Well, What is Data Structure, Anyway?
Before defining data structures, let’s back up a little and ask, “What is data?” Here’s a quick answer: Data is information optimized for processing and movement, facts and figures stored on computers.
Data structures are a specific way of organizing data in a specialized format on a computer so that the information can be organized, processed, stored, and retrieved quickly and effectively. They are a means of handling information, rendering the data for easy use.
Every application, piece of software, or programs foundation consists of two components: algorithms and data. Data is information, and algorithms are rules and instructions that turn the data into something useful to programming.
Put another way, remember these two simple equations:
Related data + Permissible operations on the data = Data Structures
Data structures + Algorithms = Programs Related learning: What Is An Algorithm? Characteristics, Types and How to write it
Related learning: BreadthFirst Search Algorithm
Characteristics of Data Structures
Data Structure is the systematic way used to organize the data. The characteristics of Data Structures are:
Linear or NonLinear
This characteristic arranges the data in sequential order, such as arrays, graphs etc.
Static and Dynamic
Static data structures have fixed formats and sizes along with memory locations. The static characteristic shows the compilation of the data.
Time Complexity
The time factor should be very punctual. The running time or the execution time of a program should be limited. The running time should be as less as possible. The less the running time, the more accurate the device is.
Correctness
Each data must definitely have an interface. Interface depicts the set of data structures. Data Structure should be implemented accurately in the interface.
Space Complexity
The Space in the device should be managed carefully. The memory usage should be used properly. The space should be less occupied, which indicates the proper function of the device.
Linear Data Structures
Data elements in a linear data structure are linked to one another in a sequential arrangement, with each element linked to the elements in front of and behind it. In this manner, a single run can traverse the structure. Linear data structures consist of four types. They are:

Stack

Array

Queue

Linked list
Stack
The linear data structure stores the data elements in the ‘firstin/ lastout’ or the ‘lastin/ first out’ order. These orders are known as FILO and LIFO orders, respectively. By using Stack, the element can be added and removed simultaneously from the same end. In Python, Stack can be developed in the following ways.

Queue.LifoQueue

List

Collections.deque
In Stack, the terms ‘Push’ and ‘Pop’ are used instead of ‘insert’ and ‘delete’.
Array
It is the collection of similar data types that are stored in the Contiguous Memory Locations. Arrays are used in Python as well. Arrays work on the scale of 0 to (n1), where ‘n’ denotes the size of the array. Arrays are of two types. They are:

Onedimensional Array

Multidimensional Array
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Queue
The queue is a linear data structure that follows the FIFO order. FIFO stands for First In and First Out. The order is that the elements which are inserted first are to be removed first. The properties of Queue data structure are:

Inserting an element

Deleting the element

Time of access.
Linked List
Linked Lists separate the data structures that are stored consecutively. The last node of a data structure will be linked to the first node of the next data structure. The first element of any data structure is known as the Head of the List. The linked list helps in memory allocation, stores data in internal structure etc. There are three types of Linked Lists. They are:

Single Linked List

Double Linked List

Circular Linked List
NonLinear Data Structures
The data structure in which the data elements are randomly arranged. The elements are nonarranged sequentially. The data elements are present at different levels. In Nonlinear data structures, there are different paths for an element to reach the other element. The data elements in the nonlinear data structures are connected to one or more elements. There are two types of nonlinear data structures. They are:

Tree Data Structure

Graph Data Structure
Tree Data Structure
Tree data structures are completely different from the arrays, stacks, queues and linked lists. Tree data structures are hierarchic. The tree data structure collects the nodes together to depict and stimulate the sequence. Tree data structure does not store the data sequentially. It stores the data on multiple levels. The top node of the Tree Data Structure is known as the Root Node. Any type of data can be stored in the root node. Each node shall definitely contain the data. The branches in the Tree Data Structure are known as the children.
The different parts of the Tree Data Structure are:

Root Node

Child Node

Edge

Siblings

Leaf Node

Internal Nodes

Height of the tree

Degree of the Node
Graph Data Structure
In Graph Data Structure, one node is simply connected to the other node through the edge of the graph. The Graph Data Structure obviously uses Nonlinear data structures which are not sequentially arranged. The graph data structures consist of edges and nodes represented by E and V, respectively. Graph Data Structures do not have root nodes. It does not have a standard order of arranging the data. Every tree is also known as the graph with n1 edges where ‘n’ represents the total number of vertices in the graph. There are various categories in the graphs such as undirected, unweighted, directed and weighted.
The different parts of the graph are as follows.

