Day 12

Day 12: Introduction to Queues

Intermediate 12 min

12/60 Days

Introduction to Queues#

Welcome to Day 12 of our 60 Days of Coding Algorithm Challenge! Today, we’ll explore queues, another fundamental data structure that follows the First-In-First-Out (FIFO) principle.

What is a Queue?#

A queue is a linear data structure that follows a particular order in which operations are performed. The order is First In First Out (FIFO). A good example of a queue is any queue of consumers for a resource where the consumer that came first is served first.

Basic Operations of a Queue#

Enqueue: Adds an element to the rear of the queue
Dequeue: Removes the element from the front of the queue
Front: Get the front element from the queue without removing it
Rear: Get the last element from the queue without removing it
isEmpty: Check if the queue is empty

Implementing a Queue in Python#

We can implement a queue using a Python list or create a custom class. Let’s implement both:

Using a Python List#

 1class Queue:
 2    def __init__(self):
 3        self.items = []
 4
 5    def is_empty(self):
 6        return len(self.items) == 0
 7
 8    def enqueue(self, item):
 9        self.items.append(item)
10
11    def dequeue(self):
12        if not self …

Introduction to Queues#

Welcome to Day 12 of our 60 Days of Coding Algorithm Challenge! Today, we’ll explore queues, another fundamental data structure that follows the First-In-First-Out (FIFO) principle.

What is a Queue?#

Basic Operations of a Queue#

Enqueue: Adds an element to the rear of the queue
Dequeue: Removes the element from the front of the queue
Front: Get the front element from the queue without removing it
Rear: Get the last element from the queue without removing it
isEmpty: Check if the queue is empty

Implementing a Queue in Python#

We can implement a queue using a Python list or create a custom class. Let’s implement both:

Using a Python List#

 1class Queue:
 2    def __init__(self):
 3        self.items = []
 4
 5    def is_empty(self):
 6        return len(self.items) == 0
 7
 8    def enqueue(self, item):
 9        self.items.append(item)
10
11    def dequeue(self):
12        if not self.is_empty():
13            return self.items.pop(0)
14        else:
15            raise IndexError("Queue is empty")
16
17    def front(self):
18        if not self.is_empty():
19            return self.items[0]
20        else:
21            raise IndexError("Queue is empty")
22
23    def rear(self):
24        if not self.is_empty():
25            return self.items[-1]
26        else:
27            raise IndexError("Queue is empty")
28
29    def size(self):
30        return len(self.items)

Using a Custom Node Class#

 1class Node:
 2    def __init__(self, data):
 3        self.data = data
 4        self.next = None
 5
 6class Queue:
 7    def __init__(self):
 8        self.front = None
 9        self.rear = None
10
11    def is_empty(self):
12        return self.front is None
13
14    def enqueue(self, item):
15        new_node = Node(item)
16        if self.rear is None:
17            self.front = self.rear = new_node
18            return
19        self.rear.next = new_node
20        self.rear = new_node
21
22    def dequeue(self):
23        if self.is_empty():
24            raise IndexError("Queue is empty")
25        dequeued = self.front.data
26        self.front = self.front.next
27        if self.front is None:
28            self.rear = None
29        return dequeued
30
31    def get_front(self):
32        if self.is_empty():
33            raise IndexError("Queue is empty")
34        return self.front.data
35
36    def get_rear(self):
37        if self.is_empty():
38            raise IndexError("Queue is empty")
39        return self.rear.data
40
41    def size(self):
42        count = 0
43        current = self.front
44        while current:
45            count += 1
46            current = current.next
47        return count

Time Complexity#

Enqueue operation: O(1)
Dequeue operation: O(1) for linked list implementation, O(n) for array implementation
Front operation: O(1)
Rear operation: O(1)

Applications of Queues#

CPU Scheduling: Manages processes waiting to be executed
Breadth-First Search: Used in graph algorithms
Handling of requests on a single shared resource: Printer, CPU task scheduling
Buffering: In streaming media, routers, etc.
Asynchronous data transfer: IO Buffers, pipes, file IO, etc.

Example: Level Order Traversal of a Binary Tree#

Let’s implement a function to perform level order traversal of a binary tree using a queue:

 1from collections import deque
 2
 3class TreeNode:
 4    def __init__(self, value):
 5        self.value = value
 6        self.left = None
 7        self.right = None
 8
 9def level_order_traversal(root):
10    if not root:
11        return []
12    
13    result = []
14    queue = deque([root])
15    
16    while queue:
17        level_size = len(queue)
18        current_level = []
19        
20        for _ in range(level_size):
21            node = queue.popleft()
22            current_level.append(node.value)
23            
24            if node.left:
25                queue.append(node.left)
26            if node.right:
27                queue.append(node.right)
28        
29        result.append(current_level)
30    
31    return result
32
33# Test the function
34root = TreeNode(1)
35root.left = TreeNode(2)
36root.right = TreeNode(3)
37root.left.left = TreeNode(4)
38root.left.right = TreeNode(5)
39
40print(level_order_traversal(root))  # Output: [[1], [2, 3], [4, 5]]

Exercise#

Implement a circular queue using an array.
Create a function to reverse the first K elements of a queue.
Implement a stack using two queues.

Summary#

Today, we explored the queue data structure, its implementation, and a practical application in tree traversal. Queues are essential in many algorithms and real-world applications due to their FIFO nature. Understanding queues will help you solve various problems efficiently, especially in scenarios involving ordered processing or breadth-first algorithms.

Tomorrow, we’ll begin our exploration of trees, starting with an introduction to tree data structures and their properties. Stay tuned!

Solution#

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Show the solution

 1class CircularQueue:
 2    def __init__(self, capacity):
 3        self.capacity = capacity
 4        self.items = [None] * capacity
 5        self.front = 0
 6        self.count = 0
 7
 8    def is_full(self):
 9        return self.count == self.capacity
10
11    def enqueue(self, value):
12        if self.is_full():
13            raise OverflowError("Queue is full")
14        rear = (self.front + self.count) % self.capacity
15        self.items[rear] = value
16        self.count += 1
17
18    def dequeue(self):
19        if self.count == 0:
20            raise IndexError("Queue is empty")
21        value = self.items[self.front]
22        self.front = (self.front + 1) % self.capacity
23        self.count -= 1
24        return value
25
26
27def reverse_first_k(queue, k):
28    stack = []
29    for _ in range(k):
30        stack.append(queue.pop(0))   # pop first k into a stack
31    while stack:
32        queue.append(stack.pop())    # push them back reversed
33    for _ in range(len(queue) - k):
34        queue.append(queue.pop(0))   # rotate the rest to the back
35    return queue
36
37
38class StackUsingQueues:
39    def __init__(self):
40        from collections import deque
41        self.q = deque()
42
43    def push(self, value):
44        self.q.append(value)
45        for _ in range(len(self.q) - 1):
46            self.q.append(self.q.popleft())  # rotate so newest is at front
47
48    def pop(self):
49        return self.q.popleft()
50
51
52print(reverse_first_k([1, 2, 3, 4, 5], 3))  # [3, 2, 1, 4, 5]

CircularQueue reuses a fixed array with modular arithmetic, so enqueue and dequeue are O(1) time with O(capacity) space. reverse_first_k uses a stack to flip the first k elements, then rotates the remaining ones to the back: O(n) time and O(k) space. The stack from two queues makes push O(n) (it rotates the newest element to the front) while pop stays O(1).

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