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Recursion in Python - GeeksforGeeks

Last Updated : 14 Aug, 2025

Recursion is a programming technique where a function calls itself either directly or indirectly to solve a problem by breaking it into smaller, simpler subproblems.

In Python, recursion is especially useful for problems that can be divided into identical smaller tasks, such as mathematical calculations, tree traversals or divide-and-conquer algorithms.

A recursive function is just like any other Python function except that it calls itself in its body. Let's see basic structure of recursive function:

def recursive_function(parameters):
if base_case_condition:
return base_result
else:
return recursive_function(modified_parameters)

Recursive function contains two key parts:

• Base Case: The stopping condition that prevents infinite recursion.
• Recursive Case: The part of the function where it calls itself with modified parameters.

Let's understand recursion better with the help of some examples.

This code defines a recursive function to calculate factorial of a number, where function repeatedly calls itself with smaller values until it reaches the base case.

def factorial(n):
    if n == 0:  # Base case
        return 1
    else:       # Recursive case
        return n * factorial(n - 1)

print(factorial(5))

Explanation:

• Base Case: When n == 0, recursion stops and returns 1.
• Recursive Case: Multiplies n with the factorial of n-1 until it reaches the base case.

This code defines a recursive function to calculate n^th Fibonacci number, where each number is the sum of the two preceding ones, starting from 0 and 1.

def fibonacci(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))  

Explanation:

• Base Cases: If n == 0, the function returns 0. If n == 1, the function returns 1. These two cases are necessary to stop the recursion.
• Recursive Case: function calls itself twice with decrements of n (i.e., fibonacci(n-1) and fibonacci(n-2)), summing results of these calls.

Recursion can be broadly classified into two types: tail recursion and non-tail recursion. The main difference between them is related to what happens after recursive call.

• Tail Recursion: The recursive call is the last thing the function does, so nothing happens after it returns. Some languages can optimize this to work like a loop, saving memory.
• Non-Tail Recursion: The function does more work after the recursive call returns, so it can’t be optimized into a loop.

This code compares tail recursion and non-tail recursion using two versions of factorial function one with an accumulator (tail-recursive) and one with multiplication after recursive call (non-tail-recursive).

def tail_fact(n, acc=1):
    # Base case
    if n == 0:
        return acc
    # Tail recursive call with an accumulator
    else:
        return tail_fact(n-1, acc * n)

def nontail_fact(n):
    # Base case
    if n == 1:
        return 1
    # Non-tail recursive call because the multiplication happens after the call
    else:
        return n * nontail_fact(n-1)

# Example usage
print(tail_fact(5))  
print(nontail_fact(5))  

Explanation:

• def tail_fact(n, acc=1): - Defines a tail-recursive factorial function with an accumulator acc to store intermediate results.
• if n == 0: return acc - Base case: when n reaches 0, return the accumulated result.
• return tail_fact(n-1, acc * n) - Tail-recursive call: multiplies acc by n before the call, so no extra work is left after recursion.
• def nontail_fact(n): - Defines a non-tail-recursive factorial function.
• if n == 1: return 1 - Base case: factorial of 1 is 1.
• return n * nontail_fact(n-1) - Non-tail call: multiplication happens after the recursive call returns, so more work remains after recursion.

It is often more intuitive and easier to implement when the problem is naturally recursive, like tree traversals. It can lead to solutions that are easier to understand compared to iterative ones.

Iteration involves loops (for, while) to repeat the execution of a block of code. It is generally more memory-efficient as it does not involve multiple stack frames like recursion.

• When the problem can be solved easily with loops.
• When recursion depth is large enough to risk a stack overflow.
• When performance is critical and function call overhead matters.

• Simplicity: Recursive code is generally simpler and cleaner, especially for problems inherently recursive in nature (e.g., tree traversals, dynamic programming problems).
• Reduced Code Length: Recursion can reduce the length of the code since the repetitive tasks are handled through repeated function calls.

• Memory Overhead: Each recursive call adds a new layer to the stack, which can result in significant memory use, especially for deep recursion.
• Performance Issues: Recursive functions may lead to slower responses due to overheads like function calls and returns.
• Risk of Stack Overflow: Excessive recursion can lead to a stack overflow error if the recursion depth exceeds the stack limit.

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