Data Structures in Python

Data structures are one of the most essential concepts in computer science and software management and are extensively used to write programs in any language. Python is a powerful, interactive, object-oriented, and interpreted programming language with which the fundamentals of data structure can be studied more effortlessly, unlike other programming languages.

It is one of the most versatile and efficient languages primarily used in making interactive and high-level websites and other fields like Machine Learning and Artificial Intelligence. It heavily relies on data that requires it to be efficiently stored and accessed on time. This is achieved with the help of data structures in Python. 

What is a Data Structure? 

The primary purpose of data structures is to allow the effective organization of data for storing data collections and executing operations on them. For instance, in a website, the backend data needs to be efficiently stored, managed, and organized to have easier access for future modifications.

Linear Data Structures

Linear data structures allow the storage of data elements in a proper sequence. There are several types of linear data structures. They are as follows:-

• Array− A sequential arrangement of the data elements paired with the index of the data element.
• Stack− A stack is a data structure that follows a particular order of operation. They are called FILO (First In Last Out) or LIFO (last in First Out).
• Linked List− Every data element consists of a link to another element and the data present in it.
• Matrix− A matrix is a two dimensional data structure where the data element is indicated by a pair of indices.

Queue− Queue is similar to Stacks, but the only order of operation is FIFO (First In First Out).

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Non-Linear Data Structures

Non-linear data structures do not follow a sequential linking of data elements. In this data structure, any group or pair of data elements can be linked to one another, and there is no strict sequence to access the data elements.

• Heap− A heap is a special type of tree data structure where data in the parent node is either strictly lesser than the child nodes or strictly greater than or equal to the child nodes.
• Binary Tree− A binary tree is a data structure that connects each data element to a maximum number of two other data elements. It starts with a root node.
• Graph− A graph is an arrangement of nodes and vertices where some nodes are connected to the other via links.
• Hash Table− A hash table is a data structure made of arrays associated with each other with the help of a hash function. It uses keys to retrieve values rather than the index from data elements.

In-Built Data Structures In Python

Data Structures in Python are specific to this programming language because they help in the flexible storage of variegated data types and help process data faster in a Python environment. As these data structures come pre-loaded in Python environments, the Python language is significantly easier to use for programmers as they obtain or can develop solutions faster.

Python has implicit support for Data Structures which aids in the storage and easy access of data and allows users to make their own Data Structures with total control. The inbuilt data structures in Python are as follows:-

List 

An ordered collection of items is defined as a list, which is an integral part of data structures in creating a project in Python. “Ordered collections” means every item belonging to a list consists of an order that is specifically used to uniquely identify them. This order of elements is a characteristic that inherently remains constant as long as the list lasts. Everything in Python is deemed an object. Hence, making a list essentially creates a Python object of a particular type. 

List_A = [item 1, item 2, item 3….., item n]

When a list is nested, it can have any kind of object or include a different list or a sublist that can also consist of different sublists. A nested list does not have a limit to the depth with which lists can be nested. Lists made in Python can be mutable because they are subject to change even after being created. A user can add, search, move, shift and delete elements from the list as per their discretion. When elements in a list require replacement, the number of added elements need not be equal to the number of elements. Python will adjust itself as per requirement.

Tuple 

A tuple is another data structure built-in Python and is the ordered collection of objects. Tuples come with bounded functionality, unlike lists. Mutability is the main characteristic used to differentiate between Tuples and Lists. Lists are mutable, and tuples are immutable. Tuples cannot be added, modified, or deleted once they have already been made. In lists, the usage of parentheses is required to enclose the elements divided by commas. On the other hand, tuples are optional to use parentheses in creating them but are widely recommended to discern between the beginning and end of the tuple. Here is a sample tuple:

tuple_A = (item 1, item 2, item 3,…, item n)

Tuples are created as one single entity and are primarily used when the programmer does not want to allow any modification data. They are extensively used to create an object intended to be kept intact all throughout its lifetime. Since tuples are immutable, they can potentially prevent accidental modification, addition, or removal of any data. One benefit of using tuples is that they use less memory, which helps execute programs faster than using lists. 

Sets

A unique collection of specific elements that are not subjected to follow any particular order is known as a set. When an object’s existence in a collection of objects is more significant than the order of the objects or the number of times of its appearance, sets are used. Sets are mutable, unlike tuples, and can be easily modified, replaced, added, or removed. Here is a sample set:-

set_a = {“item 1”, “item 2”, “item 3”,….., “item n”}

Sets are primarily used to check whether or not certain elements are within a set or not. For instance, sets are greatly optimized for membership tests. They can also be used to see if a set is a subset of a different set and recognize the relationship between two sets.

Dictionary 

Dictionary in an integral data structure used in Python. It is the non-linear collection of data values and is primarily used to store specific data values such as maps. Dictionary holds key:value pair, unlike different Data Types that have only one particular value as an element. The Key-value is given in the dictionary to optimize it. In a Python Dictionary, there are multiple ways to add elements. At a time, only one value can be added to a Dictionary by defining the value with the key. A built-in update() method must be used to update an existing value in a Dictionary. It is also possible to add nested key values to an already existing Dictionary. If the key-value already exists while adding a value, the value gets updated; otherwise, a new Key is added to the Dictionary with the value.

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