Python is a multi-purpose programming language and has found its applications in web development, data science, task automation, data visualization, and many other things.
Python has gained huge popularity in the technology world as it is relatively easy to learn Python compared to other programming languages. It is a language that works well for personal projects and large-scale applications.
Python is portable and provides plenty of libraries for almost all tasks, making it the best choice to start your programming journey.
In this article, let’s look at the use cases of Python so that you get a clear picture of what you can do with it.
Web applications can range from small personal websites to large-scale social media applications, search engines, e-commerce websites, etc.
Working on complex websites from scratch can be difficult. This is where frameworks come into place. Frameworks are nothing but support structures that act as a foundation to build your applications upon.
Python provides many frameworks for web development, such as Django, Flask, FastAPI, web2py, CherryPy, Masonite, Dash, Jam.py, and Pyramid. Most Python frameworks are beginner-friendly and have a large community to help you.
Don’t get overwhelmed by all these technical terms if you are not familiar with them. You can start learning one concept at a time and gradually become a web application developer.
Being multipurpose is one thing, and being multipurpose and the number one choice in a certain field is another thing. In recent years, Python has become the most preferred language for data science.
Data science is a field of computer science that deals with data and extracting knowledge from the data using statistics and mathematical concepts.
Some of the processes involved during this knowledge extraction are data collection, data cleaning, data extraction, data visualization, data analysis, data modeling, etc.
We can write code for all these processes using Python. Python is preferred in data science because it is easy to code and achieves results in a few lines of code. Python can handle complexities making it the best choice for data science.
Python has a huge collection of libraries for data science to make the life of programmers easy. Here are some commonly used packages.
|Python Library||Use cases|
|Numpy||It deals with scientific computing.|
|Scipy||It is used for high-level computation, which includes scientific and technical computation.|
|Pandas||It is used for data manipulation and analysis.|
|Tensorflow||It deals with numerical computation.|
|Matplotlib||It is used in data visualization.|
|Seaborn||It is also used in data visualization.|
|Keras||It is extensively used for deep learning and neural network modeling.|
|Scikitlearn||It provides almost all machine learning algorithms.|
Data science is a growing field, and it has the potential to change the world. There is a huge requirement for programmers specializing in it. It would be a perfect choice to learn data science if you have learned Python.
Though some go with R, which is also a popular language used in data science, it is still your choice. Choose the language based on your requirements and interests.
Python provides many libraries to build desktop applications. Some of the commonly used libraries are Tkinter, PyQT, EasyGUI, WxPython, PyGUI, and PySimpleGUI.
Out of these libraries, Tkinter is one of the easiest to learn and use to build your GUI applications. You can check out this article to learn how to build a desktop application using Tkinter.
Web scraping is the process of extracting information available on a website using automation software. Python is a perfect choice for web scraping as it is easy to code and robust.
Web scraping has endless use cases like data analysis, sentiment analysis, price monitoring, academic research, automation testing of an application, and many more.
Python provides plenty of libraries to perform web scraping. Some of the famous libraries are BeautifulSoup, Selenium, Scrapy, MechanicalSoup, Urllib, Requests, etc.
Selenium is a Python library that enables us to work with browser drivers and scrape off data from websites. Data can be extracted directly as selenium objects, and their parameters can be accessed through inbuilt selenium functions.
You can check out this step-by-step tutorial to learn how to scrap data from the Internet using Python and selenium.
Game development is the process of developing video games from concept to finished product by programming the conceptualized idea.
Whenever we search for a language to choose for game development, it is always C++ or C#. Why is that? You would have seen comments saying not to choose Python as your language. This is because Python is a language built for coding performance, not computational performance.
Python is slow compared to C++ or C#. Then why did I recommend it in this article? Even though Python is slow, not all games require high computational power. Also, starting your game development in Python makes it easy to understand concepts, and you can migrate to a different language after getting the basics right.
You can build your prototypes with Python to test if the game is working with the logic you came up with or if the game as a whole is working or not.
In recent years, there have been some modules to build games and run them efficiently. One such module is Pygame. Pygame can build fully-fledged games which work in any operating system. Pygame helps the program access the multi-core processor as the backend of the Pygame was built using C and Assembler language.
Some famous libraries other than Pygame are Panda3D, PyOpenGL, Arcade, Kivy, Ren’py, etc. Again, it is your choice to pick the language you want to work on game development. Understand your use case and choose the language accordingly.
There are two types of mobile application development based on the operating systems. One is Android app development, and another is iOS app development.
Most android apps are built using Java and Kotlin. Swift is the programming language preferred for building iOS apps.
Python also provides some frameworks for developing mobile apps. Some of the famous mobile application development frameworks are Kivy, Beeware, Python-for-android, Ren’Py, etc.
Most mobile app developers prefer Java or Kotlin to build their android application development. Python is versatile, but as far as mobile app development is concerned, it is not the best language to go about.
You can check out this article to learn more about Python for mobile application development.
DevOps is a combination of practices that collaborates with software development and IT operations. The work of a DevOps engineer includes monitoring, CI/CD pipelines, deployment, cloud automation, etc. Python is the most commonly used language for DevOps.
Python helps DevOps engineers to build, test, deploy, visualize and monitor the DevOps lifecycle with ease. Python is useful in automating CI/CD pipelines. Ansible and SaltStack are some modules used for this operation.
Python is used for the deployment, configuration, and management of applications. Fabric and Cuisine are some modules used for these operations. Boto is a Python SDK module used for cloud automation. Apache Libcloud is a library used for manipulating cloud infrastructure.
If you search for DevOps jobs online, you will find that Python is a requirement for most of them. If you are interested in development operation and Python, go for it, as it has a lot of scope and demand.
Python has turned out to be a great success in multiple fields of computer science. Python is constantly upgrading itself to meet the demands of programmers. We hope Python further expands its horizons in other fields and also solves the issues in the existing fields.
Apart from the ones mentioned above, Python is also used in several other fields such as task automation, blockchain, cybersecurity, etc. If we had left out any other use cases of Python, do mention it in the comments section.
I hope you found this post useful and informative. Happy coding!