What is Data Structure? Type of Data Structures?

What is Data Structure?

In computer science, a data structure is a way of organizing and storing data in a computer so that it can be accessed and modified efficiently. Different types of data structures are suited to different kinds of applications and some are highly specialized to specific tasks. Some common examples of data structures include arrays, linked lists, stacks, queues, trees and graphs.

Data structures are a fundamental concept in computer science, and they are used in many different areas of computing including databases, operating systems and programming languages. Data structures are used to store and manipulate data in a way that is efficient and allows for quick access and modification.

There are many different types of data structures and each has its own characteristics and trade-offs. Some data structures are more efficient at certain operations such as searching or insertion, while others are better at other operations such as deleting or updating elements. Choosing the right data structure for a given task is an important part of designing efficient and effective algorithms and software.

Types of Data Structure:

Data structures are divided into two categories:

Linear data structure
Non-linear data structure

Linear Data Structure:

A linear data structure is a data structure that stores data elements in a linear or sequential fashion. This means that the data elements are arranged in a specific order and each data element is connected to the one before it and the one after it in the sequence.

Popular Linear Data Structure are:

Arrays: An array is a collection of items that are stored in a contiguous block of memory. Arrays are indexed, meaning that each element has a specific position in the array. Arrays are good for storing and accessing data quickly but they are not well-suited for inserting or deleting elements, as this requires shifting the positions of other elements.
Linked lists: A linked list is a collection of nodes where each node stores a value and a reference to the next node in the list. Linked lists are good for inserting and deleting elements but they are not as efficient as arrays for accessing elements.
Stacks: A stack is a data structure that operates on a last-in, first-out (LIFO) basis. Items are added to and removed from the top of the stack. Stacks are used in many applications such as undo/redo functionality and function call history.
Queues: A queue is a data structure that operates on a first-in, first-out (FIFO) basis. Items are added to the end of the queue and removed from the front. Queues are used in many applications such as task scheduling and message passing.

Linear data structures are useful for storing and manipulating data in a simple, ordered way and they are often used in computer algorithms and data structures.

Non-linear data structure:

A non-linear data structure is a data structure that does not store data elements in a linear or sequential fashion. This means that the data elements are not arranged in a specific order and they may not be connected to one another in a single sequence.

Popular Non-linear data structures are:

Trees: A tree is a non-linear data structure that consists of nodes arranged in a hierarchical structure. Each node in a tree has one or more child nodes and the top node is called the root node. Trees are useful for storing and organizing data in a hierarchical manner such as in file systems or decision trees.
Graphs: A graph is a non-linear data structure that consists of a set of nodes (also called vertices) and a set of edges that connect the nodes. Graphs are useful for representing relationships between data elements such as in social networks or transportation systems.
Hash tables: A hash table is a non-linear data structure that stores data elements using a hash function to map the data elements to specific indices in an array. Hash tables are useful for storing and retrieving data quickly and they are often used in database indexing and caching.

Non-linear data structures are useful for storing and manipulating data in more complex, interconnected ways and they are often used in computer algorithms and data structures.

Example of Data Structure:

Here is an example of an array, which is a type of data structure:

# Declare an array of integers
arr = [1, 2, 3, 4, 5]

# Access an element of the array
print(arr[2])  # Outputs 3

# Modify an element of the array
arr[4] = 10
print(arr)  # Outputs [1, 2, 3, 4, 10]

# Add an element to the end of the array
arr.append(20)
print(arr)  # Outputs [1, 2, 3, 4, 10, 20]

# Remove an element from the array
arr.pop(3)
print(arr)  # Outputs [1, 2, 3, 10, 20]

In this example, the ‘arr’ array is a collection of integers that are stored in a contiguous block of memory. The array is indexed, meaning that each element has a specific position in the array. The array can be accessed and modified using the square brackets ‘[]’ operator.

Arrays are a simple and efficient data structure for storing and accessing data, but they are not well-suited for inserting or deleting elements as this requires shifting the positions of other elements.

Operations of data structure:

Different types of data structures support different types of operations and the specific operations supported by a given data structure can depend on the implementation. Here are some common operations that are supported by many types of data structures:

Insert: Inserting an element into a data structure involves adding a new element to the structure. This may involve allocating memory for the new element and updating pointers or indices to maintain the integrity of the structure.
Delete: Deleting an element from a data structure involves removing the element from the structure and updating pointers or indices as necessary.
Search: Searching for an element in a data structure involves looking for an element that matches a certain criterion such as a specific value or key. Different data structures have different search algorithms and some are more efficient than others at finding specific elements.
Traverse: Traversing a data structure involves visiting each element in the structure and performing some action on it such as printing its value or adding it to a list.
Sort: Sorting the elements of a data structure involves rearranging the elements in a specific order such as ascending or descending numerical order. Different data structures have different sorting algorithms and some are more efficient than others at sorting large datasets.
Update: Updating an element in a data structure involves modifying the value of an existing element. This may involve changing a single value or replacing the element with a new one.

These are just a few examples of the many different operations that can be performed on data structures. The specific operations supported by a given data structure can depend on the implementation and the requirements of the task at hand.

