It is a well-established fact that Python is one of the most popular programming languages in both the coding and Data Science communities. But have you ever wondered why Python is so popular? What is the secret behind Python’s worldwide success and fame?
We’ll give you the answer in one line – Python is one of the top programming languages of all with a slew of applications of Python.
Whatever be your development and Data Science need, you name it – Python can take care of it as well as other related Python applications. Python is an open-source, high-level, general-purpose programming language that incorporates the features of object-oriented, structural, and functional programming.
Some believe that Java is a better language. However, it goes with saying that the former is much faster, but Python is easier to handle/read, versatile, and comes with a simple syntax. As per Stack Overflow, Python—general use and interpreted language rank fourth on the list of most popular languages for coding. It also finds immense use for Python applications.
Created way back in 1989 by Guido Van Rossum, Python stresses on the DRY (Don’t Repeat Yourself) principle, which enhances the readability of Python code. Python’s robust string manipulation, a massive collection of user-friendly libraries, and easy shell access make it a useful tool for quickly automating repetitive tasks.
While Python’s simple syntax allows for writing readable code, which can be further applied to complex software development processes to facilitate test-driven software application development, machine learning, and data analytics. Python can run on all the major operating systems, including Windows, Linux, and iOS.
Since it functions on cross-platform operating systems, Python can be used to develop a host of applications, including web apps, gaming apps, enterprise-level applications, ML apps, image processing, text processing, and so much more.
But beyond its innate simplicity and versatility, what makes Python stand out are its vast assortments of libraries and packages that can cater to a wide range of development as well as Data Science requirements.
Understanding Python applications
Given that Python is now used in data science and related applications, its growing popularity among developers is natural. The common applications of Python are so relevant that it has now become a significant resource for those looking to begin a career in data science. Having a good grasp of Python allows you to strengthen your analytical skills. Today, a data scientist or even someone in the IT sector is expected to come with relevant and new-age skills.
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A common query few might have in mind is, whether Python-run blockchain is tough to learn. It is a technology that is complex yet groundbreaking after all! However, this must never deter you from trying to get a grasp on the same. The core Blockchain concepts are mining, decentralization, and consensus mechanism. This is an immutable blockchain that is also secured cryptographically.
If you want to kick-start a career in this domain, begin by understanding the fundamentals of the same and upskill your Python programming skills to develop blockchain applications. Start your journey by studying these top four concepts of blockchain first-
- Smart Contracts
- Shared Ledger
- Consensus/Trust Mechanism
What makes Python the “Best of the Best?”
The top application of Python ensures that the language remains popular at a professional level. Here are some of the most noteworthy features of Python that make it an excellent tool for Python application professionals of all skill levels:
Python = Simplicity
We cannot stress this point enough, but Python is not only easy to learn but also easy to use and implement across any application of python. With a syntax similar to English, you can master the nitty-gritty of python application and coding in a few days. Moreover, Python is dynamically-typed, which makes indentation mandatory, thereby enhancing its readability factor.
It is an open-source language
You don’t need to pay charges to install and use Python – it is open-source. What this means is that the source code of Python is freely available to the public. You can download it from Python’s official website. Not only that, Python supports the FLOSS (Free/Libre and Open Source Software) model, which means you can also change it and distribute it. This allows the Python community to tweak it and improve its features continuously.
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It is a high-level language
Since Python is a high-level language, you need not remember its system architecture, not do you need to perform memory management. This feature contributes to Python’s user-friendliness.
It is interpreted
Unlike compiled languages like C++ and Jave wherein you must compile the code and then run it, Python is an interpreted language. What this means is that instead of executing the source code all at once, Python executes it line by line. This makes it easier to debug a Python code because you can do it while writing the code.
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It is both object-oriented and functional
An object-oriented programming language is one that can model real-world data, while a functional language focuses on functions (code that can be reused). Python supports both object-oriented and functional programming features. Also, unlike Java, Python supports multiple inheritances. Naturally, this opens up a lot of scope around the topic- what are the applications of python?
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It is portable
Python is portable and highly flexible, meaning, a Python code written for a Windows machine or a Linux machine can also run on iOS, and vice versa – you don’t need to make any alterations in the code. So, with Python eliminates the need to write different code for different machines (just make sure there’s no system-dependent feature in your Python code).
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It is extensible and embeddable
Python is an extensible language, as it allows you to write specific parts of your Python code in other programming languages such as C++. Similarly, you can also embed your Python code in the source code of other languages. This allows you to integrate Python’s scripting functionalities into a code written in another language. This also leads to a number of applications of Python programming being developed as per need. When you look up on what are the applications of Python programming, you can get an idea of myriad Python uses.
