Nth Fibonacci Term in C, C++, Java & Python – Code with Explanation & Examples in Short and Simple

   

C Program

#include <stdio.h>

int fibonacci(int n) {
    if (n <= 1) return n;
    return fibonacci(n - 1) + fibonacci(n - 2);
}

int main() {
    int n = 7;
    printf("Fibonacci term at position %d is %d\n", n, fibonacci(n));
    return 0;
}

C Output

Fibonacci term at position 7 is 13

C++ Program

#include <iostream>
using namespace std;

int fibonacci(int n) {
    if (n <= 1) return n;
    return fibonacci(n - 1) + fibonacci(n - 2);
}

int main() {
    int n = 6;
    cout << "Fibonacci term at position " << n << " is " << fibonacci(n) << endl;
    return 0;
}

C++ Output

Fibonacci term at position 6 is 8

JAVA Program

public class Main {
    static int fibonacci(int n) {
        if (n <= 1) return n;
        return fibonacci(n - 1) + fibonacci(n - 2);
    }

    public static void main(String[] args) {
        int n = 5;
        System.out.println("Fibonacci term at position " + n + " is " + fibonacci(n));
    }
}

JAVA Output

Fibonacci term at position 5 is 5

Python Program

def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

n = 4
print(f"Fibonacci term at position {n} is {fibonacci(n)}")

Python Output

Fibonacci term at position 4 is 3

Explanation

Example

The Fibonacci series begins as 0, 1, 1, 2, 3, 5, 8, 13…. It is the sum of the two preceding ones. If we request the term in the 7th position, we compute it as F(7) = F(6) + F(5), which becomes (F(5) + F(4)) + (F(4) + F(3)) and continues, until we arrive at F(0) or F(1), which are simply returned without any further recursion.

Real-Life Analogy

Consider the family tree. To learn the name of your great-great-grandparent, you ask your great-grandparent, who asks their parent, and so on, until you hit the very first ancestor. The Fibonacci computation is the same: to compute one, you have to compute the two preceding terms, which themselves need to be computed earlier.

Why It Matters

The Fibonacci sequence can be found in nature, architecture, and stock market models, which is why it is an interesting display of how math comes alive in the world. Practicing recursion with Fibonacci shows how to split a problem up into little pieces until it can be solved directly. It's a typical starting point for grasping recursive thinking.

Learning Insights

While the recursive Fibonacci code is elegant, it's also inefficient for large n since it recalc ula tes the same values many times. This is an impo rtant lesson to remember sometime the most elegant solution in code isn't the fastest. It also opens the door to concepts like memoization and dynamic programming, which are used to optimize recursive calls.

Use in Interviews and Projects

Interviewers tend to ask for Fibonacci both to check you understand recursion and whether you're able to recognize inefficiencies and make recommendations. In projects, Fibonacci-type recurrence relations appear in probability, optimization, and even graphics algorithms, so it pays to be proficient at implementing and optimizing them.

SEO-Friendly Closing

Nth Fibonacci term via recursion in C, C++, Java, and Python is a great representation of problem division into smaller subproblems until the base cases are reached. While not the most optimal solution for big inputs, it is a great method to practice recursion basics and learn how function calls stack and unwind. Whether preparing for coding interviews or exploring algorithm patterns in real-world applications, mastering recursive Fibonacci lays the groundwork for more advanced techniques like dynamic programming and memoization.