"
input = read_excel(path, range = "B2:B8")
test  = read_excel(path, range = "D2:F8", col_types = "text")

separate_by_double = function(string) {
  chars = chars(string)
  for (i in 1:(length(chars) - 1)) {
    if (chars[i] == chars[i + 1]) {
      chars[i] = paste0(chars[i], ",")
    }
  }
  df = data.frame(chars = string(chars)) %>% separate(chars, into = c("ID.1","ID.2","ID.3"), sep = ",")
  
  print(df)
}

result = map_dfr(input$ID, separate_by_double)

all.equal(result, test, check.attributes = FALSE)
# 21 in test has dot and decimal zero.

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Applies the rule iteratively until the output stabilizes

  • 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
from itertools import groupby

# Read the Excel file
path = "CH-136 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, dtype=str).fillna("")

def separate_by_double(string):
    split_data = [string[0]]
    for i in range(1, len(string)):
        if string[i] == string[i - 1]:
            split_data[-1] += ","
        split_data.append(string[i])
    split_data = ''.join(split_data).split(",")
    return split_data + [""] * (3 - len(split_data))

result = pd.DataFrame([separate_by_double(id) for id in input.iloc[:, 0]], columns=["ID.1", "ID.2", "ID.3"])

print(result.equals(test)) # Output: True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Applies the rule iteratively until the output stabilizes

  • 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.