It comes with a vast collection of libraries
When you download Python, you will automatically download the extensive collection of Python libraries with it. These libraries are built-in, so you don’t have to write individual code for every single thing. Python has libraries and packages for web browsers, threading, databases, regular expressions, image manipulation, documentation-generation, unit-testing, CGI, email, and much more.
Now that we’ve talked at length about how great a tool Python is let’s check out twelve real-world applications of Python or popular python uses.
12 Real-world Applications of Python
Python is a very stable programming language choice that is in use at the developers’ end as well as thought of as an apt choice for automation of deployment automation. Of course, it has a lot of uses in web-related development. Even the non-developer groups believe that once they have a hang of the framework in Python, it is a preferred language for conducting data-related work.
1. Web Development
When it comes to web development, Python should be your go-to tool. Why?
That’s because Python offers numerous options for web development. For instance, you have Django, Pyramid, Flask, and Bottle for developing web frameworks and even advanced content management systems like Plone and Django CMS. These web frameworks are packed with standard libraries and modules which simplify tasks like content management, database interaction, and interfacing with internet protocols like HTTP, SMTP, XML, JSON, FTP, IMAP, and POP.
Python web frameworks are known for their security, scalability, and flexibility. To add to that, Python’s Package Index comes with useful libraries like Requests, BeautifulSoup, Paramiko, Feedparser, and Twisted Python.
2. Game Development
As we mentioned earlier, Python comes loaded with many useful extensions (libraries) that come in handy for the development of interactive games. For instance, libraries like PySoy (a 3D game engine that supports Python 3) and PyGame are two Python-based libraries used widely for game development. Python is the foundation for popular games like Battlefield 2, Frets on Fire, World of Tanks, Disney’s Toontown Online, Vega Strike, and Civilization-IV.
Apart from game development, game designers can also use Python for developing tools to simplify specific actions such as level design or dialog tree creation, and even use those tools to export those tasks in formats that can be used by the primary game engine. Also, Python is used as a scripting language by many game engines.
3. Scientific and Numeric Applications
Thanks to its massive library base, Python has become a crucial tool in scientific and numeric computing. In fact, Python provides the skeleton for applications that deal with computation and scientific data processing. Apps like FreeCAD (3D modeling software) and Abaqus (finite element method software) are coded in Python.
Some of the most useful Python packages for scientific and numeric computation include:
- SciPy (scientific numeric library)
- Pandas (data analytics library)
- IPython (command shell)
- Numeric Python (fundamental numeric package)
- Natural Language Toolkit (Mathematical And text analysis)
4. Artificial Intelligence and Machine Learning
AI and ML models and projects are inherently different from traditional software models. When we talk about AI/ML projects, the tools and technologies used and the skillset required is totally different from those used in the development of conventional software projects. AI/ML applications require a language that is stable, secure, flexible, and is equipped with tools that can handle the various unique requirements of such projects. Python has all these qualities, and hence, it has become one of the most favored languages of Data Science professionals and Python is a must tool in data science courses.
Python’s simplicity, consistency, platform independence, great collection of resourceful libraries, and an active community make it the perfect tool for developing AI and ML applications. Some of the best Python packages for AI and ML are:
- SciPy for advanced computing
- Pandas for general-purpose data analysis
- Seaborn for data visualization
- Keras, TensorFlow, and Scikit-learn for ML
- NumPy for high-performance scientific computing and data analysis
Apart from these libraries, there are also other Python-based libraries like NLTK, Caffee, PyTorch, and Accord.NET, that are useful for AI and ML projects.
Python not only boasts of an English-like syntax, but it also features a modular architecture and the ability to work on multiple operating systems. These aspects, combined with its rich text processing tools, make Python an excellent choice for developing desktop-based GUI applications.
Python offers many GUI toolkits and frameworks that make desktop application development a breeze. PyQt, PyGtk, Kivy, Tkinter, WxPython, PyGUI, and PySide are some of the best Python-based GUI frameworks that allow developers to create highly functional Graphical User Interfaces (GUIs).
6. Software Development
Python packages and applications aim to simplify the process of software development. From developing complex applications that involve scientific and numeric computing to developing desktop and web applications, Python can do it all. This is the reason why Software Developers use Python as a support language for build control, testing, and management.
For instance, SCons is designed explicitly for build control, Buildbot and Apache Gump allow for automated continuous compilation and testing, and Roundup and Trac are great for bug tracking and project management.
