Dequeue Using Linked List in DSA Python - Build from Scratch

Dequeue Using Linked List

📖 Scenario: Imagine you are managing a line of people waiting for a ride. Sometimes people join the line at the front, sometimes at the back. Also, people can leave from either end. This is like a double-ended queue, or dequeue, which we will build using a linked list.

🎯 Goal: You will create a dequeue data structure using a linked list. You will add people to the front and back, remove people from the front and back, and finally show the current line.

📋 What You'll Learn

Create a Node class to hold data and a pointer to the next node

Create a Dequeue class with methods to add to front and back

Create methods to remove from front and back

Print the current state of the dequeue after operations

💡 Why This Matters

🌍 Real World

Dequeue is used in real-world scenarios like task scheduling, undo operations in editors, and managing buffers where insertion and deletion happen at both ends.

💼 Career

Understanding dequeue implementation helps in software development roles that require efficient data handling, such as backend development, systems programming, and algorithm design.

1

Create Node and Dequeue Classes

Create a class called Node with an __init__ method that takes data and sets self.data = data and self.next = None. Then create a class called Dequeue with an __init__ method that sets self.front = None and self.rear = None.

DSA Python

# Create Node and Dequeue classes with __init__ methods
# Your code here

Hint

Think of Node as a box holding a person and a pointer to the next box. The Dequeue keeps track of the first and last boxes.

2

Add Methods to Insert at Front and Rear

In the Dequeue class, add a method add_front(self, data) that creates a new Node with data and adds it to the front. Also add a method add_rear(self, data) that adds a new Node to the rear. Handle the case when the dequeue is empty by setting both front and rear to the new node.

DSA Python

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class Dequeue:
    def __init__(self):
        self.front = None
        self.rear = None

    # Add a method add_front to add a node at the front
    # Your code here

    # Add a method add_rear to add a node at the rear
    # Your code here

Hint

When adding to front, point new node's next to current front. When adding to rear, point current rear's next to new node.

3

Add Methods to Remove from Front and Rear

In the Dequeue class, add a method remove_front(self) that removes the node from the front and returns its data. Also add a method remove_rear(self) that removes the node from the rear and returns its data. Handle empty dequeue by returning None. When removing the rear, traverse the list to find the node before rear.

DSA Python

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class Dequeue:
    def __init__(self):
        self.front = None
        self.rear = None

    def add_front(self, data):
        new_node = Node(data)
        if self.front is None:
            self.front = self.rear = new_node
        else:
            new_node.next = self.front
            self.front = new_node

    def add_rear(self, data):
        new_node = Node(data)
        if self.rear is None:
            self.front = self.rear = new_node
        else:
            self.rear.next = new_node
            self.rear = new_node

    # Add a method remove_front to remove node from front and return its data
    # Your code here

    # Add a method remove_rear to remove node from rear and return its data
    # Your code here

Hint

To remove from front, move front pointer to next node. To remove from rear, find the node before rear and update rear pointer.

4

Print the Current Dequeue

Add a method print_dequeue(self) in the Dequeue class that prints the data of all nodes from front to rear separated by -> and ending with null. Then create a Dequeue object, add 10 to front, add 20 to rear, add 5 to front, remove one from rear, and finally print the dequeue.

DSA Python

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class Dequeue:
    def __init__(self):
        self.front = None
        self.rear = None

    def add_front(self, data):
        new_node = Node(data)
        if self.front is None:
            self.front = self.rear = new_node
        else:
            new_node.next = self.front
            self.front = new_node

    def add_rear(self, data):
        new_node = Node(data)
        if self.rear is None:
            self.front = self.rear = new_node
        else:
            self.rear.next = new_node
            self.rear = new_node

    def remove_front(self):
        if self.front is None:
            return None
        removed_data = self.front.data
        self.front = self.front.next
        if self.front is None:
            self.rear = None
        return removed_data

    def remove_rear(self):
        if self.rear is None:
            return None
        if self.front == self.rear:
            removed_data = self.rear.data
            self.front = self.rear = None
            return removed_data
        current = self.front
        while current.next != self.rear:
            current = current.next
        removed_data = self.rear.data
        self.rear = current
        self.rear.next = None
        return removed_data

    # Add a method print_dequeue to print all nodes from front to rear
    # Your code here

# Create a Dequeue object called dq
# Add 10 to front
# Add 20 to rear
# Add 5 to front
# Remove one from rear
# Print the dequeue
# Your code here

Hint

Traverse from front to rear, collect data, and print with arrows. Follow the steps exactly to get the final output.

Practice

1. What is a dequeue (double-ended queue) implemented using a linked list?

easy

A. A queue where elements can only be added at the rear

B. A list that only allows insertion at the front

C. A stack implemented using arrays

D. A data structure where elements can be added or removed from both ends

Solution

1. Step 1: Understand dequeue operations

   A dequeue allows insertion and deletion from both front and rear ends.

