Common Operations of Data Structures

Overview

People have been referring to computer information that is communicated or stored as "data" since the development of computers. On the other hand, order types also contain data. Data might take the shape of printed numbers or sentences and bytes saved in the memory of electrical devices or knowledge that has been mentally preserved. This data became an important part of everyone's daily lives as the world became more modern, and different implementations allowed people to store it in various ways by using Data structure operations.

Data Structure

Computer science has a subfield called as data structures. Understanding data organization and flow management by studying data structures enables us to optimize any process or program's efficiency. Data Structure is a specific method of storing and organizing data in the computer's memory to retrieve and effectively use it later quickly. A data structure is a logical or mathematical representation of a certain type of data organization. Data can be managed in a variety of ways.

A specific data model's scope is determined by two things:

The data must first be sufficiently put into the structure to indicate a clear association between the data and a physical item.
Second, the structure should be so simple that anyone may learn how to analyze the data effectively as needed.

Data structures include things like arrays, linked lists, stacks, queues, trees, etc. Nearly every area of computer science, including compiler design, operating systems, graphics, artificial intelligence, and many others, makes extensive use of data structures.

The essential component of many algorithms in computer science is a data structure, which enables programmers to manage data efficiently. As the primary goal of the software is to save and retrieve user data as quickly as possible, it is essential to enhance the performance of an application or piece of software.

Any software or code is constructed from data structures. It can be very difficult for a programmer to choose the right data structure for a project.

When discussing data structures, the following basic terms are frequently used:

Data: A collection of values or an elementary value are both acceptable definitions of data. Examples of employee-related data include the name and ID of the employee.
Data Items: Data Items are single units of value.
Group Items: Group Items are data items that have subordinate data items. An employee's name, for instance, could include their first, middle, and last names.
Elementary Items: Data items classified as elementary cannot be subdivided into other items. Using an employee's ID as a demonstration.
Entity and Attribute: An entity represents a class of specific objects. It is made up of several Attributes. Each attribute represents a particular attribute of that entity.

Different Operations on Data Structure

A variety of Data structure operations that can be used to manipulate the data. The following operations are described and shown:

Traversing

A data structure's elements can be visited by traversing through it. It makes systematic visits to the data. Any DS can be used to accomplish this. The program used to demonstrate traversal in an array, stack, queue, and linked list is shown below:

Array:

Output:

Stack:

Output:

Queue:

Output:

Linked List:

Output:

Time Complexity: $O(N)$
Auxiliary Space: $O(1)$

The program used to demonstrate array traversal is shown below in different programming languages:

C++:

Output:

Java:

Output:

Python:

Output:

C#:

Output:

Time Complexity: $O(N)$
Auxiliary Space: $O(1)$

Below is the program to illustrate traversal in a Stack:

C++:

Output:

Java:

Output:

Python:

Output:

C#:

Output:

Time Complexity: $O(N)$
Auxiliary Space: $O(1)$

Searching

Searching is locating a specific piece within a given data structure. When the necessary component is located, the attempt becomes successful. We may execute searches on various data structures, including arrays, linked lists, trees, graphs, etc. The program to find an element in an array, stack, queue, and linked list is shown below.

Array:

Output:

Stack:

Output:

Queue:

Output:

Linked List:

Output:

Time Complexity: $O(N)$
Auxiliary Space: $O(1)$

Deletion

It is a method we use on all data structures. A data structure's element can be deleted using deletion. When the necessary element is removed from the data structure, the deletion operation is successful. A deletion in a data structure, such as an array, linked list, graph, tree, etc., has the same name. This procedure is known as Pop in a stack. Dequeue is the name of this operation in Queue.

The program to demonstrate dequeue in a stack, queue and LinkedList is provided below.

Stack:

Output:

Queue:

Output:

LinkedList:

Output:

Time Complexity: $O(N)$
Auxiliary Space: $O(1)$

Insertion

It is a method we use on all data structures. Insertion refers to the act of adding a new element to a data structure. When the required element is added to the required data structure, the insertion operation is successful. In other instances, it fails because there is no more room to add any more elements to the data structure because it is already full. The insertion is known by the same term as an insertion into an array, linked list, graph, or data tree. This action is referred to as Push in a stack. This action is referred to as Enqueue in the queue.

