Top 12 Fascinating Python Applications in Real-World [2024] | upGrad blog (2024)

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-

1. Smart Contracts
2. Shared Ledger
3. Consensus/Trust Mechanism
4. Cryptography

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.

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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).

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.

One of the great things about Python is it owns a huge collection of data science libraries like SciPy, NumPy, and Matplotlib. All these libraries help you to conduct visualization and data analysis easily.

Python is famous in the scientific community because of its large collection of libraries for physics, math, machine learning, and engineering. For instance, TensorFlow is a Google Brain library used in ML projects. So, there are plenty of application areas of Python.

It supports different paradigms

Python is a versatile programming language and supports multiple paradigms like functional, object-oriented, and procedural programming. It suggests that you can choose the paradigm that best outfits your requirements and preferences.

Python features a simple syntax for ease of learning and use. This characteristic helps you to explore various application areas of Python, including automation.

It should be your foremost choice to learn various programming paradigms.

Python owns a huge standard library that contains modules for string handling, data manipulation, Internet protocols, file I/O, and operating system interfaces. Python’s syntax makes it easy to learn and understand python programs that are typically used for automation tasks.

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It owns a strong community of developers

Python comes with a powerful community of developers who are always dedicated to assisting you. Many online resources like chat rooms and forums are available. It aids you in solving different Python programming problems.

Python provides support for debugging tools and unit testing. It is, therefore, an ideal language for even advanced software development. You get ample resources to get started with the application of Python due to its vast and friendly community.

It is popular in data science and machine learning

Python has become quite popular in machine learning and data science applications. Its flexibility and readability make it a wonderful choice for prototyping new ideas and algorithms. Many libraries are available to help you easily execute complex data analysis tasks.

For instance, the SciPy library contains scientific and mathematical computation tools. The Pandas library is widely used for manipulation and data analysis. These are only a few of the numerous libraries that Python offers.

One of the key reasons why Python is also a popular programming language in machine learning is that it offers various powerful tools for data handling. These tools help you to conduct tasks like data modelling, transformation, and cleaning. Moreover, various machine learning libraries like Theano and TensorFlow help you to build an advanced application of Python. So, Python is worth considering if you are fascinated by machine learning and/or data science.

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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.

Web development is an amazing application of Python programming. The reason is it offers a broad range of frameworks like Flask, Django, Bottle, and more that streamline developers’ tasks. Python also contains inbuilt libraries and tools that make the web development process quite effortless. You can effectively build the best Python app using its characteristics like wonderful visualization, convenience in development, enhanced security, and quick development process.

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.

Python is also used to develop many contemporary popular game titles like World of Tanks, Sims 4, Eve Online, and Civilization IV. A few other titles that use Python are Doki Doki Literature Club, Mount & Blade, Disney’s Toontown Online, and Frets on Fire. So, the application of Python programming is not only limited to the professional world but game development too.

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.

Two of the trendiest subjects right now are Artificial Intelligence and Machine Learning. With the inbuilt tools and libraries, it facilitates the development of ML and AI algorithms. Moreover, it provides easy, concise, and readable code that makes it simpler for developers to write complicated algorithms.

Few of the built-in tools and libraries that enhance ML and AI processes through the application of Python programming are:

• Keras for Machine learning
• Seaborn for data visualization
• Numpy for complex data analysis
• SciPy for technical computing

5.Desktop GUI

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).

Python is a dynamic programming language that assists developers in easily and efficiently creating GUIs. It features a long list of inbuilt tools like kivy, PyQT,wxWidgets, and several other libraries. These libraries help you to efficiently and securely build a functional GUI.

Python’s modular programming approach and easy-to-understand syntax are the basis for responsive and super-fast GUI. So, they streamline the whole development process. A few of the prominent tools available for GUI development using applications of Python are Tkinter, PyQt, wxWidgets, Python GTK+, and Kivy.

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.

