Data structures can be broadly classified based on how they organize and store data. Understanding this classification helps in choosing the right structure for specific tasks, improving both efficiency and clarity in programming.
1. Classification Based on Structure
A. Primitive Data Structures
These are the basic, built-in types provided by most programming languages.
- Examples:
- Integer (
int) - Float (
float) - Character (
char) - Boolean (
True/False)
- Integer (
These serve as the foundation for building more complex data structures.
B. Non-Primitive Data Structures
These are more complex and are built using primitive data types. They are further classified into:
i. Linear Data Structures
Data is arranged in a sequential manner, and elements are connected one after another.
- Examples:
- Array: Fixed-size, elements stored at contiguous memory locations.
- List (e.g., Python list): Dynamic size, supports multiple data types.
- Stack: LIFO (Last In, First Out) structure.
- Queue: FIFO (First In, First Out) structure.
- Linked List: Nodes connected via pointers.
ii. Non-Linear Data Structures
Data elements are not stored sequentially. These are used for representing hierarchical or network relationships.
- Examples:
- Tree: Hierarchical structure with parent-child relationships (e.g., binary tree, BST).
- Graph: Nodes connected via edges; can represent complex relationships like social networks or maps.
iii. Hash-Based Data Structures
Use a hash function to map keys to values. Enable fast data access.
- Examples:
- Hash Table
- Dictionary in Python (key-value pair storage)
2. Classification Based on Usage
A. Static Data Structures
- Size is fixed at compile time.
- Easier to manage but not flexible.
- Example: Arrays in C.
B. Dynamic Data Structures
- Size can change during program execution.
- More flexible and efficient for unknown or changing data sizes.
- Examples: Lists, Linked Lists, Trees.
3. Classification by Access Type
A. Sequential Access
- Data is accessed in order.
- Examples: Arrays, Lists, Queues.
B. Random Access
- Data can be accessed directly using an index or key.
- Examples: Arrays, Hash Tables.
Conclusion
Data structures are classified into primitive and non-primitive, with non-primitive structures further divided into linear, non-linear, and hash-based types. Choosing the right type depends on how the data will be used—whether it needs to be accessed sequentially, stored hierarchically, or retrieved quickly using keys. A good understanding of these classifications helps in writing efficient, maintainable, and scalable code.
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