Day 25 - Python

Water Measurement

Problem Statement

You are given a list of containers, each with a specific amount of water. You need to measure the water in a series of rounds. In each round, you find the smallest non-zero amount of water and reduce all containers with water by that amount. At the end of each round, record how many containers still have water. Continue until all containers are empty.

Input Format

• The first line contains an integer, $n$, the number of containers.
• The second line contains $n$ space-separated integers, representing the water levels in the containers.

Output Format

• Print the number of containers still containing water after each round.

Constraints

• $1 \leq n \leq 1000$
  
• $1\le \text{water level}\le 10^4$

Sample Input 1

5
5 1 2 3 4

Sample Output 1

5
4
3
2
1

Explanation 1

• Initial water levels: [5, 1, 2, 3, 4]
• Smallest non-zero: 1 → Subtract 1 → Remaining: [4, 0, 1, 2, 3] → Count: 5
• Smallest non-zero: 1 → Subtract 1 → Remaining: [3, 0, 0, 1, 2] → Count: 4
• Smallest non-zero: 1 → Subtract 1 → Remaining: [2, 0, 0, 0, 1] → Count: 3
• Smallest non-zero: 1 → Subtract 1 → Remaining: [1, 0, 0, 0, 0] → Count: 2
• Smallest non-zero: 1 → Subtract 1 → Remaining: [0, 0, 0, 0, 0] → Count: 1

Sample Input 2

4
8 3 6 7 7 3

Sample Output 2

6
4
3
1

Explanation 2

• Initial water levels: [8, 3, 6, 7, 7, 3]
• Smallest non-zero: 3 → Subtract 3 → Remaining: [5, 0, 3, 4, 4, 0] → Count: 6
• Smallest non-zero: 2 → Subtract 2 → Remaining: [2, 0, 0, 1, 1, 0] → Count: 4
• Smallest non-zero: 1 → Subtract 1 → Remaining: [1, 0, 0, 0, 0, 0] → Count: 3
• Smallest non-zero: 1 → Subtract 1 → Remaining: [0, 0, 0, 0, 0, 0] → Count: 1

Python Code

def water_measurement(n, containers):
    results = []
    while any(water > 0 for water in containers):
        smallest = min(water for water in containers if water > 0)
        containers = [max(0, water - smallest) for water in containers]
        results.append(sum(1 for water in containers if water > 0))
    return results

n = int(input())
containers = list(map(int, input().split()))

result = water_measurement(n, containers)
for r in result:
    print(r)

#This is the expansion of the comprehension code

    results = []
    while any(water > 0 for water in containers):
        smallest = float('inf')
        for water in containers:
            if water > 0 and water < smallest:
                smallest = water

        for i in range(len(containers)):
            if containers[i] > 0:
                containers[i] -= smallest

        count = 0
        for water in containers:
            if water > 0:
                count += 1

        results.append(count)
    return results

Insights
1. Code Conciseness - The comprehension-based version is more concise and follows Pythonic principles, making it easier to write but potentially harder to understand for beginners.
2. Clarity - The non-comprehension version uses explicit loops, making it easier to follow each step of the process, especially for those new to Python.
3. Performance - Both versions have the same O(n2) time complexity but the comprehension-based version can be slightly faster due to Python's internal optimizations.
4. Readability - Comprehensions improve readability for experienced developers but may look complex to beginners, while explicit loops are straightforward but verbose.
5. Debugging - Explicit loops are easier to debug as they allow inspection of intermediate variables, unlike the compact comprehension version.
6. Flexibility - Explicit loops offer more flexibility to insert additional logic during iterations, whereas comprehensions are best for simple transformations.
7. Maintainability - Explicit loops are easier to modify or extend, while comprehensions are better suited for tasks that are unlikely to change in structure.
8. Use Case - Comprehensions are ideal for competitive programming where brevity is valued, while explicit loops are better for educational or collaborative coding environments.
9. Learning Curve - The comprehension approach has a steeper learning curve for beginners, while the non-comprehension version provides a more gradual introduction to Python concepts.
10. Error-Proneness - Explicit loops reduce the risk of subtle bugs during complex operations, whereas errors in comprehensions can be harder to trace due to their compact nature.

When it comes to water and list comprehensions: keep it short and sweet if the flow is simple, but use loops if you’re in deep water! 🚰