Python is ideal for software development. Famous applications like Google, Reddit, and Netflix use Python. It offers the following great features for software development:

• Platform independence
• High compatibility
• Inbuilt frameworks and libraries to streamline development
• Enhanced code reusability and readability

Python also provides enhanced features to work with swiftly growing technologies like AI and ML. These features make applications of Python the famous choice for software development.

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

Business applications vastly vary from average consumer software. Firstly, provide a set of explicit features instead of plenty of features. Secondly, they target a small user group, commonly an organization. One of the best things about Python is that it perfectly delivers performance-efficient custom solutions. So, it can work on both business applications and consumer applications.

One of the most crucial facets of any application is security. Python’s security features are standout when it comes to business applications. This is because it is built considering information security. One of the great applications of Python programming is scalability through which a business can expand its horizon.

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.

Python features a shorter learning curve compared to other programming languages. So, you can quickly learn the development of applications of Python programming. This facet makes it one of the best options for educational programs. Platforms like Coursera, Udemy, edX, Harvard, and Python Institute are among the leading online providers of Python educational courses.

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.

In other words, web scraping is an automated process for easily and quickly extracting information from websites. Python presents various features that make it appropriate for web scraping and justify the applications of Python programming. Some of the features are:

• Easy to understand and use
• A concise syntax that improves the readability and saves your time
• The web scraping process is made easy and efficient with various tools and libraries like matplotlib, Pandas, and Selenium

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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Other Real Time Applications of Python

1. Python in IoT (Internet of Things)

Python has emerged as a prominent language in the realm of Internet of Things (IoT) development. Its versatility, simplicity, and a rich ecosystem of libraries make it an ideal choice for building robust IoT solutions. Python facilitates the development of IoT applications, from sensor data processing to communication with cloud platforms, ensuring seamless connectivity in the IoT ecosystem.

Python seamlessly integrates with a wide array of IoT devices and platforms. Its adaptability allows developers to connect and communicate with various sensors, actuators, and IoT hardware. Python’s compatibility with popular IoT platforms, such as Raspberry Pi and Arduino, empowers developers to create innovative IoT projects with ease.

Python serves as the backbone for numerous IoT projects, ranging from smart home automation to industrial IoT applications. Frameworks like MicroPython and Zerynth provide Python support for microcontrollers, enabling developers to deploy Python code directly on resource-constrained IoT devices. Additionally, popular IoT frameworks like Home Assistant leverage Python for building comprehensive home automation solutions.

2. Advancements in Python Web Frameworks

Modern Python web frameworks play a pivotal role in web development, offering efficient tools for building scalable and feature-rich applications. Frameworks such as Django, Flask, and FastAPI provide developers with the scaffolding needed to streamline development workflows. Django, known for its batteries-included approach, offers a comprehensive set of features, while Flask embraces simplicity and flexibility. FastAPI, a newcomer, stands out for its speed and automatic OpenAPI documentation.

Each Python web framework has its unique strengths and use cases. Django, a high-level framework, is renowned for rapid development and a built-in admin interface. Flask, a microframework, grants developers more flexibility by allowing them to choose components as needed. FastAPI, designed for building APIs quickly, stands out for its automatic validation and support for asynchronous programming. Comparing these frameworks helps developers choose the one that aligns with their project requirements.

Python web development continues to evolve with emerging trends. Serverless architecture, powered by platforms like AWS Lambda and Azure Functions, is gaining traction, allowing developers to build scalable applications without managing server infrastructure. Microservices architecture, supported by Python frameworks, is fostering the development of modular and maintainable web applications. Moreover, the rise of frontend technologies like Vue.js and React, coupled with Python backend services, contributes to a more dynamic and interactive user experience.

3. Python forNatural Language Processing (NLP)

Python has become a leading language for Natural Language Processing (NLP), enabling developers to work with and analyze human language data. Its simplicity and a wealth of NLP libraries make it accessible for tasks such as text parsing, sentiment analysis, and language translation. Python provides a conducive environment for handling the intricacies of natural language, making it a preferred choice for NLP practitioners.