Python also supports data analyzation and visualization, thereby further simplifying the process of creating custom solutions minus the extra effort and time investment.
7. Enterprise-level/Business Applications
Enterprise-level software or business applications are strikingly different from standard applications, as in the former demands features like readability, extensibility, and scalability. Essentially, business applications are designed to fit the requirements of an organization rather than the needs of individual customers.
Thus, these applications must be capable of integrating with legacy systems like existing databases and non-web apps. Since business applications are developed, keeping in mind the custom requirements to cater to the specific needs of an organization’s operating model, the entire development process becomes very complicated.
This is where Python can make a significant difference. Python high performance, scalability, flexibility, and readability are just the features required for developing fully-functional and efficient business applications. Furthermore, Python has other tools for business application development, like:
- Odoo, an all-in-one management software that forms a complete suite of enterprise management applications.
- Tryton, a three-tier, high-level, general-purpose application platform, is another fantastic tool for building business applications.
Learn more about: Top Python tools
8. Education programs and training courses
If there’s any beginner-friendly programming language, it is Python. We’ve said it many times before, and we’re repeating it – Python has an extremely straightforward syntax that’s similar to the English language. It has a short learning curve and hence, is an excellent choice for beginners. Python’s easy learning curve and simplicity are the two main reasons why it is one of the most used programming languages in educational programs, both at beginner and advanced levels.
However, Python is not just great as an introductory language – even professional developers and coders all around the world rely heavily on Python.
9. Language Development
Over the years, Python’s design and module architecture has been the inspiration behind the development of many new programming languages such as Boo, Swift, CoffeeScript, Cobra, and OCaml. All of these languages share numerous similarities with Python on grounds like object model, syntax, and indentation.
10. Operating Systems
Yes, Python is the secret ingredient behind many operating systems as well, most popularly of Linux distributions. Linux-based Ubuntu’s Ubiquity Installer and Fedora and Red Hat Enterprise’s Anaconda Installer are coded in Python. Even Gentoo Linux leverages Python Portage (package management system). Usually, Python is combined with the C programming language to design and develop operating systems.
11. Web Scraping Applications
Python is a nifty tool for extracting voluminous amounts of data from websites and web pages. The pulled data is generally used in different real-world processes, including job listings, price comparison, R&D, etc.
BeautifulSoup, MechanicalSoup, Scrapy, LXML, Python Requests, Selenium, and Urllib are some of the best Python-based web scraping tools.
12. Image Processing and Graphic Design Applications:
Alongside all the uses mentioned above, Python also finds a unique use case in image processing and graphic design applications. The programming language is used globally to design and build 2D imaging software like Inkscape, GIMP, Paint Shop Pro, and Scribus. Also, Python is used in several 3D animation packages such as Blender, Houdini, 3ds Max, Maya, Cinema 4D, and Lightwave, to name a few.
With so many uses up its sleeve, Python ranks as a highly loved language for programming. It is a top pick of software engineers and hackers, too, since it is laced with flexibility, versatility, and object-oriented specifications.
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After reading about all these versatile and diverse real-world applications of Python, it is safe to conclude that Python is capable of handling almost any development requirement. In the last few years, Python applications have gained newfound traction in the field of Data Science as well, particularly in Machine Learning.
Python has brought in a lot of changes to the industry given it is easy to use as well as comes packed with powerful libraries. Additionally, it offers a wide range of applications that boost productivity. The jobs in Python pay hefty packages over time and Python developers are in high demand. The ease of learning Python makes it an option to bag a steady and well-paying job too.
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What factors contribute to the popularity of Python in 2022?
What are the career aspects of a Python developer?
Python is a very versatile language and if you have a good knowledge of Python, there can be various career opportunities in your hand. Some of these opportunities are as follows: You can be a Python developer right after acquiring the Python knowledge. Python developers are responsible for building websites, optimize data algorithms, or write clean and efficient Python codes. A data analyst has to deal with large sets of data, analyze them and create visualizations out of them. If you are a Python geek and love to play with data then this job is for you. Project management is in high demand as a project manager is highly responsible for the business and marketing of the companies. A machine learning engineer trains the machines or models for making predictions on the basis of the data provided to them.
What are the most popular Python libraries?
Python has a wide range of rich libraries and modules but being a Python geek, you must be handy with the top and most used Python libraries. The following are some of the most popular Python libraries: TensorFlow is a boon to Machine Learning engineers. This library is developed by Google and can be considered a computational library. Numpy is again a machine learning library used by other Python libraries like TensorFlow to perform internal operations. Keras is another popular Python library that provides a convenient mechanism for neural networks.