Explain different non primitive data structures and operations associated with them.

Non-Primitive Data Structures:

Non-primitive data structures are complex data types that store and organize data in a structured manner. These data structures allow efficient storage, retrieval, and manipulation of data.

1. Arrays:

• Definition: An array is a collection of similar data elements stored at contiguous memory locations. Each element is identified by an index, which is used to access the element.
• Operations:
  • Traversal: Iterating over each element in the array.
  • Insertion: Adding an element to the array.
  • Deletion: Removing an element from the array.
  • Searching: Finding an element in the array.
  • Sorting: Arranging the elements in a specific order.

2. Linked Lists:

• Definition: A linked list is a linear data structure where elements are linked together using pointers. Each element contains a data field and a pointer to the next element.
• Operations:
  • Traversal: Iterating over each element in the linked list.
  • Insertion: Adding an element to the linked list.
  • Deletion: Removing an element from the linked list.
  • Searching: Finding an element in the linked list.

3. Stacks:

• Definition: A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle. Elements are added and removed from the top of the stack.
• Operations:
  • Push: Adding an element to the top of the stack.
  • Pop: Removing an element from the top of the stack.
  • Top: Retrieving the element at the top of the stack.

4. Queues:

• Definition: A queue is a linear data structure that follows the First-In-First-Out (FIFO) principle. Elements are added to the rear of the queue and removed from the front.
• Operations:
  • Enqueue: Adding an element to the rear of the queue.
  • Dequeue: Removing an element from the front of the queue.
  • Front: Retrieving the element at the front of the queue.

5. Trees:

• Definition: A tree is a hierarchical data structure where data is organized into nodes connected by edges. Nodes can have multiple child nodes, and each node has a parent node, except for the root node.
• Operations:
  • Traversal: Visiting each node in the tree using various traversal techniques (e.g., depth-first search, breadth-first search).
  • Insertion: Adding a new node to the tree.
  • Deletion: Removing a node from the tree.
  • Searching: Finding a node in the tree.

6. Graphs:

• Definition: A graph is a non-linear data structure consisting of a set of vertices and a set of edges connecting these vertices. Vertices represent data elements, and edges represent relationships between data elements.
• Operations:
  • Traversal: Visiting each vertex in the graph using various traversal techniques (e.g., depth-first search, breadth-first search).
  • Insertion: Adding a new vertex or edge to the graph.
  • Deletion: Removing a vertex or edge from the graph.
  • Searching: Finding a vertex or edge in the graph.

Each non-primitive data structure has specific characteristics and behaviors, making it suitable for different applications and scenarios. The choice of data structure depends on the specific requirements of the problem being solved.