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Implementing a Priority Queue in Python

Implementing a Priority Queue in Python
Shahid Ali
Technical writer
Python
10.07.2024
Reading time: 6 min

A priority queue is a special type of queue where each element is associated with a priority. In a priority queue, elements are dequeued in order of their priority, rather than their insertion order. This makes priority queues a powerful data structure for scenarios where elements with higher priorities need to be processed first.

Importance of Priority Queues in Programming

Priority queues are widely used in various programming applications, including:

  • Task scheduling in operating systems

  • Managing the order of events in simulations

  • Implementing Dijkstra's algorithm for finding the shortest path in graphs

  • Managing a to-do list where tasks have different levels of urgency

Implementing a Priority Queue Using heapq

Python provides a built-in library called heapq that can be used to implement priority queues. The heapq module offers an efficient way to maintain a heap, which is a binary tree where the parent node is always smaller than or equal to its child nodes (min-heap).

Basic Operations: Push, Pop, Peek

Let's explore the basic operations of a priority queue using the heapq module.

Pushing an Element

To add an element to the priority queue, use the heappush function:

import heapq

# Create an empty priority queue
priority_queue = []

# Add elements to the priority queue
heapq.heappush(priority_queue, (2, 'task 2'))
heapq.heappush(priority_queue, (1, 'task 1'))
heapq.heappush(priority_queue, (3, 'task 3'))

print(priority_queue)

Output:

Defrdgtfyg

Popping an Element

To remove and return the smallest element from the priority queue, use the heappop function:

smallest_task = heapq.heappop(priority_queue)
print(smallest_task)
print(priority_queue)

Output:

82d536c3 E66f 4397 810d 0ef2636f4b85

Peeking at the Smallest Element

To look at the smallest element without removing it, simply access the first element of the list:

# Peek at the smallest element
smallest_task = priority_queue[0]
print(smallest_task)

Output:

Fd334278 B8cd 4dec A075 0bd06b53ef18

Example Use Cases for Priority Queues

Let's look at different use cases for priority queues.

Task Scheduling

In task scheduling, tasks with higher priorities should be executed before those with lower priorities. A priority queue can manage the order of task execution efficiently.

import heapq

tasks = []

# Add tasks with priorities
heapq.heappush(tasks, (1, 'write report'))
heapq.heappush(tasks, (3, 'email team'))
heapq.heappush(tasks, (2, 'prepare presentation'))

while tasks:
    priority, task = heapq.heappop(tasks)
    print(f'Executing: {task}')

Output:

Ce6078dc 7694 42a3 8ee1 1239b1f14112

Dijkstra's Algorithm

Dijkstra's algorithm finds the shortest path from a source node to all other nodes in a graph. A priority queue is used to select the next node to process based on the shortest known distance.

import heapq

def dijkstra(graph, start):
    priority_queue = []
    heapq.heappush(priority_queue, (0, start))
    distances = {node: float('inf') for node in graph}
    distances[start] = 0

    while priority_queue:
        current_distance, current_node = heapq.heappop(priority_queue)

        if current_distance > distances[current_node]:
            continue

        for neighbor, weight in graph[current_node]:
            distance = current_distance + weight
            if distance < distances[neighbor]:
                distances[neighbor] = distance
                heapq.heappush(priority_queue, (distance, neighbor))

    return distances

graph = {
    'A': [('B', 1), ('C', 4)],
    'B': [('A', 1), ('C', 2), ('D', 5)],
    'C': [('A', 4), ('B', 2), ('D', 1)],
    'D': [('B', 5), ('C', 1)],
}

distances = dijkstra(graph, 'A')
print(distances)

Output:

Efb44852 0a8e 4faa B356 604eda8a5fb0

Advanced Priority Queue Operations

In this section, we will look closer at advanced usage of priority queues.

