What is Data Structure in Python? – Python is a high-level, general-purpose programming language that finds multiple applications. Python is easy to learn and can be used for various applications and programs. Python is easy to learn and can be used for various applications and programs. Its syntax is similar to that of other programming languages such as Java and C and it is very easy to learn and use.
This blog throws light on Data Structure in Python: Types, Classifications, and Applications provide a few examples of each type to help you understand the various data structures in Python and their classification. Let’s start with a brief overview of data structures in Python.
What is Data Structure in Python?
A data structure is a way of organizing information in a computer so that it can be used quickly and efficiently to perform various operations. It is essentially a computer representation of information. The data that is stored in any computer system can be represented in different ways depending on the nature of its operations. For example, computers store data in various forms of tables called data structures. Each python data type has its own advantages and disadvantages and some may be more appropriate for certain operations than others. Data structures also play an important role in developing computer programs because different types of programs are designed to operate on different kinds of data structures.
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Classification of Data Structures in Python:
Data Structure plays a pivotal role in organizing the data. The two primary classifications of data structures are:
- Linear data structure
- Non-Linear data structure
The main difference between them is that linear data structures are organized in a single column while non-linear data structures are arranged in multiple columns. Each of these categories is further classified into sub-categories based on the type of data that they contain. Some common examples of linear data structures include lists, arrays, and strings. Examples of non-linear structures in python examples include trees and graphs. These data structures in python classification can be explained as follows:
Linear Data Structure:
A linear data structure is a collection in which each element is associated with a specific index and the elements are ordered according to their indices. The elements of a linear data structure are arranged in a single row and have only a single column as each row is associated with only one value. Examples of linear data structures in python classification include lists, arrays, and strings. Lists and arrays are used to store multiple values in sequential order. Strings are used for storing text data such as words and sentences.
* Linked Lists – A linked list is a collection of data elements linked together in a circular manner. It is one of the most common types of data structures used in computer programming and is made up of one or more nodes. Each node contains an item of data along with a pointer that points to the previous node in the list.
Application of Linked Lists:
1) They can be used to implement a FIFO queue in which data is inserted in the order in which it is received.
2) They can be used to implement a LIFO queue in which the oldest item is removed from the front and added to the back, thereby making room for new items to be inserted at the front.
3) They can also be used as stacks in which the items on the left-hand side are removed and the items on the right-hand side are added.
4) They can also be used as queues in which new data elements are inserted at the top and older data elements are removed from the bottom.
* Stacks – A stack is a list of data elements arranged in the form of a vertical structure. It is used for implementing dynamic memory allocation in a programming language and is commonly known as a last-in-first-out data structure and algorithms in python. It is a sequential list where the top element can be accessed through a single index value and each subsequent element is accessed by incrementing the index value by one.
Application of Stacks:
1) When items are added or removed from the stack it maintains the invariant that the last item added to the stack is the first item to be removed from it.
2) It is used for the efficient implementation of priority queues and dictionaries.
3) It can be used for implementing a stack for storing the digits of a number when scanning them in a scanner.
4) It is used to implement the built-in exception handler in Java.
5) It can be used to implement queues such as circular queues or priority queues.
6) It can be used to implement a heap that uses recursion as a method for finding a node in the tree with maximum depth.
* 2D Arrays – In a two-dimensional array, data is arranged in a grid-like structure with rows and columns. Each row is identified by its column number and each entry is represented by a variable known as a subscript. Information is stored within a two-dimensional array using a zero-based indexing system.
Application of Arrays:
1) An array can be used to keep track of items found in a tree. For example, the items in a binary search tree can be represented by a one-dimensional array.
2) An array can also be used to store pointers to items in a particular order, such that each element’s pointer points to an element that occurs after it in the sequence.
3) It can hold values of different types.4) It can be indexed
*Queue: A queue is another form of linear data structures and algorithms in python that consists of a series of records stored in a circular arrangement and can be sorted according to the records of the first, second, and last positions. The data items in the queue are accessed using an index known as the queue position number. s=Structure in python Examples of queues include the waiting list of a hospital, the line of customers at a bank, the stack of documents in an office, etc.
Application of Queue:
1) It can be used to implement a FIFO queue in which data is stored in the order in which it is received.
2) It can also be used to implement a LIFO queue in which the oldest item is removed from the front and added to the back, thereby making room for new items to be inserted at the front.
3) It can also be used to implement a stack in which items are added and removed using the L operator.
4) It can also be used to implement a priority list in which each item is examined to determine its priority level, after which an item with the highest priority is inserted in the first position and an item with the lowest priority is inserted in the last position.
5) It can also be used to implement a skip list in which an item that is either not found or found multiple times is removed each time it is encountered.
6) It can also be used to implement a round-robin queue in which each item is evaluated according to a given algorithm, after which the item is reinserted in the same position as before the evaluation.
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Non-Linear Data Structure:
A non-linear data structure consists of a set of elements that are connected to each other in various configurations and forms. Non-linear data structures enable developers to organize data in an effective and efficient manner. Structure in python examples of non-linear data structures include trees, graphs, and linked lists.
* Trees – A tree is a hierarchical data structure that consists of a set of branches known as nodes that are connected one to another by links known as edges. In a tree data structure, each node is connected to only one other node (i.e. a root node) and has no parent node. A tree may have any number of levels depending on its complexity.
Application of Trees:
1) They can be used to implement a binary search tree in which two nodes are linked to form a parent-child relationship. This structure is used to locate a specific element in a list of items without having to traverse the entire list one by one.
* Graphs: Graphs are also known as networks that are visual representations of the relationships between objects. The relationships are represented by lines connecting the objects and are called the edges. A graph can be represented using a number of different data structures such as trees, linked lists, and arrays.
Application of Graphs:
1) They can be used to represent social networks in which individuals are represented as nodes and relationships are represented by the lines connecting them (i.e. the edges). Examples of social networks include Facebook, Twitter, and LinkedIn.
2) They can be used to represent geographical networks in which individual nodes represent geographical locations and the edges of the graph represent the connections between them. An example of a geographical network is a map of a city or region.
3) They can be used to represent internet protocols that enable different computers to exchange information over the internet. Examples include HTTP, FTP, and SMTP.
*Tuple: A tuple is a data structure that contains multiple elements of the same type. It is similar to an array but uses commas to separate the names of the elements in it instead of brackets. Tuples are usually used for storing related data items together in a single variable and can be used to perform operations on those items as a group rather than individually.
Application of Tuple:
1) They can be used to represent sets of variables in the same order as they are defined in them. For example, you can use tuples to store the names of people in an address book together with their phone numbers in a comma-separated form.
2) They can be used to group the data from two or more sources into a single variable so that they can be processed by the same operation without requiring you to loop through the different sources multiple times. For example, you can use a tuple to store information on all the books you have borrowed from the library and then process that information using an algorithm to find all the books you have read.
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Wrapping Up!!
To conclude, we can state that data structures in Python simplify data processing and analysis by providing a more convenient way to access and manipulate the data. They also have different applications outside of computer science and practical applications in everyday life as well.
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