Omid - Challenge 368

data-challenges
advanced-exercises
🔰 In the id column in the question table, find
Published

March 24, 2026

Illustration for Omid - Challenge 368

Challenge Description

🔰 In the id column in the question table, find

Solutions

library(tidyverse)
library(readxl)
library(stringi)

path <- "300-399/368/CH-368 Text Cleaning.xlsx"
input <- read_excel(path, range = "B3:B8")
test <- read_excel(path, range = "E3:E8")

mirror_half <- function(x) {
  n <- nchar(x)
  best <- ""
  for (i in seq_len(n)) {
    for (len in seq_len((n - i + 1) %/% 2)) {
      left <- substr(x, i, i + len - 1)
      right <- substr(x, i + len, i + 2 * len - 1)
      if (left == stri_reverse(right) && len > nchar(best)) {
        best <- left
      }
    }
  }
  best
}

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

all.equal(result$ID, test$ID)
# Last one correct but not accordign to given answers.

  • 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 = "300-399/368/CH-368 Text Cleaning.xlsx"
input = pd.read_excel(path, usecols="B", nrows = 6, skiprows = 2)
test = pd.read_excel(path, usecols="E", nrows = 6, skiprows =2 )

def mirror_half(s):
     n = len(s)
     best = ""
     for i in range(n):
         for l in range(1, (n - i + 1) // 2 + 1):
             left = s[i:i+l]
             right = s[i+l:i+2*l]
             if left == right[::-1] and len(left) > len(best):
                 best = left
     return best

input["Cleaned"] = input["ID"].apply(mirror_half)
print(input["Cleaned"].equals(test["ID.1"]))
# Last one correct but not according to the expected output.

  • 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.