DSAverse

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Sorting Algorithm

Binary Tree

Graph Traversal

Preparing interactive visualizations for optimal learning experience...

DSAverse

Initializing Sorting Algorithms...

Sorting Algorithm

Binary Tree

Graph Traversal

Preparing interactive visualizations for optimal learning experience...

Back to Basics

Queue: Array Implementation

Explore how a queue data structure works using array implementation. Watch enqueue, dequeue, and peek operations in action with FIFO (First In, First Out) principle.

Interactive Operations

Array Implementation

O(1) Operations

Queue Visualization

Speed:Max Size:

Empty Queue

Size: 0 / 8

Status: Empty

Current Step:

Select an operation to begin visualization

Complexity Analysis

O(1)

Enqueue

O(n)

Dequeue*

O(1)

Peek

O(n) Space

Fixed array allocation

*O(n) due to shifting elements. Circular array implementation achieves O(1).

Queue Operations

Enqueue

Add element to the rear of queue

Dequeue

Remove and return front element

Peek/Front

View front element without removing

FIFO Principle

• First In: Elements added at the rear

• First Out: Elements removed from the front

• Order Preservation: Maintains insertion order

• Fair Processing: No element jumps the queue

Applications

• Task Scheduling: OS process scheduling

• BFS Algorithms: Graph traversal

• Print Queue: Document printing order

• Buffer: Data stream processing

• Breadth-First Search: Tree/graph exploration

Implementation

class QueueArray:
    def __init__(self, max_size):
        self.array = [None] * max_size  # Fixed-size array
        self.front = 0                  # Index of front element
        self.rear = -1                  # Index of rear element
        self.size = 0                   # Current number of elements
        self.max_size = max_size       # Maximum capacity
    
    def enqueue(self, value):
        # Check for queue overflow
        if self.size >= self.max_size:
            raise Exception("Queue Overflow")
        
        # Add element at rear
        self.rear = (self.rear + 1) % self.max_size  # Circular increment
        self.array[self.rear] = value
        self.size += 1
        return value
    
    def dequeue(self):
        # Check for queue underflow
        if self.size <= 0:
            raise Exception("Queue Underflow")
        
        # Get front element and move front pointer
        value = self.array[self.front]
        self.array[self.front] = None  # Optional: clear the slot
        self.front = (self.front + 1) % self.max_size  # Circular increment
        self.size -= 1
        return value
    
    def peek(self):
        # Check if queue is empty
        if self.size <= 0:
            raise Exception("Queue is Empty")
        
        return self.array[self.front]
    
    def is_empty(self):
        return self.size == 0
    
    def is_full(self):
        return self.size >= self.max_size
    
    def get_size(self):
        return self.size

DSAverse

Initializing Sorting Algorithms...

Sorting Algorithm

Binary Tree

Graph Traversal

Preparing interactive visualizations for optimal learning experience...

Back to Basics

Queue: Array Implementation

Explore how a queue data structure works using array implementation. Watch enqueue, dequeue, and peek operations in action with FIFO (First In, First Out) principle.

Interactive Operations

Array Implementation

O(1) Operations

Queue Visualization

Speed:Max Size:

Empty Queue

Size: 0 / 8

Status: Empty

Current Step:

Select an operation to begin visualization

Complexity Analysis

O(1)

Enqueue

O(n)

Dequeue*

O(1)

Peek

O(n) Space

Fixed array allocation

*O(n) due to shifting elements. Circular array implementation achieves O(1).

Queue Operations

Enqueue

Add element to the rear of queue

Dequeue

Remove and return front element

Peek/Front

View front element without removing

FIFO Principle

• First In: Elements added at the rear

• First Out: Elements removed from the front

• Order Preservation: Maintains insertion order

• Fair Processing: No element jumps the queue

Applications

• Task Scheduling: OS process scheduling

• BFS Algorithms: Graph traversal

• Print Queue: Document printing order

• Buffer: Data stream processing

• Breadth-First Search: Tree/graph exploration

Implementation

class QueueArray:
    def __init__(self, max_size):
        self.array = [None] * max_size  # Fixed-size array
        self.front = 0                  # Index of front element
        self.rear = -1                  # Index of rear element
        self.size = 0                   # Current number of elements
        self.max_size = max_size       # Maximum capacity
    
    def enqueue(self, value):
        # Check for queue overflow
        if self.size >= self.max_size:
            raise Exception("Queue Overflow")
        
        # Add element at rear
        self.rear = (self.rear + 1) % self.max_size  # Circular increment
        self.array[self.rear] = value
        self.size += 1
        return value
    
    def dequeue(self):
        # Check for queue underflow
        if self.size <= 0:
            raise Exception("Queue Underflow")
        
        # Get front element and move front pointer
        value = self.array[self.front]
        self.array[self.front] = None  # Optional: clear the slot
        self.front = (self.front + 1) % self.max_size  # Circular increment
        self.size -= 1
        return value
    
    def peek(self):
        # Check if queue is empty
        if self.size <= 0:
            raise Exception("Queue is Empty")
        
        return self.array[self.front]
    
    def is_empty(self):
        return self.size == 0
    
    def is_full(self):
        return self.size >= self.max_size
    
    def get_size(self):
        return self.size