Data Structures:

Arrays / List:

• Read: O(1)
• Insertion: O(n)
• Deletion: O(n)
• Fast at reading but slow at insertion and deletion.

Linked Lists:

• Read: O(n)
• Insertion: O(1)
• Deletion: O(1)
• Slow at reading but efficient for insertion and deletion.

HashMaps:

• Read: O(1)
• Insertion: O(1)
• Deletion: O(1)
• Similar to arrays but with named indexes (keys); unordered but provide fast lookup.

hash map use linked list and array list for fast memory access and collision handling

Stacks:

• Push: O(1)
• Pop: O(1)
• Peak: O(1)
• Follow the LIFO (Last In, First Out) principle; useful for fast retrieval of the topmost element but can be cumbersome for inserting or deleting elements in the middle or end.

Queues:

• Enqueue: O(1)
• Dequeue: O(1)
• Front: O(1)
• Follow the FIFO (First In, First Out) principle; the first element in line is the first to come out. Think of them as playlists for organizing items in order of arrival.

Trees:

• Read/Search: O(log n)
• Insertion: O(log n)
• Deletion: O(log n)
• Nodes connected by edges; root, parent-child connections.

Binary Trees:

• Efficient searching of ordered values.
• Follow a binary search property where left child nodes are less than the parent and right child nodes are greater.
• Useful for tasks like number guessing games or dictionary implementations.

Graphs:

• Traversal/Search: O(V + E) (V: number of vertices, E: number of edges)
• Insertion: O(1)
• Deletion: O(1)
• Versatile models for connections between nodes and edges; can be directed or undirected with no neighboring limit. Can include cycles and weights on paths. Used for tasks like route optimizationData Structures: