Reviewed by:
Summary: Discover Python data structures, including linear types like lists and non-linear types like trees. Understand their classifications and applications to organise and manipulate data efficiently.
Introduction
Python is a high-level, general-purpose programming language with multiple applications. It 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 illuminates Data Structures in Python: Types, Classifications, and Applications. It provides 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.
Explore More:
Importance of Python Programming: Real-Time Applications.
Pattern Programming in Python: A Beginner’s Guide.
What is Data Structure in Python?
A data structure is a way of organising information in a computer to be used quickly and efficiently to perform various operations. It is a computer representation of information. The data stored in any computer system can be represented differently depending on the nature of its operations.
For example, computers store data in various tables called data structures. Each Python data type has advantages and disadvantages; some may be more appropriate for certain operations than others.
Data structures also play an essential role in developing computer programs because different programs are designed to operate on various data structures.
Read More: Types of Programming Paradigms in Python You Should Know.
Classification of Data Structures in Python
Data Structure plays a pivotal role in organising the data. The two primary classifications of data structures are:
- Linear data structure
- Non-Linear data structure
The main difference is that linear data structures are organised in a single column, while non-linear data structures are arranged in multiple columns. Each category is further classified into sub-categories based on the type of data they contain.
Some common examples of linear data structures include lists, arrays, and strings. Examples of non-linear structures in Python 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 ordered according to its indices. The components 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.
Lists, arrays, and strings are examples of linear data structures in Python classification. Lists and arrays store multiple values in sequential order, while strings store text data such as words and sentences.
Linked Lists
A linked list is a collection of data elements linked together circularly. It is one of the most common data structures used in computer programming and comprises one or more nodes. Each node contains an item of data and a pointer pointing to the previous node in the list.
Application of Linked Lists:
- You can use them to implement a FIFO queue where you insert data in the order you receive it.
- You can use them to implement a LIFO queue where you remove the oldest item from the front and add it to the back, making room for new items to insert at the front.
- You can also use them as stacks where you remove items from the left side and add items to the right side.
- You can also use them as queues where you insert new data elements at the top and remove older data elements from the bottom.
Stacks
A stack is a list of data elements arranged in a vertical structure. It implements dynamic memory allocation in programming and is known as a last-in-first-out data structure in Python. It is a sequential list where the top element is accessed through a single index, with each subsequent element accessed by incrementing the index by one.
Application of Stacks:
- The stack maintains the invariant that the last item added is the first item removed when items are added or removed.
- Developers use it to implement priority queues and dictionaries efficiently.
- Programmers can use it for implementing a stack to store the digits of a number when scanning them in a scanner.
- Java uses it to implement the built-in exception handler.
- You can use it to implement queues such as circular queues or priority queues.
- You can use it to implement a heap that uses recursion to find a node in the tree with maximum depth.
2D Arrays
A two-dimensional array arranges data in a grid-like structure with rows and columns. The column number identifies each row, and a variable known as a subscript represents each entry. The system stores information within a two-dimensional array using zero-based indexing.
Application of Arrays:
- An array can be used to track items found in a tree. For example, a one-dimensional array can represent the items in a binary search tree.
- An array can also store pointers to items in a particular order, such that each element’s pointer points to a component that occurs after it in the sequence.
- 3It 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. It stores a series of records in a circular arrangement and can sort them according to the records in the first, second, and last positions.
An index known as the queue position number accesses the data items in the queue. Examples of queues in Python include a hospital’s waiting list, a bank’s line of customers, and an office’s stack of documents.
Application of Queue:
- You can use it to implement a FIFO queue, where you store data in the order you receive it.
- You can also implement a LIFO queue, where you remove the oldest item from the front and add it to the back, allowing you to insert new items at the front.
- You can implement a stack, adding and removing items using the L operator.
- You can use it to implement a priority list, examining each item to determine its priority level. Then, insert the item with the highest priority in the first position and the item with the lowest priority in the last position.
- Implement a skip list by removing each item that you either don’t find or find multiple times every time you encounter it.
- You can implement a round-robin queue, evaluating each item according to a given algorithm and then reinserting the item in the same position as before the evaluation.
Read Blog: Python Web Scraping Library
Non-Linear Data Structure
A non-linear data structure consists of a set of elements connected to each other in various configurations and forms. These structures enable developers to organise data effectively and efficiently. In Python, examples of non-linear data structures include trees, graphs, and linked lists.
Trees
A tree is a hierarchical data structure consisting of a set of branches known as nodes connected to one another by links known as edges.
In a tree data structure, each node connects to only one other node (i.e., a root node) and has no parent node. Depending on its complexity, a tree may have any number of levels.
Application of Trees:
- You can use these to implement a binary search tree, where two nodes link to form a parent-child relationship.
- This structure helps you locate a specific element in a list of items without having to traverse the entire list individually.
Graphs
Graphs, also known as networks, visually represent the relationships between objects. Lines connecting the objects, called edges, represent these relationships. You can represent a graph using various data structures, such as trees, linked lists, and arrays.
Application of Graphs:
- Social networks represent individuals as nodes, with lines (i.e., edges) defining their relationships. Examples of social networks include Facebook, Twitter, and LinkedIn.
- You can use them to represent geographical networks where individual nodes represent geographical locations, and the edges of the graph show the connections between them. A map of a city or region is an example of a geographical network.
- They can 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 containing multiple elements of the same type. It is similar to an array but uses commas instead of brackets to separate the names of the elements in it.
We usually use tuples to store related data items together in a single variable and perform operations on those items as a group rather than individually.
Application of Tuple:
- They can represent sets of variables in the same order as defined in them. For example, you can use tuples to store people’s names and phone numbers in an address book in a comma-separated form.
- You can use them to group data from two or more sources into a single variable, allowing the same operation to process the data 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.
Also Learn More About:
R Programming vs. Python: A Comparison for Data Science.
Decoding Python Automation and Scripting.
Artificial Intelligence Using Python: A Comprehensive Guide.
Understanding the Functional Programming Paradigm.
Frequently Asked Questions
What are the main types of data structures in Python?
Python’s primary data structures are linear and non-linear. Linear structures include lists, arrays, and strings, where elements are sequentially organised. Non-linear structures include trees and graphs, which allow complex relationships between components. Each type optimises specific operations and is suited for various programming tasks.
How do linked lists differ from arrays in Python?
Linked lists consist of nodes connected via pointers, offering dynamic sizing and efficient insertions or deletions. In contrast, arrays are fixed-size collections stored in contiguous memory. They allow fast access to elements but require resizing or shifting operations for changes in size.
What is the use of tuples in Python?
Tuples are immutable collections of elements that allow you to group related data into a single variable. They are ideal for storing fixed data sets, such as coordinates or records, and they ensure that the data cannot be modified accidentally. Tuples support indexing and iteration for efficient data handling.
Wrapping Up
To conclude, 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.
With this knowledge, you can start professionally honing your skillset by joining the Python Programming Course by Pickl.AI.
Authors
-
Written by:
Asmita KarReviewed by:
