A queue is a first-in-first-out arrangement whereby objects are removed or accessible according to first-come, first-served priority. A line at the movie ticket booth is an illustration of a queue. So, what then is a priority queue?

In Python, a priority queue is an abstract data structure or a data structure defined by its actions. Thus, it’s like a normal queue but has unique “keys” assigned to each item to indicate its priority.

For instance, if the movie theater chooses to give its most loyal customers priority, it will arrange them according to the number of tickets they have bought or their loyalty points. In that instance, the ticket line will be most-loyal-first-served instead of first-come, first-served.

The “items” of this priority queue will be the customers. Then their loyalty will be the “key” or “priority”. Moreover, this piece will give you an insight into priority queues in Python.

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**What is a Priority Queue in Python?**

A priority queue in Python is a data structure that enables you to insert objects with a priority value and access the object with the highest priority first in time. Some key things to know about priority queues in Python include the following:

- They are typically implemented using the heapq module. This module provides a min heap implementation that can be used as a priority queue.

- Items are inserted into the Python priority queue using `heapq.heappush(pq, (priority, item))`. Hence, the priority is used to determine order, so higher priority items will be retrieved first.

- The highest priority item can be retrieved with `heapq.heappop(pq)`. This pops and returns the highest priority item.

- Items in the heap are stored based on the priority order. So, the priority queue returns items based on priority order. Thus, higher-priority items are returned before lower-priority items.

- The Python heapq module implements a min heap, so the lowest numeric queue priority will be returned first. However, negative priorities can be used to emulate a max heap.

Overall, it’s an ordered collection that allows efficient access to the “highest priority” item. Using heapq allows it to be implemented efficiently in Python.

**How To Build a Priority Queue in Python **

You can build a priority queue in Python in these three ways:

- Using list: This method works well if you don’t need to make a lot of insertions.

- Using heapq: This variant allows for O(logn) time for both the smallest element and insertion.

- Making use of queue.PriorityQueue: This class interface facilitates concurrent processing.

**How To Implement Priority Queues in Python**

Let’s assume that we want to have a priority queue of customers according to their loyalty points. So, their priority increases with the number of points.

However, there are several ways that priority queues can be implemented in Python. Here, we’ll look at three of them.

**1.** **Using a List**

Using the standard list and sorting it each time a new item is added is a straightforward method. When an item is added to the list, though, maintaining the order takes O(n log n) time. However, it works well when we don’t require many insertions.

**2.** **Using Heapq**

We can alternatively implement our priority queue using the Python heapq module. The smallest element can be inserted and extracted in O(log n) time using this implementation. Remember that heapq only provides a min heap implementation. However, you can use max heap in other ways.

**3.** **Using Queue.PriorityQueue**

The PriorityQueue has the same temporal complexity because it internally uses the same heapq implementation. There are two main differences, though. First, it’s synchronized, thus it enables concurrent processes. Secondly, unlike heapq’s function-based interface, this one is a class interface. As a result, PriorityQueue represents the traditional approach to building and utilizing priority queues in object-oriented programming (OOP).

So, these are different ways you can implement priority queues in Python.

**Key Differences Between Priority Queues And Normal Queues**

We’ll explore the key Differences between priority queues and normal queues below.

- In the normal queue, the oldest element is dequeued first. While, in the priority queue, an element based on the highest priority is dequeued.

- When elements are taken out of a priority queue, the result obtained is either sorted in increasing order or in decreasing Order. While, when elements are popped from a simple queue, a first-in-first-out (FIFO) order of data is obtained in the result.

- In a normal queue, the FIFO rule is implemented. However, in a priority queue, the values are popped out based on priority. So, the element with the topmost priority is removed first.

**Advantages of Priority Queue in Python**

Here’s a roundup of the advantages of priority queues in Python:

- It facilitates quicker access to the elements. This is so that the highest priority element can be quickly retrieved without having to look through the whole queue since elements in a priority queue are arranged according to priority.

- In a priority queue, the elements are arranged dynamically. A priority queue can dynamically rearrange itself when priorities change by allowing elements to have their priority values modified.

- Implementing effective algorithms is possible. Many algorithms, such as the A* search algorithm for pathfinding and Dijkstra’s algorithm for determining the shortest route in a graph, use priority queues to increase their efficiency.

- Incorporated in real-time systems. This is so that you may promptly obtain the element with the highest priority using priority queues. So, they are typically used in real-time systems where time is essential.

**Disadvantages of Priority Queue in Python**

Here’s a roundup of the disadvantages of priority queues in Python:

- High complexity. Compared to simple data structures like arrays and linked lists, priority queues are more complicated and may be more challenging to implement and manage.

- High memory consumption. It can require more memory to store the priority value for every element in a priority queue. This could be problematic for systems with constrained resources.

- It’s not necessarily the best data format in terms of efficiency. Certain tasks, including determining the minimum or maximum element in the queue, may occasionally be more effectively performed using alternative data structures, such as heaps or binary search trees.

- At times it is less predictable. This is because the priority values of the elements in a priority queue dictate their order. Therefore, the order in which elements are retrieved may be less predictable compared to other data structures like queues or stacks, which follow a FIFO or LIFO (last-in-last-out) order.

**Conclusion**

Priority queues are a useful data structure in Python when you need to frequently access elements by their priority level. Further, the heapq module provides an efficient min heap implementation that handles the details of ordering elements by priority for you.

So, just initialize a list, heapify it into a min heap, and you have an efficient priority queue ready to use. Additionally, functions like heappush and heappop make it easy to enqueue and dequeue elements while maintaining the priority order.