5 Best Ways to Program to Find the Maximum Number of Water Bottles You Can Drink in Python

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πŸ’‘ Problem Formulation: In this article, we tackle the problem of calculating the maximum number of water bottles one can drink given a starting number of bottles and the exchange rate of empty bottles for a full one. For example, if you start with 10 bottles and can exchange 3 empty bottles for 1 full one, the goal is to find out the total number of bottles you can drink.

Method 1: Iterative Approach

The iterative approach involves simulating the process of drinking bottles and exchanging the empties. We keep trading empty bottles for full ones as long as the exchange criteria are met, and count the total number of bottles consumed.

Here’s an example:

def max_bottles_to_drink(initial_bottles, exchange_rate):
    total_bottles_drunk = initial_bottles
    empty_bottles = initial_bottles
    while empty_bottles >= exchange_rate:
        trade_in = empty_bottles // exchange_rate
        empty_bottles = empty_bottles % exchange_rate + trade_in
        total_bottles_drunk += trade_in
    return total_bottles_drunk
print(max_bottles_to_drink(10, 3))

Output: 14

This code snippet defines a function max_bottles_to_drink() taking initial_bottles and exchange_rate as parameters to calculate the total consumption of water. It uses a simple iterative loop, reducing the complexity and ensuring ease of understanding.

Method 2: Recursive Approach

The recursive approach decomposes the problem into smaller sub-problems, making the function call itself with updated values until the base condition is met. It is more elegant but may not be as efficient as iterative methods for large inputs.

Here’s an example:

def max_bottles_to_drink_recursive(initial_bottles, exchange_rate):
    if initial_bottles < exchange_rate:
        return initial_bottles
        new_bottles = initial_bottles // exchange_rate
        remaining_bottles = initial_bottles % exchange_rate
        return initial_bottles + max_bottles_to_drink_recursive(new_bottles + remaining_bottles, exchange_rate)

print(max_bottles_to_drink_recursive(10, 3))

Output: 14

In this snippet, we define a function max_bottles_to_drink_recursive() which calls itself with the number of bottles remaining after an exchange. This demonstrates how recursion can simplify problem-solving by breaking it into smaller, more manageable chunks.

Method 3: Mathematical Approach

By analyzing the problem, we can sometimes derive a formula to find the total number of bottles drunk. It reduces the problem to a pure mathematical equation, providing the most efficient solution.

Here’s an example:

def max_bottles_math(initial_bottles, exchange_rate):
    return initial_bottles + (initial_bottles - 1) // (exchange_rate - 1)
print(max_bottles_math(10, 3))

Output: 14

The function max_bottles_math() calculates the total number of bottles by applying a derived mathematical formula. This approach eliminates the need for iteration or recursion, resulting in the fastest execution time, especially for large inputs.

Method 4: Using a Queue

Implementing a queue can simulate the process of drinking and exchanging bottles in an organized manner. Here, we enqueue initial bottles and continue the process until we can no longer exchange.

Here’s an example:

from collections import deque

def max_bottles_queue(initial_bottles, exchange_rate):
    queue = deque([initial_bottles])
    total_bottles_drunk = 0
    while queue:
        bottles = queue.popleft()
        total_bottles_drunk += bottles
        empty_bottles = bottles
        if empty_bottles >= exchange_rate:
            queue.append(empty_bottles // exchange_rate)
    return total_bottles_drunk

print(max_bottles_queue(10, 3))

Output: 14

The function max_bottles_queue() uses a deque object to track the number of empty bottles and handles the exchanges efficiently. This approach can make the logic simpler to follow and debug but might not be as space-efficient as the mathematical method.

Bonus One-Liner Method 5: Lambda with Reduce

This bonus one-liner uses Python’s functools.reduce() to compactly simulate the process of drinking and exchanging by continually accumulating the total.

Here’s an example:

from functools import reduce

max_bottles_lambda = lambda initial, exchange: reduce(lambda x, _: x + x // exchange, range(initial), initial)

print(max_bottles_lambda(10, 3))

Output: 14

This concise one-liner defines a lambda function max_bottles_lambda() that uses reduce() to condense the iterative logic into a single chain of operations. This is a powerful yet succinct technique suited for those familiar with functional programming paradigms.


  • Method 1: Iterative Approach. Straightforward logic. Easy to understand. It may be slower with very large input numbers because of the explicit loop.
  • Method 2: Recursive Approach. Elegant and straightforward. But it can lead to a stack overflow for very large inputs and is typically slower due to overhead of function calls.
  • Method 3: Mathematical Approach. Highly efficient. Works best with well-defined mathematical relations. It might however be difficult to derive a formula for more complex problems.
  • Method 4: Using a Queue. Good for simulating real-world processes. It can be less space-efficient, and the logic may be more complex than necessary for this problem.
  • Method 5: Lambda with Reduce. Compact and elegant for functional programming enthusiasts. However, it can be difficult to read and troubleshoot for those not familiar with this style.