The program to demonstrate insertion in an array, stack, queue, and linked list is provided below.

Array:

Output:

Stack:

Output:

Queue:

Output:

Linked List:

Output:

Time Complexity: $O(N)$
Auxiliary Space: $O(1)$

Create

Declaring them sets aside memory for program elements.

Data structures can be created during the following:

Compile-time
Run-time

The malloc() function is available.

Selection

It chooses particular data from the current data. By including a condition in the loop, you can choose any particular piece of data.

Update

The data in the data structure is updated. By including a condition in the loop, similar to the select approach, you may also update any particular data.

Sort

Sorting is the action of ordering the data items in a data structure by particular key values in a predetermined order (ascending or descending). Sorting the student records in an array is possible using the students' names, registration numbers, or test scores. To meet the user's processing requirements, each sorting will employ a distinct set of criteria.

Sorting data in a particular order (ascending or descending) order. To sort data quickly, we can use a variety of sorting methods. A bubble sort, for instance, sorts data in $O(n^2)$ time. There are many kinds of algorithms, including quick sort, insertion sort, merge sort, and selection sort.

Below is the table of different sorting techniques along with their time complexity:

Algorithm	Best Time Complexity	Average Time Complexity	Worst Time Complexity	Space Complexity
Selection Sort	$Ω(n^2)$	$θ(n^2)$	$O(n^2)$	$O(1)$
Bubble Sort	$Ω(n)$	$θ(n^2)$	$O(n^2)$	$O(1)$
Insertion Sort	$Ω(n)$	$θ(n^2)$	$O(n^2)$	$O(1)$
Heap Sort	$Ω(n log(n))$	$θ(n log(n))$	$O(n log(n))$	$O(1)$
Quick Sort	$Ω(n log(n))$	$θ(n log(n))$	$O(n^2)$	$O(n)$
Merge Sort	$Ω(n log(n))$	$θ(n log(n))$	$O(n log(n))$	$O(n)$
Bucket Sort	$Ω(n +k)$	$θ(n +k)$	$O(n^2)$	$O(n)$
Radix Sort	$Ω(nk)$	$θ(nk)$	$O(nk)$	$O(n + k)$
Count Sort	$Ω(n +k)$	$θ(n +k)$	$O(n +k)$	$O(k)$
Shell Sort	$Ω(n log(n))$	$θ(n log(n))$	$O(n^2)$	$O(1)$
Tim Sort	$Ω(n)$	$θ(n log(n))$	$O(n log (n))$	$O(n)$
Tree Sort	$Ω(n log(n))$	$θ(n log(n))$	$O(n^2)$	$O(n)$
Cube Sort	$Ω(n)$	$θ(n log(n))$	$O(n log(n))$	$O(n)$

Merge

It is possible to merge data from two different orders in an ascending or descending order. To combine sorts of data, we utilize merge sort.

Merging can be considered simply appending a group of elements from one data structure after elements from another data structure with the same element structure. For instance, two arrays with the same fields and student information from two distinct classes can be combined to make one list. Sorting may or may not be present in the final data structure.

Split Data

Dividing data into many sub-components to speed up the process.

FAQs

Q. What are the three fundamental Data structure operations?

A. Examples of basic operations include adding a data item to the data structure, removing a data item from the data structure, and accessing a specific data item.

Q. What is a fundamental Data structure operations?

A. Print each individual array element in a traversal operation. Insertion: Inserts a new element at the specified index. Deletes the element at the specified index. Use the provided index or value to search for an element. updates the element at the specified index.

Q. What are the six data structure operations?

A. Traversal, Insertion, Deletion, Searching, Sorting, and Merging are some operations that can be performed on a linear data structure. Stack and queue are two examples of linear data structures.

Conclusion

Algorithms are constructed from data structures. Data structure operations are the foundation for understanding algorithms.
Data is arranged in data structures in memory. They specify how different bits of data relate to one another and make it easy to access and change that data.
We suggest reading a series of articles on Scaler if you want to learn more about Data structure operations in-depth.
Finally, we advise choosing a language like Java or C++ when you are prepared to begin building Data structure operations. You can implement data structures in these languages however you like.
We hope that this post has given you a better understanding of data structures and how to use them to solve issues. Please get in touch if you have any queries on how to begin using data structures.

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