Types of data structure - linear and non linear data structure

Types of data structure- linear data structure and non linear data structure

1. Linear Data Structures:

Linear data structures organize and store data elements sequentially, meaning each element is connected to its previous and next elements in a linear order.

Examples include arrays, linked lists, stacks, and queues.

Arrays store elements in contiguous memory locations and allow random access to elements based on their index.

Linked lists consist of nodes where each node contains data and a reference to the next node, allowing dynamic memory allocation and efficient insertion/deletion operations.

 Stacks and queues are abstract data types that follow specific order rules: Last In, First Out (LIFO) for stacks and First In, First Out (FIFO) for queues.

2. Non-linear Data Structures:

  

Non-linear data structures organize and store data elements in a hierarchical or non-sequential manner, where each element may have multiple connections to other elements Examples include trees, graphs, and hash tables.

  Tree - Trees are hierarchical data structures consisting of nodes where each node has a parent-child relationship, with the top node being the root. 

   Graph- Graphs represent connections between nodes (vertices) using edges. They can be directed or undirected and are useful for modeling networks and relationships.

   Hash table- Hash tables store key-value pairs and use a hash function to compute an index where the value associated with the key is stored. They provide fast insertion, deletion, and lookup operations, typically with an average time complexity of O(1).

These data structures serve different purposes and are chosen based on factors such as the nature of the data, the operations required, and the efficiency considerations for various operations.

When to use which data structure? 

1.Arrays: Use when you need constant-time access to elements by index, and the size is fixed or known beforehand.

2.Lists: Choose lists when you need a collection of elements that can grow or shrink dynamically.

3. Stacks :  Ideal for implementing Last-In-First-Out (LIFO) behavior, such as in function call management or backtracking algorithms.

4. Queues :  Use queues for First-In-First-Out (FIFO) behavior, like managing tasks in a system or implementing breadth-first search.

5. Sets : Great for storing unique elements without any particular order, useful for tasks like removing duplicates or checking membership.

6. Maps (or Dictionaries): Use when you need to associate keys with values, like storing information about entities with unique identifiers.

7. Trees: Opt for trees when you need hierarchical data structures, like organizing data in a hierarchical manner or implementing algorithms like binary search.

8. Graphs: Choose graphs for modeling relationships between objects, such as social networks, transportation networks, or dependency management.

Remember, the choice of data structure depends on the problem you're solving and the operations you'll perform most frequently.