Omid - Challenge 195

data-challenges
advanced-exercises
🔰 Challenge 195 : Missing Char!
Published

March 24, 2026

Illustration for Omid - Challenge 195

Challenge Description

🔰 Challenge 195 : Missing Char!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-195 Missing Char.xlsx"
input = read_excel(path, range = "B2:B7")
test  = read_excel(path, range = "D2:D7")

transform_text <- function(text) {
  chars <- substr(text, 1, 2) 
  
  for (i in seq(3, nchar(text))) {
    char <- substr(text, i, i)  
    cond <- ((nchar(chars) + 1) %% 3 == 0) && (char != "/") 
    chars <- paste0(chars, ifelse(cond, "-", ""), char)
  }
  
  return(chars)
}

result = input %>%
  mutate(result = map_chr(ID, transform_text))

all.equal(result$result, test$ID, check.attributes = FALSE) # TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

    • 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

path = "CH-195 Missing Char.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="D", skiprows=1, nrows=6).rename(columns=lambda x: x.split('.')[0])

def transform_text(text):
    chars = text[:2] 
    
    for i in range(2, len(text)):
        char = text[i] 
        cond = ((len(chars) + 1) % 3 == 0) and (char != "/")  
        chars += ('-' if cond else '') + char 
    
    return chars

input['result'] = input.iloc[:, 0].apply(transform_text)

result = input['result'].tolist()
expected = test.iloc[:, 0].tolist()

print(result == expected) # 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.