Omid - Challenge 178

data-challenges
advanced-exercises
🔰 Find the prime Factors of numbers in the question table.
Published

March 24, 2026

Illustration for Omid - Challenge 178

Challenge Description

🔰 Find the prime Factors of numbers in the question table.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-178 Prime Factors.xlsx"
input = read_excel(path, range = "B2:B7")
test  = read_excel(path, range = "F2:G7")


find_prime_factors <- function(n) {
  factors <- c()
  for (divisor in 2:n) {
    while (n %% divisor == 0) {
      factors <- c(factors, divisor)
      n <- n / divisor
    }
  }
  str_c(factors, collapse = "*")
}

result = input %>%
  mutate(prime_factors = map_chr(Numbers, find_prime_factors)) 

all.equal(result$prime_factors, test$`Result 1`)
# [1] TRUE

  • 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

    • 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
import numpy as np

path = "CH-178 Prime Factors.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="F:G", skiprows=1, nrows=6)

def find_prime_factors(n):
    factors = []
    for divisor in range(2, int(n**0.5) + 1):
        while n % divisor == 0:
            factors.append(divisor)
            n //= divisor
    if n > 1:
        factors.append(n)
    return "*".join(map(str, factors))

input['prime_factors'] = input.iloc[:, 0].apply(find_prime_factors)

print(all(input["prime_factors"] == test['Result 1'])) # 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 to challenging:

  • It depends on a non-trivial iterative or rule-based transformation.

  • Getting the expected output requires more than one straightforward dataframe step.