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Calculating the Power of a Number Using Recursion in Python

Updated: at 02:50 AM

Recursion is a powerful programming technique in which a function calls itself repeatedly to solve a problem. This allows the function to repeat the same logic over and over with different inputs to achieve the desired solution.

In Python, recursion is an elegant way to implement mathematical computations like factorial, fibonacci series, etc. Calculating the power of a number is another great example that can be solved recursively.

This comprehensive guide will explain what recursion is, its advantages and disadvantages, and provide step-by-step instructions on how to calculate the power of a number using recursive functions in Python.

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What is Recursion?

Recursion refers to the repeated application of a recursive procedure or definition. In programming terms, recursion means that a function calls itself from within its own code. The function executes repeatedly on its own outputs until a base condition is met at which point it terminates.

For example, consider the mathematical operation of calculating factorials. The factorial of a number n is denoted by n! and can be defined as:

n! = n * (n-1) * (n-2) * .... * 1

The factorial function can be implemented recursively in Python as:

def factorial(n):
    if n == 0:
        return 1
        return n * factorial(n-1)

Here, the factorial() function calls itself recursively with the decremented value of n until the base case of n == 0 is reached. The stack of recursive calls unwinds backwards returning the factorial value.

Some key properties of recursive functions:

Advantages of Recursion

Some of the advantages of using recursion include:

Disadvantages of Recursion

The cons of recursive functions include:

To mitigate these issues, recursion must be applied judiciously with proper base cases and stack size monitoring.

Calculating Power Using Recursion

The mathematical power operation raises a number to an exponent. For example, 5^3 = 5 * 5 * 5 = 125.

This can be implemented recursively in Python by repeatedly multiplying the number by itself, decrementing the exponent on each recursive call until the exponent reaches 0.

Here is the recursive power function:

def power(base, exp):
    if exp == 0:
        return 1
        return base * power(base, exp-1)

This uses the following logic:

Let’s calculate 5^4 step-by-step:

power(5, 4)
= 5 * power(5, 3)
= 5 * (5 * power(5, 2))
= 5 * (5 * (5 * power(5, 1)))
= 5 * (5 * (5 * (5 * power(5, 0))))
= 5 * (5 * (5 * (5 * 1)))
= 625

The stack depth corresponds to the exponent value before returning the final result.

Here is the full working code:

def power(base, exp):
    """Calculates base raised to the exp power recursively"""
    if exp == 0:
        return 1
    return base * power(base, exp-1)

print(power(5, 3)) # 125
print(power(2, 5)) # 32

The power() function could be improved further using these techniques:

Handling Edge Cases

Edge cases refer to problem inputs that need special handling, such as:

Let’s update the power() function to handle these edge cases:

import math

def power(base, exp):

    # Base case
    if exp == 0:
        return 1

    # Edge cases
    if base == 0:
        return 0
    if exp < 0:
        return 1/base * power(base, -exp)

    # Recursive case
    return base * power(base, exp-1)

Now the function can deal with special cases like:

print(power(5, -2)) # 0.04
print(power(0, 5)) # 0

Memoization to Optimize Recursion

Recursion can sometimes lead to repeated recursive calls with the same inputs.

Memoization is an optimization technique that caches previously computed results, avoiding recalculation of recurring inputs.

We can use a dict in Python to store cached power values for each unique input pair.

# Cache for memoization
power_cache = {}

def power(base, exp):

    # Check cache
    if (base, exp) in power_cache:
        return power_cache[(base, exp)]

    # Base case
    if exp == 0:
        return 1

    # Recursive case
    if exp > 0:
        result = base * power(base, exp-1)
        result = power(base, -exp) ** -1

    # Store in cache
    power_cache[(base, exp)] = result
    return result

Now recurring calls with same arguments will reuse the cached result instead of recomputing. This improves performance significantly for larger exponents.

Measuring Time Complexity

We can compare recursive and iterative implementations by measuring their time complexity.

Here is an iterative power function that uses a for loop:

def power_iter(base, exp):
    result = 1
    for i in range(exp):
        result *= base
    return result

To test the time taken, we can use the timeit module in Python:

import timeit

# Test recursive power
print(timeit.timeit('power(5, 100)', globals=globals(), number=100))

# Test iterative power
print(timeit.timeit('power_iter(5, 100)', globals=globals(), number=100))

The recursive function takes more time due to repeated calls. Memoization improves the recursion performance by caching results.

Setting Recursion Limits

By default, Python limits recursion depth to 1000 calls to prevent stack overflows. The sys module allows checking and updating this limit:

import sys

print(sys.getrecursionlimit()) # 1000


The recursion limit can be set higher based on the required stack depth for the problem. But optimal recursion should work within reasonable stack limits.


This guide covered a step-by-step implementation of power calculation using recursion in Python. We discussed the advantages and disadvantages of recursion, handling edge cases, memoization for optimization, measuring time complexity, and setting recursion limits.

Key Takeaways:

Recursion is a fundamental programming concept that opens up elegant solutions to complex problems using simple recursive functions. Mastering recursion provides a powerful technique to write clean and maintainable code.