Omid - Challenge 72

data-challenges
advanced-exercises
🔰 Challenge 72 : Fibonacci sequence!
Published

March 24, 2026

Illustration for Omid - Challenge 72

Challenge Description

🔰 Challenge 72 : Fibonacci sequence!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-072 Fibonacci sequence .xlsx"
test = read_excel(path,  range = "B1:B19") %>% pull()


fibonacci <- function(n) {
  if (n == 1) {
    return(c(0))
  } else if (n == 2) {
    return(c(0, 1))
  } else {
    fib_sequence <- accumulate(3:n, .init = c(0, 1), ~ c(.x, .x[length(.x)] + .x[length(.x) - 1]))
    return(unlist(fib_sequence[n - 1]))
  }
}
identical(fibonacci(18), test) 
# [1] TRUE

fibonacci1 <- function(n) {
  fib_sequence <- numeric(n)
  fib_sequence[1] <- 0
  if (n > 1) {
    fib_sequence[2] <- 1
  }
  if (n > 2) {
    map(3:n, ~ {
      fib_sequence[.x] <<- fib_sequence[.x - 1] + fib_sequence[.x - 2]
    })
  }
  return(fib_sequence)
}
identical(fibonacci1(18), test)
# [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-072 Fibonacci sequence .xlsx"
test = pd.read_excel(path, usecols="B").values.tolist()
test = [item for sublist in test for item in sublist]

def fibonacci_memo(n, memo={0: 0, 1: 1}):
    if n not in memo:
        memo[n] = fibonacci_memo(n - 1, memo) + fibonacci_memo(n - 2, memo)
    return memo[n]

def generate_fibonacci_sequence(N):
    return [fibonacci_memo(i) for i in range(N)]

result = generate_fibonacci_sequence(18)
print(result == 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.