Implementing Priority Queues with `heapq`

import heapq

# A min-heap implementation using a list
priority_queue = []

# Add tasks to the priority queue. Items are tuples: (priority, task_name)
# Lower priority numbers mean higher priority (min-heap property)
heapq.heappush(priority_queue, (3, 'Low priority task A'))
heapq.heappush(priority_queue, (1, 'High priority task C'))
heapq.heappush(priority_queue, (2, 'Medium priority task B'))
heapq.heappush(priority_queue, (1, 'High priority task D')) # Same priority as C

print(f"Queue after pushes: {priority_queue}") # Not sorted visually, but heap property maintained

# Retrieve and remove the highest priority (lowest number) item
priority, task = heapq.heappop(priority_queue)
print(f"Processed highest priority task: {task} (Priority: {priority})") # Output: Processed highest priority task: High priority task C (Priority: 1)

priority, task = heapq.heappop(priority_queue)
print(f"Processed highest priority task: {task} (Priority: {priority})") # Output: Processed highest priority task: High priority task D (Priority: 1)

# You can also use objects if they are comparable or define a __lt__ method
class Job:
    def __init__(self, priority, description):
        self.priority = priority
        self.description = description
    def __lt__(self, other): # Define less than for comparison
        return self.priority < other.priority
    def __repr__(self):
        return f"Job(priority={self.priority}, description='{self.description}')"

job_queue = []
heapq.heappush(job_queue, Job(5, 'Background cleanup'))
heapq.heappush(job_queue, Job(1, 'Urgent user email'))
heapq.heappush(job_queue, Job(3, 'Analytics report generation'))

print(f"
Job queue: {job_queue}")
processed_job = heapq.heappop(job_queue)
print(f"Processed job: {processed_job}") # Output: Processed job: Job(priority=1, description='Urgent user email')