Omid - Challenge 211

data-challenges
advanced-exercises
🔰 Question Result ID MN XYZ AB CD A
Published

March 24, 2026

Illustration for Omid - Challenge 211

Challenge Description

🔰 Question Result ID MN XYZ AB CD A

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-211Column Splitting.xlsx"
input = read_excel(path, range = "B2:B8")
test  = read_excel(path, range = "D2:F8")

split_string <- function(x) {
  x <- as.character(x)
  n <- nchar(x)
  
  get_parts <- function(s, len) {
    if (len <= 3) {
      c(s, NA, NA)
    } else if (len <= 6) {
      mid <- ceiling(len / 2)
      c(substr(s, 1, mid),
        substr(s, mid + 1, len),
        NA)
    } else {
      first <- substr(s, 1, 3)
      second <- substr(s, 4, 6)
      third <- substr(s, 7, len)
      c(first, second, third)
    }
  }
  result <- t(sapply(seq_along(x), function(i) {
    get_parts(x[i], n[i])
  }))
  colnames(result) <- c("Part 1", "Part 2", "Part 3")
  as.data.frame(result, stringsAsFactors = FALSE)
}

result = split_string(input$ID)

all.equal(result, test, check.attributes = FALSE)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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-211Column 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("").astype(str)

def split_string(series):
    def get_parts(s):
        s = str(s)
        n = len(s)
        if n <= 3:
            return [s, "", ""]
        elif n <= 6:
            mid = (n + 1) // 2
            return [s[:mid], s[mid:], ""]
        else:
            return [s[:3], s[3:6], s[6:]]
    
    result = [get_parts(x) for x in series]
    return pd.DataFrame(result, columns=["Part 1", "Part 2", "Part 3"]).astype(str)

result = split_string(input["ID"])

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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.