2. Step 2: Linked list implementation

   Using a linked list, nodes can be added or removed from both ends efficiently.

3. Final Answer:

   A data structure where elements can be added or removed from both ends -> Option D

4. Quick Check:

   Dequeue = double-ended queue [OK]

Hint: Dequeue means double-ended queue, both ends allowed [OK]

Common Mistakes:

• Confusing dequeue with stack or simple queue
• Thinking insertion is only at one end
• Assuming array implementation only

2. Which of the following is the correct way to initialize an empty dequeue using a linked list in Python?

easy

A. class Dequeue: def __init__(self): self.front = None; self.rear = None

B. class Dequeue: def __init__(self): self.list = []

C. class Dequeue: def __init__(self): self.head = 0; self.tail = 0

D. class Dequeue: def __init__(self): self.front = 0; self.rear = 0

Solution

1. Step 1: Identify linked list pointers

   For linked list, front and rear pointers are initialized to None to indicate empty.

2. Step 2: Check options

   class Dequeue: def __init__(self): self.front = None; self.rear = None correctly initializes front and rear as None; others use incorrect types or structures.

3. Final Answer:

   class Dequeue: def __init__(self): self.front = None; self.rear = None -> Option A

4. Quick Check:

   Linked list empty pointers = None [OK]

Hint: Empty linked list pointers are None, not zero or list [OK]

Common Mistakes:

• Using list instead of linked list
• Initializing pointers with 0 instead of None
• Confusing front/rear with indices

3. What will be the output after performing these operations on an empty dequeue implemented with linked list?
dq.insert_rear(10)
dq.insert_front(20)
dq.insert_rear(30)
dq.delete_front()
Print the dequeue from front to rear.

medium

A. 20 -> 10 -> 30 -> null

B. 20 -> 30 -> null

C. 10 -> 30 -> null

D. 30 -> 10 -> 20 -> null

Solution

1. Step 1: Perform insert_rear(10) on empty dequeue

   Dequeue becomes: 10 -> null

2. Step 2: Perform insert_front(20)

   Dequeue becomes: 20 -> 10 -> null

3. Step 3: Perform insert_rear(30)

   Dequeue becomes: 20 -> 10 -> 30 -> null

4. Step 4: Perform delete_front()

   Remove 20 from front, dequeue becomes: 10 -> 30 -> null

5. Final Answer:

   10 -> 30 -> null -> Option C

6. Quick Check:

   After operations, front to rear = 10, 30 [OK]

Hint: Track each operation stepwise front to rear [OK]

Common Mistakes:

• Forgetting delete_front removes front element
• Mixing order of insert_front and insert_rear
• Printing rear to front instead of front to rear

4. Identify the error in this dequeue linked list method for deleting from rear:

def delete_rear(self):
    if self.rear is None:
        return None
    temp = self.rear
    self.rear = self.rear.next
    return temp.data

medium

A. Does not check if dequeue is empty

B. Incorrectly moves rear pointer forward instead of backward

C. Returns data before deleting node

D. Uses wrong variable name for rear

Solution

1. Step 1: Understand rear pointer movement

   In linked list, rear points to last node; to delete rear, rear must move backward (to previous node).

2. Step 2: Analyze code

   Code moves rear to rear.next (forward), which is incorrect for rear deletion.

3. Final Answer:

   Incorrectly moves rear pointer forward instead of backward -> Option B

4. Quick Check:

   Rear deletion moves pointer backward [OK]

Hint: Rear deletion moves pointer backward, not forward [OK]

Common Mistakes:

• Moving rear pointer forward instead of backward
• Not handling empty dequeue properly
• Confusing front and rear pointers

5. You want to implement a dequeue using a doubly linked list to support these operations efficiently:
1. Insert at front
2. Insert at rear
3. Delete from front
4. Delete from rear

Which of the following is the best approach to maintain pointers for O(1) time complexity for all operations?

hard

A. Maintain front and rear pointers; each node has next and prev links

B. Maintain only front pointer; traverse list for rear operations

C. Use singly linked list with only next pointers and rear pointer

D. Use array to simulate linked list with indices

Solution

1. Step 1: Understand operation requirements

   All operations must be O(1), so no traversal allowed.

2. Step 2: Analyze pointer needs

   Doubly linked list with front and rear pointers allows insert/delete at both ends in O(1).

3. Step 3: Check options

   Maintain front and rear pointers; each node has next and prev links matches this approach; others require traversal or use arrays which are not linked lists.

4. Final Answer:

   Maintain front and rear pointers; each node has next and prev links -> Option A

5. Quick Check:

   Doubly linked list + front/rear pointers = O(1) ops [OK]

Hint: Use doubly linked list with front and rear pointers [OK]

Common Mistakes:

• Using singly linked list causing O(n) rear operations
• Maintaining only one pointer causing traversal
• Confusing arrays with linked lists