Vertex

Edges

Directed Edge

Undirected Edge

Weighted Edge

Degree

Indegree

Outdegree
Data Types
Data types are the basic elements in the classification of the data. Data types are used in the transmission of information between the programmer and the compiler. There are different data types. They are as follows.
Boolean:
In computer programs, there are three types of data such as numbers, text and booleans. Boolean is a data type, and the value in the boolean can be either false or true or positive or negative. A boolean type of data proves whether the data is valid or invalid. Boolean values have two possible states such as True or False. In the binary system, the values 0 and 1 are used. Boolean expressions deal with algebra, logical values and binary variables.
Integer:
The integer data type stores both positive numbers and negative numbers along with zero. The integer data type uses all these integers to maintain precision. All the arithmetic operations can be efficiently done through integer data types. If the value of data is beyond the numerical range of the integer then it is the case that the database server cannot store the value. However, integer data type consumes four(4) bytes of storage per value.
FloatingPoint Numbers
A floatingpoint data type approximates real values using a formula in order to allow a tradeoff between range and precision. Due to the need for quick processing speeds, systems with extremely small and very large real numbers are frequently found to use floatingpoint calculation. An exponent in a fixed base is typically used to scale a number and approximate its representation to a specific number of significant digits.
FixedPoint Numbers
Binary words are used to store numbers in digital electronics. A fixedlength string of bits (1s and 0s) is a binary word. The data type determines how these 1s and 0s are interpreted by hardware elements and software processes. There are two different data types for binary numbers: fixedpoint and floatingpoint. Both signed and unsigned fixedpoint data formats are available. There is no sign bit. Therefore the binary word does not typically explicitly express whether a fixedpoint value is signed or unsigned. In contrast, the architecture of the computer implicitly defines the sign information.
Binary words are used to store numbers in digital electronics. A fixedlength string of bits (1s and 0s) is referred to as a binary word. The data type determines how these 1s and 0s are interpreted by hardware elements and software processes. There are two different data types for binary numbers: fixedpoint and floatingpoint.
Character
Character information is stored in a fixedlength field by the CHAR data type. Data can be a string of letters, integers, and other characters that are supported by the code set of your database locale, whether they are singlebyte or multibyte characters.
Strings with a fixed length or a variable length can be used to hold character data. Variablelength strings are not extended; fixedlength strings are rightextended with spaces on output.
Pointers
Blocks of memory that are dynamically allocated are managed and stored using pointers. Data objects or arrays of objects are stored in such alliances. The heap or free store, which is a memory space provided by the majority of structured and objectoriented languages, is where objects are dynamically allocated.
String
A string data type consists of a series of characters, either in the form of a literal constant or a variable. The latter can either be constant in length or allow its elements to alter (after creation). An array data structure of bytes (or words) is frequently used to create a string, which is typically thought of as a sort of data and contains a succession of items, typically characters, using some kind of character encoding.
How Are Data Structures Used?
Implementing the physical representations of abstract data types uses data structures. When creating effective software, data structures are a key component. They are also essential to the design of algorithms and the use of those algorithms in software. The data structures are used in different aspects, such as,
Storing Data
When providing the set of attributes and matching structures that will be used to store records in a database management system, data structures are utilised to efficiently persist data.
Managing Resources and Services
Data structures, including linked lists for memory allocation, file directory management and file structure trees, as well as process scheduling queues, are used to allow core operating system (OS) resources and functions.
Data Exchange
Information shared across applications, such as TCP/IP packets, is organised using data structures.
Ordering and Sorting
Binary search trees, sometimes referred to as ordered or sorted binary trees, are data structures that offer practical ways to sort things, such as character strings used as tags. Programmers can control objects arranged in a given priority using data structures like priority queues.
Indexing
To index items, such as those kept in a database, even more complex data structures like Btrees are utilised.
Searching
Btrees, hash tables, and binary search trees are standard techniques used to generate indexes that speed up the process of finding a particular item.
Scalability
Data structures are used by big data applications to allocate and manage data storage across distributed storage sites, assuring performance and scalability. To make querying easier, several big data programming environments, like Apache Spark, offer data structures that replicate the fundamental structure of database entries.
Choosing a Data Structure
The steps to be used in choosing the data structure are given below.
1) The first step in determining the basic operations that must be supported is to analyse the problem. Inserting a data item into the data structure, deleting a data item from the data structure, and finding a specific data item are examples of basic operations.
2)Identify the resource constraints for each operation and quantify them.
3) Determine which data structure best meets these requirements.
Supported Operations:
If the underlying data type of an attribute can be translated into one of the kinds for which an operation is supported, processes between the data types not included in the table can be carried out. Data can have numbers added or subtracted from them. The number of days to be added or removed is represented by integers.
For instance, since IEG INT8 is turned into IEG DOUBLE and the addition of IEG DOUBLE and IEG MONEY is supported, the addition of IEG INT8 and IEG MONEY is possible.
Computational Complexity:
Computational complexity is a metric for how much time and memory (resources) a specific algorithm uses when it is executed. Before developing the code, computer scientists can forecast an algorithm’s execution time and memory needs using mathematical metrics of complexity. For programmers implementing and choosing algorithms for practical applications, these predictions are essential guides.
Programming Elegance:
One of those things that are simple to recognise but difficult to define is an elegant programme. It uses words effectively without resorting to obfuscation. It is succinct without employing complicated code. It strikes a balance between the concepts of simplicity and explicitness, coding complexity while staying superficial to read and understand. Writing the coding equivalent of flawless writing is every programmer’s ultimate ambition.
There is no “magic solution” or one solution to this issue. Coding standards can be used to aid, but they must be founded on a solid framework that ensures the core of the matter is conveyed to the programmer and reflected in the code.
Data Types and Their Relationship With Data Structures
To answer the question of what is data structure, there are three basic data types to understand.
Abstract.
Abstract data is defined by how it behaves. This type encompasses graphs, queues, stacks, and sets.
Composite (or Compound).
Composite data comprises combined primitive data types and includes arrays, classes, records, strings, and structs. They may also consist of other composite types.
Primitive.
Primitive data is classified as basic data and consists of Boolean, characters, integers, pointers, and fixed and floatingpoint numbers.
These data types are the building blocks of data structures. Data types tell the interpreter or the computer how the programmer plans on using the data. Furthermore, data analysts can choose from different data structure classifications. The trick is to select the structure best suited for your needs and situation.