Here is an example of inserting an element into a data structure in Python:

# Inserting an element into an array

array = [1, 2, 3]
element = 4
index = 3

# Insert the element at the specified index
array.insert(index, element)

print(array)  # [1, 2, 3, 4]

# Inserting an element into a linked list

class Node:
    def __init__(self, data, next=None):
        self.data = data
        self.next = next

class LinkedList:
    def __init__(self):
        self.head = None

    def insert(self, element):
        new_node = Node(element)
        new_node.next = self.head
        self.head = new_node

linked_list = LinkedList()
linked_list.insert(1)
linked_list.insert(2)
linked_list.insert(3)

# Insert the element at the beginning of the list
linked_list.insert(4)

node = linked_list.head
while node is not None:
    print(node.data)
    node = node.next

# Output: 4, 3, 2, 1

# Inserting an element into a tree

class TreeNode:
    def __init__(self, data, left=None, right=None):
        self.data = data
        self.left = left
        self.right = right

class BinarySearchTree:
    def __init__(self):
        self.root = None

    def insert(self, element):
        new_node = TreeNode(element)
        if self.root is None:
            self.root = new_node
        else:
            current_node = self.root
            while True:
                if element < current_node.data:
                    if current_node.left is None:
                        current_node.left = new_node
                        break
                    current_node = current_node.left
                else:
                    if current_node.right is None:
                        current_node.right = new_node
                        break
                    current_node = current_node.right

bst = BinarySearchTree()
bst.insert(5)
bst.insert(3)
bst.insert(7)

# Insert the element at the appropriate position in the tree
bst.insert(6)

# Inserting an element into a hash table

class HashTable:
    def __init__(self, size):
        self.size = size
        self.table = [None] * size

    def hash_function(self, key):
        return key % self.size

    def insert(self, key, value):
        index = self.hash_function(key)
        if self.table[index] is None:
            self.table[index] = (key, value)
        else:
            # Handle collision using chaining
            current_node = self.table[index]
            while current_node[1] is not None:
                current_node = current_node[1]
            current_node[1] = (key, value)

hash_table

Applications of Data Structure:

Databases: Data structures are used to store and retrieve data from databases. Different types of data structures are used to store different types of data, such as lists of customers, products or orders.
Operating systems: Data structures are used in operating systems to manage memory, process scheduling and file systems. For example, a tree data structure might be used to store the directory structure of a file system, while a queue might be used to manage tasks that need to be executed by the system.
Programming languages: Data structures are used in programming languages to store and manipulate data. For example, arrays are commonly used to store lists of data such as the elements of a form on a web page and linked lists are used to store sequences of data that need to be updated or searched frequently.
Data analysis: Data structures are used in data analysis to store and manipulate large sets of data. For example, a graph data structure might be used to represent relationships between data, such as the connections between people in a social network.
Graphics and video games: Data structures are used in graphics and video games to store and manipulate data related to the game world, such as characters, objects and environments.

Data structures are an essential part of many different areas of computing and they are used to store and manipulate data in a way that is efficient and allows for quick access and modification.

What advantages of Data structures?

Data structures are an important concept in computer science and are used to store, organize and manipulate data efficiently. There are several advantages to using data structures:

Improved performance: Data structures are designed to store and manipulate data in specific ways that can improve the performance of algorithms and applications. For example, using a hash table can improve the speed of searching for data and using a tree data structure can improve the speed of sorting data.
Greater flexibility: Data structures provide different ways to store and manipulate data, so you can choose the one that best fits your needs. This allows you to design your algorithms and applications to be more flexible and adaptable.
Ease of use: Data structures provide a standardized way to store and manipulate data which can make it easier for programmers to work with large amounts of data.
Reusability: Data structures can be used in multiple algorithms and applications which means that you can reuse them instead of having to write new code every time you need to store or manipulate data.
Improved data organization: Data structures can help you organize data in a logical and structured way which can make it easier to understand and work with large amounts of data.

Here are some common questions about data structures:

What are data structures?

Data structures are ways of organizing and storing data in a computer so that it can be accessed and modified efficiently. Different types of data structures are suited to different kinds of applications and some are highly specialized to specific tasks.
Why are data structures important?

Data structures are a fundamental concept in computer science and they are used in many different areas of computing, including databases, operating systems and programming languages. Data structures are used to store and manipulate data in a way that is efficient and allows for quick access and modification.
What are some common types of data structures?

Some common types of data structures include arrays, linked lists, stacks, queues, trees and graphs.
How do I choose the right data structure for a given task?

Choosing the right data structure for a given task is an important part of designing efficient and effective algorithms and software. Different types of data structures are more efficient at certain operations such as searching or insertion, while others are better at other operations such as deleting or updating elements. It is important to consider the specific requirements of the task when selecting a data structure.
How do I implement a data structure in a programming language?

To implement a data structure in a programming language, you will need to define a data type that represents the structure and implement the operations that are supported by the data structure. This will typically involve writing functions or methods that perform the desired operations on the data.

Here are some resources for learning Data Structures:

Also Read:

Complete guide to C programming language
Complete guide to C++ programming language
Complete guide to Python programming langauge

What is Data Structure? Type of Data Structures?