8/25/2023 0 Comments Python priority queue heapReturn str((self.priority, scription, self.customer))įor our customers, the list of tasks might look something like this: Task((2022, 'Delivery delay', ('', 'Jennifer Taylor'))) Self.priority = customer.get_signup_year() class Task():ĭef _init_(self, customer, description): Let's now create the Task class which will hold a customer and the description of why they are in the queue. Running this, we get the following output: ('', 'Jennifer Taylor') Let's start by defining the Customer class and generating some instances: import randomįirst_names = For prioritization, we'll simply use the year of signup. It will contain a reference to a customer and some details about their request. To make things more realistic, each item we push to the queue be an instance of the Task class. To make it more fair, we also want the queue to be stable with respect to the insertion order - if two or more customers with the same priority are in the queue, we must serve them in the order of arrival. We'll use it to reveal two important problems with our tuple-based solution from the previous section.Īssume we have a customer support center, and we want to implement a queueing policy that prioritizes requests from older customers (those who signed up for our service earlier). Resolving equal priorities and order stability We'll use priority queue this way in our next example. If we want to reverse this, we can simply use the negative of the priority value when adding elements to the queue.Īs a more robust solution, we can wrap our queue items in a custom class with overloaded comparison operators implemented to give us the desired ordering. That means the smallest priority value always comes out first. Technically, PriorityQueue is implemented using a min-heap data structure. That is priority 0 is higher than priority 1. Note that by default, the lower the value of the priority number, the higher the priority of the entry. Print('Getting items from the priority queue') # They will be printed in the order of their priority # Let's get items from the priority queue one by one # We add items to priority queue in the order they're listed Items = įor item, priority in zip(items, priorities): Here's a dummy example of how to use it: import random In the simplest case, an entry in the priority queue will be a tuple (priority_number, data). Python comes with a built-in PriorityQueue class, contained in the queue module. Priority queue in Python? Short answer: use queue.PriorityQueue A queue is a first in, first out (FIFO) data structure, whereas in a priority queue the element with the highest priority is served before lower priority elements. If another thread modifies the queue whilst it is being iterated over then the above method will start yielding the wrong numbers and if you're lucky it may produce an exception.Priority queue is a data structure similar to a queue, but where each element has an associated priority. It should be noted that the above method is not thread safe. The method can be monkey patched onto queue.PriorityQueue if you're feeling lazy, but I would encourage you to implement your own priority queue class using the heapq module as the PriorityQueue comes with a lot of excess functionality (mainly it is thread safe which almost certainly don't need). Min_index = min(next_indices, key=q._getitem_) For instance: def iter_priority_queue(queue): Thus, to iterate over the queue you need to keep track of all the currently "viewable" nodes. That is, the third item in the queue (25) could have been in the same position as either 19 or 1. However, the third item could either be the remaining node of the root node, or either of the children of the second node in the queue. The second item is the larger of the root's children. However, the only requirement of a heap is that all a node's children have a value greater than or equal to the node's value (or greater than depending on the implementation).įor instance, in the above linked wiki page about binrary heap's you'll find the following image. Where i is the index of the nodes in the list then it's children are stored at: All nodes that appear after the last node to have children will have zero children (duh).Ī node's children are stored in relation to the node's own position in the list. The rules being that all nodes have two children, unless they are the last node to have children in which case they may have one child instead. To iterate over the queue you need to know the rules about where children are stored in the list. The PriorityQueue is implemented as binary heap, which is implemented using a list (array) in python.
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