Updating an Element's Priority

Updating an element's priority involves removing the element and adding it again with the new priority. This can be inefficient, but it's a necessary step since heapq does not support direct priority updates.

import heapq

priority_queue = [(2, 'task 2'), (1, 'task 1'), (3, 'task 3')]
heapq.heapify(priority_queue)

# Remove task 2 and add it with a new priority
priority_queue = [(p, t) for p, t in priority_queue if t != 'task 2']
heapq.heapify(priority_queue)
heapq.heappush(priority_queue, (4, 'task 2'))

print(priority_queue)

Output:

E15df7a3 F0d4 4cba B069 91edfeb4f0c2

Merging Two Priority Queues

To merge two priority queues, use the heapq.merge function, which returns an iterator over the sorted values.

import heapq

pq1 = [(1, 'task 1'), (3, 'task 3')]
pq2 = [(2, 'task 2'), (4, 'task 4')]

merged_pq = list(heapq.merge(pq1, pq2))
print(merged_pq)

Output:

8f0cb6f9 5feb 42cd 8fe4 66c6088ce7d8

Practical Code Examples

Priority Queue with Custom Objects

You can use custom objects in a priority queue by implementing comparison methods.

import heapq

class Task:
    def __init__(self, priority, description):
        self.priority = priority
        self.description = description

    def __lt__(self, other):
        return self.priority < other.priority

priority_queue = []
heapq.heappush(priority_queue, Task(2, 'task 2'))
heapq.heappush(priority_queue, Task(1, 'task 1'))
heapq.heappush(priority_queue, Task(3, 'task 3'))

while priority_queue:
    task = heapq.heappop(priority_queue)
    print(task.description)

Output:

Image4

Using Priority Queue in a Multi-threaded Environment

Python's queue.PriorityQueue is a thread-safe priority queue that can be used in multi-threaded applications.

import threading
import queue

def worker(task_queue):
    while not task_queue.empty():
        priority, task = task_queue.get()
        print(f'Processing {task}')
        task_queue.task_done()

task_queue = queue.PriorityQueue()
task_queue.put((2, 'task 2'))
task_queue.put((1, 'task 1'))
task_queue.put((3, 'task 3'))

threads = []
for i in range(3):
    thread = threading.Thread(target=worker, args=(task_queue,))
    thread.start()
    threads.append(thread)

for thread in threads:
    thread.join()

task_queue.join()

Output:

0b1e61bc Dbf4 4b1c 9dec 9bcba64a99bb

Conclusion

Priority queues are essential for efficiently managing tasks and resources based on priority. Python's heapq and queue.PriorityQueue modules provide powerful tools to implement and manipulate priority queues. Whether for simple tasks or complex algorithms like Dijkstra's, understanding how to use priority queues effectively can significantly enhance your programming toolkit. 

If you want to build a web service using Python, you can rent a cloud server at competitive prices with Hostman.

For further reading and additional resources, consider the following:

Python
10.07.2024
Reading time: 6 min

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Understanding the main() Function in Python

