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

 

C Program

#include<stdio.h>

int f(int n) {
    return n < 2 ? n : f(n - 1) + f(n - 2);
}

int main() {
    int n;
    scanf("%d", &n);
    for (int i = 0; i < n; i++)
        printf("%d ", f(i));
}

C Output

Input: 5  
Output: 0 1 1 2 3

C++ Program

#include<iostream>
using namespace std;

int f(int n) {
    return n < 2 ? n : f(n - 1) + f(n - 2);
}

int main() {
    int n;
    cin >> n;
    for (int i = 0; i < n; i++)
        cout << f(i) << " ";
}

C++ Output

Input: 5  
Output: 0 1 1 2 3

JAVA Program

import java.util.*;

class F {
    static int f(int n) {
        return n < 2 ? n : f(n - 1) + f(n - 2);
    }

    public static void main(String[] args) {
        Scanner sc = new Scanner(System.in);
        int n = sc.nextInt();
        for (int i = 0; i < n; i++)
            System.out.print(f(i) + " ");
    }
}

JAVA Output

Input: 5  
Output: 0 1 1 2 3

Python Program

def f(n):
    return n if n < 2 else f(n - 1) + f(n - 2)

n = int(input())
for i in range(n):
    print(f(i), end=' ')

Python Output

Input: 5  
Output: 0 1 1 2 3

In-Depth Learning – Complete Concept in Paragraphs

What Is Recursive Fibonacci?

The recursive Fibonacci approach is a direct implementation of the mathematical definition of the Fibonacci sequence into programming. In this method, the function continues to call itself to find solutions to smaller and smaller subproblems until it hits the base case — 0 or 1. These base cases are the points that bring the recursion to a halt. When arrived at, the values come back and start aggregating upwards again along the recursive tree to compute terms further up. The beauty of recursion comes from this successive breakdown and reconstructions in layers, similar to opening a stack of nested boxes one layer at a time and then closing them again in the opposite direction.

Example

Suppose we need to compute the Fibonacci number at position 4. The recursive function begins by inquiring, "What is f(4)? To do that, it requires f(3) and f(2). But now it must respond to f(3), so it calls f(2) and f(1). This repeats until it hits a point where n is below 2. That's the stop case: if n is 0 or 1, the function simply returns n. When we hit the bottom, the function begins unwinding by summing up the returned values.".

That's how we obtain f(4) = f(3) + f(2) = 2 + 1 = 3.

Real-Life Analogy 

Suppose you wish to find how many ways you can ascend a staircase by either taking one or two steps at once. If you wish to be at the fifth step, then either you can arrive from the fourth step (by one step) or from the third step (by two steps). So the ways to achieve the fifth step is the ways to achieve the fourth and the ways to achieve the third.

This is the recursive thought process — each decision level relies on the sum of the two previous possibilities, as Fibonacci does.

How the Recursive Code Works

In our code, the function f(n) just returns n if it is 0 or 1. If it's more than that, the function calls itself twice, once with n-1 and once with n-2. This structure builds a branching tree of function calls. The main function loops from 0 to n-1 and calls this recursive function for every index to print the entire series. While mathematically clean and easy, this method is slow for big n since it re-computes the same values repeatedly. For instance, f(4) calls f(2) twice, which is not efficient.

This is why this approach is used primarily for educational or educational purposes or used in combination with memoization for performance enhancement.

Why Recursive Fibonacci Matters in Learning 

Recursion is one of the most fundamental topics in computer science. It teaches the mind to conceptualize problems as instances of themselves in small scales — which is the basis of most algorithms. The recursive Fibonacci, though not the most optimal, is a great learning tool to see how recursive calls function, how base cases function to prevent looping infinitely, and how multiple instances of functions are handled by the call stack.

Through learning recursion with Fibonacci, students develop the thought process necessary for higher-level problems in trees, graphs, backtracking, and dynamic programming.

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 If you want to learn the Fibonacci sequence with recursion in its most basic form, this page has got you covered — from brief, functional code snippets in C, C++, Java, and Python, to a thorough explanation of how recursion constructs solutions from base cases upwards. The recursive Fibonacci sequence is also one of the most frequently encountered programming interview questions and is utilized extensively in competitive programming, exam questions at school/university, and introductory-level recursion exercise. If you are appearing for a TCS or a Wipro interview, sitting for a coding test, or simply looking to learn how functions can recursively call themselves to break down a solution into step-by-step parts, this explanation has all the insight that you are looking for. By learning Fibonacci using recursion, you are also laying a solid foundation for solving tree-related problems, dynamic programming, and investigating how algorithms expand from smaller inputs to larger outputs through the strength of self-reference