10 Types Of Arrays In Data Structure

Types Of Arrays In Data Structure. In computer science and data structures, an “array” is a fundamental data structure used to store a collection of elements of the same data type. There are different types of arrays based on various characteristics and use cases. Here are 10 types of arrays:

Types Of Arrays In Data Structure

1. Static Array: This is the most basic type of array, with a fixed size determined at the time of declaration. The size cannot be changed during runtime.
2. Dynamic Array: Also known as a resizable array, it can grow or shrink in size dynamically as elements are added or removed. Common implementations include Python’s list and Java’s ArrayList.
3. Sparse Array: An array in which most of the elements have the same default value, and only the non-default (non-sparse) values are stored along with their indices.
4. Multidimensional Array: An array that holds other arrays as its elements, forming a matrix or higher-dimensional structure. Common examples include 2D arrays (matrices) and 3D arrays.
5. Jagged Array: A multidimensional array in which each row can have a different length, allowing for irregular or ragged structures.
6. Zero-based Index Array: The most common type of array, where indexing starts from zero. Many programming languages, like C, C++, and Python, use zero-based indexing.
7. One-based Index Array: In some programming languages or mathematical contexts, arrays might use one-based indexing, where the first element is at index 1.
8. Circular Array: An array where the last element is connected to the first element, forming a circular structure. Useful for implementing circular buffers or queues.
9. Character Array (String): A specialized array used to store sequences of characters, representing text or strings in programming languages.
10. Boolean Array: An array that stores only boolean values (true or false), often used for representing binary data or flags.

These are just some of the types of arrays found in data structures. Each type has its own characteristics, advantages, and use cases, and understanding these variations can be crucial for designing efficient algorithms and data structures in computer programming.