The main methods that are useful with this class are popleft and appendleft

from collections import deque

d = deque([1, 2, 3])
p = d.popleft()        # p = 1, d = deque([2, 3])
d.appendleft(5)        # d = deque([5, 2, 3])

Creating empty deque:

dl = deque()  # deque([]) creating empty deque

Creating deque with some elements:

dl = deque([1, 2, 3, 4])  # deque([1, 2, 3, 4])

Adding element to deque:

dl.append(5)  # deque([1, 2, 3, 4, 5])

Adding element left side of deque:

dl.appendleft(0)  # deque([0, 1, 2, 3, 4, 5])

Adding list of elements to deque:

dl.extend([6, 7])  # deque([0, 1, 2, 3, 4, 5, 6, 7])

Adding list of elements to from the left side:

dl.extendleft([-2, -1])  # deque([-1, -2, 0, 1, 2, 3, 4, 5, 6, 7])

Using .pop() element will naturally remove an item from the right side:

dl.pop()  # 7 => deque([-1, -2, 0, 1, 2, 3, 4, 5, 6])

Using .popleft() element to remove an item from the left side:

dl.popleft()  # -1 deque([-2, 0, 1, 2, 3, 4, 5, 6])

Remove element by its value:

dl.remove(1)  # deque([-2, 0, 2, 3, 4, 5, 6])

Reverse the order of the elements in deque:

dl.reverse()  # deque([6, 5, 4, 3, 2, 0, -2])

Use the maxlen parameter while creating a deque to limit the size of the deque:

from collections import deque
d = deque(maxlen=3)  # only holds 3 items
d.append(1)  # deque([1])
d.append(2)  # deque([1, 2])
d.append(3)  # deque([1, 2, 3])
d.append(4)  # deque([2, 3, 4]) (1 is removed because its maxlen is 3)

The Deque is the only Python data structure with fast Queue operations (opens new window). (Note queue.Queue isn't normally suitable, since it's meant for communication between threads.) A basic use case of a Queue is the breadth first search (opens new window).

from collections import deque

def bfs(graph, root):
    distances = {}
    distances[root] = 0
    q = deque([root])
    while q:
        # The oldest seen (but not yet visited) node will be the left most one.
        current = q.popleft()
        for neighbor in graph[current]:
            if neighbor not in distances:
                distances[neighbor] = distances[current] + 1
                # When we see a new node, we add it to the right side of the queue.
                q.append(neighbor)
    return distances

Say we have a simple directed graph:

graph = {1:[2,3], 2:[4], 3:[4,5], 4:[3,5], 5:[]}

We can now find the distances from some starting position:

>>> bfs(graph, 1)
{1: 0, 2: 1, 3: 1, 4: 2, 5: 2}

>>> bfs(graph, 3)
{3: 0, 4: 1, 5: 1}

Syntax

• dq = deque() # Creates an empty deque
• dq = deque(iterable) # Creates a deque with some elements
• dq.append(object) # Adds object to the right of the deque
• dq.appendleft(object) # Adds object to the left of the deque
• dq.pop() -> object # Removes and returns the right most object
• dq.popleft() -> object # Removes and returns the left most object
• dq.extend(iterable) # Adds some elements to the right of the deque
• dq.extendleft(iterable) # Adds some elements to the left of the deque

Parameters

Remarks

This class is useful when you need an object similar to a list (opens new window) that allows fast append and pop operations from either side (the name deque stands for “double-ended queue”).

The methods provided are indeed very similar, except that some like pop, append, or extend can be suffixed with left. The deque data structure should be preferred to a list if one needs to frequently insert and delete elements at both ends because it allows to do so in constant time O(1).