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As a program grows, the logic quickly becomes tangled and demands re‑organization: # function containing the program’s main logic (entry point) def main():     print("Hello, world!") # launch the main logic if __name__ == "__main__":     main()                    # call the function with the main logic With more actions the code might look like: def main(): print("Hello, world!") print("How are you, world?") print("Good‑bye, world...") if __name__ == "__main__": main() This implementation has several important aspects, discussed below. The main() Function The core program logic lives inside a separate function. Although the name can be anything, developers usually choose main, mirroring C, C++, Java, and other languages.  Both helper code and the main logic are encapsulated: nothing sits “naked” at file scope. # greeting helper def greet(name): print(f"Hello, {name}!") # program logic def main(): name = input("Enter your name: ") greet(name) # launch the program if __name__ == "__main__": main() Thus main() acts as the entry point just as in many other languages. The if __name__ == "__main__" Check Before calling main() comes the somewhat odd construct if __name__ == "__main__":.  Its purpose is to split running from importing logic: If the script runs directly, the code inside the if block executes. If the script is imported, the block is skipped. Inside that block, you can put any code—not only the main() call: if __name__ == "__main__":     print("Any code can live here, not only main()") __name__ is one of Python’s built‑in “dunder” (double‑underscore) variables, often called magic or special. All dunder objects are defined and used internally by Python, but regular users can read them too. Depending on the context, __name__ holds: "__main__" when the module runs as a standalone script. The module’s own name when it is imported elsewhere. This lets a module discover its execution context. Advantages of Using  main() Organization Helper functions and classes, as well as the main function, are wrapped separately, making them easy to find and read. Global code is minimal—only initialization stays at file scope: def process_data(data): return [d * 2 for d in data] def main(): raw = [1, 2, 3, 4] result = process_data(raw) print("Result:", result) if __name__ == "__main__": main() A consistent style means no data manipulation happens at the file level. Even in a large script you can quickly locate the start of execution and any auxiliary sections. Isolation When code is written directly at the module level, every temporary variable, file handle, or connection lives in the global namespace, which can be painful for debugging and testing. Importing such a module pollutes the importer’s globals: # executes immediately on import values = [2, 4, 6] doubles = [] for v in values: doubles.append(v * 2) print("Doubled values:", doubles) With main() everything is local; when the function returns, its variables vanish: def double_list(items): return [x * 2 for x in items] # create a new list with doubled elements def main(): values = [2, 4, 6] result = double_list(values) print("Doubled values:", result) if __name__ == "__main__": main() That’s invaluable for unit testing, where you might run specific functions (including  main()) without triggering the whole program. Safety Without the __name__ check, top‑level code runs even on import—usually undesirable and potentially harmful. some.py: print("This code will execute even on import!") def useful_function(): return 42 main.py: import some print("The logic of the imported module executed itself...") Console: This code will execute even on import! The logic of the imported module executed itself... The safer some.py: def useful_function():     return 42 def main():     print("This code will not run on import") main() plus the __name__ check guard against accidental execution. Inside main() you can also verify user permissions or environment variables. How to Write main() in Python Remember: main() is not a language construct, just a regular function promoted to “entry point.” To ensure it runs only when the script starts directly: Tools – define helper functions with business logic. Logic – assemble them inside main() in the desired order. Check – add the if __name__ == "__main__" guard.  This template yields structured, import‑safe, test‑friendly code—excellent practice for any sizable Python project. Example Python Program Using main() # import the standard counter from collections import Counter # runs no matter how the program starts print("The text‑analysis program is active") # text‑analysis helper def analyze_text(text): words = text.split() # split text into words total = len(words) # total word count unique = len(set(words)) # unique word count avg_len = sum(len(w) for w in words) / total if total else 0 freq = Counter(words) # build frequency counter top3 = freq.most_common(3) # top three words return { 'total': total, 'unique': unique, 'avg_len': avg_len, 'top3': top3 } # program’s main logic def main(): print("Enter text (multiple lines). Press Enter on an empty line to finish:") lines = [] while True: line = input() if not line: break lines.append(line) text = ' '.join(lines) stats = analyze_text(text) print(f"\nTotal number of words: {stats['total']}") print(f"Unique words: {stats['unique']}") print(f"Average word length: {stats['avg_len']:.2f}") print("Top‑3 most frequent words:") for word, count in stats['top3']: print(f" {word!r}: {count} time(s)") # launch program if __name__ == "__main__": main() Running the script prints a prompt: Enter text (multiple lines). Press Enter on an empty line to finish: Input first line: Star cruiser Orion glided silently through the darkness of intergalactic space. Second line: Signals of unknown life‑forms flashed on the onboard sensors where the nebula glowed with a phosphorescent light. Third line: The cruiser checked the sensors, then the cruiser activated the defense system, and the cruiser returned to its course. Console output: The text‑analysis program is active Total number of words: 47 Unique words: 37 Average word length: 5.68 Top‑3 most frequent words: 'the': 7 time(s) 'cruiser': 4 time(s) 'of': 2 time(s) If you import this program (file program.py) elsewhere: import program         # importing program.py Only the code outside main() runs: The text‑analysis program is active So, a moderately complex text‑analysis utility achieves clear logic separation and context detection. When to Use main() and When Not To Use  main() (almost always appropriate) when: Medium/large scripts – significant code with non‑trivial logic, multiple functions/classes. Libraries or CLI utilities – you want parts of the module importable without side effects. Autotests – you need to test pure logic without extra boilerplate. You can skip main() when: Tiny one‑off scripts – trivial logic for a quick data tweak. Educational snippets – short examples illustrating a few syntax features. In short, if your Python program is a standalone utility or app with multiple processing stages, command‑line arguments, and external resources—introduce  main(). If it’s a small throw‑away script, omitting main() keeps things concise. Conclusion The  main() function in Python serves two critical purposes: Isolates the program’s core logic from the global namespace. Separates standalone‑execution logic from import logic. Thus, a Python file evolves from a straightforward script of sequential actions into a fully‑fledged program with an entry point, encapsulated logic, and the ability to detect its runtime environment.
14 July 2025 · 8 min to read

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