Python boasts powerful libraries dedicated to NLP tasks. The Natural Language Toolkit (NLTK) offers a comprehensive set of tools for tasks like tokenization, stemming, and part-of-speech tagging. SpaCy, another popular library, stands out for its speed and efficiency in processing large amounts of text. These libraries empower developers to implement sophisticated NLP algorithms with ease, making Python a cornerstone in the field.

Python’s influence extends to the creation of advanced language models and chatbots. With frameworks like TensorFlow and PyTorch, developers can build and train complex language models for tasks like language generation and understanding. Python’s simplicity and extensive community support also contribute to the development of conversational agents and chatbots, enhancing user interactions across various platforms.

Some other real-world applications of Python:

• Automation and robotics through inbuilt tools and libraries like Dart, PyDy, pyro, and PyRobot
• Image processing through tools and libraries like OpenCV, Blender, PIL, and Houdini
• Scientific applications and the best Python app are developed through libraries like Pandas, SciPy, Matplotlib

Latest Trends and Updates in Python

To stay ahead in the tech world, it’s crucial for developers and enthusiasts alike to be aware of the latest trends, updates, and developments in the Python ecosystem. Let’s delve into the exciting advancements that are shaping the future of Python with the help of python course for beginners.

1. Adoption of Python in Machine Learning and AI:

Python’s dominance in the realm of machine learning and artificial intelligence continues to grow. Libraries such as TensorFlow and PyTorch are witnessing widespread adoption, empowering developers to create sophisticated models and applications. The seamless integration of Python with machine learning frameworks reinforces its position as the go-to language for AI enthusiasts.

2. Web Development with FastAPI:

FastAPI, a modern, fast, and highly performant web framework for building APIs with Python 3.7 and above, has gained significant traction. Its simplicity, automatic validation, and support for asynchronous programming make it an attractive choice for developers working on web applications and APIs. FastAPI’s rising popularity showcases Python’s adaptability to evolving web development needs.

3. Serverless Computing and Python:

Serverless architecture has become a popular paradigm in cloud computing, and Python is playing a pivotal role in this space. Platforms like AWS Lambda, Azure Functions, and Google Cloud Functions seamlessly support Python, enabling developers to build scalable and cost-effective serverless applications of python.

4. Increased Focus on Data Science and Visualization:

With the rise of data-driven decision-making, Python’s role in data science and visualization is more prominent than ever. Libraries like Pandas, NumPy, and Plotly continue to be instrumental in handling, analyzing, and visualizing data. Python’s simplicity and extensive ecosystem contribute to its widespread adoption in data-centric applications.

5. Microservices Architecture

Python’s versatility extends to microservices architecture, with frameworks like Flask and Django providing robust support. Microservices enable developers to build scalable and maintainable applications by breaking them down into smaller, independent services. Python’s ease of use and diverse ecosystem contribute to the efficiency of microservices development.

6. Enhanced Type Hinting and Static Analysis

Type hinting, introduced in Python 3.5, has gained momentum, leading to improved static analysis tools and support in popular IDEs. The push towards more statically-typed Python code enhances code quality, readability, and developer collaboration. Tools like MyPy and Pyright contribute to the growing emphasis on static analysis in the Python community.

7. Python in DevOps and Automation

Python’s simplicity and readability make it a preferred choice for DevOps tasks and automation scripts. Infrastructure as Code (IaC) tools like Ansible leverage Python for defining and managing infrastructure. The language’s extensive standard library and third-party modules facilitate seamless automation across various domains.

8. Quantum Computing with Python

As quantum computing gains momentum, Python is becoming a language of choice for quantum programming. Libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python’s accessibility is lowering the barrier for entry into the fascinating field of quantum computing.

Conclusion

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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