Self.rear = int((self.rear + 1) % self.max_size) Self.queue = for i in range(5)] #creates a list Queue implementation using list in Python, handling enqueue and dqueue as per inbuild queue data structure: class queue:ĭef _init_(self, max_size, size=0, front=0, rear=0): Just use deque anywhere a single- or double-ended queue is required. iteration, pickling, len(d), reversed(d), py(d), epcopy(d), membership testing with the in operator, and subscript references such as d. The deque class provides significantly more features, including: The Queue class defined above is given only as a trivial demonstration of the general-purpose utility of the deque API. You're better off just using a raw deque object rather than attempting to manually encapsulate that object in a Queue wrapper. The proof is in the hellish pudding: > queue = Queue() Is silently removed from this queue *before* the passed item isĭequeues (i.e., removes) the item at the head of this queue *and* If this queue is already full, the item at the head of this queue Queues the passed item (i.e., pushes this item onto the tail of this Maximum number of items contained in this queue. Initialize this queue to the empty queue. Thread-safe, memory-efficient, maximally-sized queue supporting queueing and Instead, implement your Queue class in terms of the standard que type as follows: from collections import deque
#Enqueue python array manual#
More critically, deque also provides out-of-the-box support for a maximum length via the maxlen parameter passed at initialization time, obviating the need for manual attempts to limit the queue size (which inevitably breaks thread safety due to race conditions implicit in if conditionals). Though list objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs for pop(0) and insert(0, v) operations which change both the size and position of the underlying data representation. To quote the official deque documentation:
But who cares? enqueue() remains pitifully slow. Since removing the last item from a C-based array and hence Python list is a constant-time operation, implementing the dequeue() method in terms of a Python list retains the same worst-case time complexity of O(1). As I note below, implementing the enqueue() methods in terms of a Python list increases its worst-case time complexity to O(n). It's also thread-safe and presumably more space and time efficient, given its C-based heritage. While doing so reduces the worst-case time complexity of your dequeue() and enqueue() methods to O(1), the que type already does so. What Not to DoĪvoid reinventing the wheel by hand-rolling your own: As Uri Goren astutely noted above, the Python stdlib already implemented an efficient queue on your fortunate behalf: que.
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