Python, from its inception in 1991, has been committed to code readability. A brainchild of Guido van Rossum, it is now supported and advanced by the Python Software Foundation. Take a brief look at what we will be covering in this blog.
An Introduction to Python
As the fastest-growing language in the world, this general-purpose, high-level programming language has found immense applications in the real world and has become one of the most sought-after certifications.
Python allows programmers to work and integrate systems in a more efficient manner. In addition to its focus on readability, Python’s syntax allows for reduced lines of code. There are two major Python versions:
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Benefits of Using Python
Several factors contribute to Python’s popularity with all its features and benefits:
- High-level language
- Object-oriented language
- Easy to learn
- User-friendly data structures
- Dynamically typed language, i.e., the data type is not required
- Open source and community development
- Extensive support libraries such as pandas, NumPy, etc.
- Portable across different operating systems
- Presence of third-party modules
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Applications of Python in the Real World
Have you ever wondered, what is Python used for? The wide range of Python uses and applications is proof of why it is the go-to programming language for most developers. Let us discuss the top 12 applications of Python:
Web Application Development
Web application development undeniably falls under the top applications of Python due to its provisions for scalability, convenience, and security. Python comes with several Web Development frameworks such as Django, Pyramid, and Flask. Django’s framework comes with standard libraries that allow for protocol integration, such as HTTPS, SSL, FTP, etc., and even help with email, XML, JSON, and many more. The development time is significantly reduced, and as a result, there is more time to market the application. The dynamic development features make it a perfect tool for developing Python web applications.
Data Science and Big Data
The crucial part of Data Science and Big Data is the extraction of valuable insights from large data sets and data visualizations. This is where Python’s libraries, such as NumPy, SciPy, pandas, TensorFlow, Matplotlib, etc., come into play. pandas, NumPy, SciPy, etc., work with data, while Seaborn and Matplotlib are used for data visualization through graphs.
Data science is very popular and widespread. It is a preliminary requirement for data-based organizations and research. It stands to reason that the data science field has contributed tremendously to Python’s popularity as well.
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Artificial Intelligence and Machine Learning
Python can handle computations that are crucial to building Machine Learning models. It is stable, secure, and has important applications in Machine Learning algorithms. The plethora of libraries it has includes Keras, SciPy, pandas, NumPy, and many others. Many developers find Python applications for building ML algorithms easy and convenient.
Python is also great for AI solutions including Data Analytics, advanced computing, text and data processing, image recognition, and several others. Python also supports the domain with its extensive libraries.
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Owing to its numerous functionalities and add-ons, Python has proved to be a significant contributor to the gaming industry. Game developers have adopted the language for the rapid prototyping of interactive video games. Here are the top Python frameworks that are used in game development:
- Pygame: It helps to build fully featured games and multimedia programs.
- PyKyra: It is a fast framework for game development. It supports direct image reading, MPEG video, MP3, WAV, Ogg Vorbis, etc.
- Pyglet: It supports OpenGL graphics, UI event handling, windowing, loading images and videos, joysticks, and playing sounds and music
- PyOpenGL: It is interoperable with several external GUI libraries for Python including PyGame, Raw Xlib, PyQt, and many others.
- Kivy: It runs on OS X, Windows, Linux, iOS, Android, and Raspberry Pi and can use most inputs, protocols, and devices including WM_TOUCH, WM_PEN, Magic Mouse, Mac OS X Trackpad, Linux Kernel HID, Mtdev, and TUIO.
- Panda3D: It is used for real-time Python applications in 3D games, simulations, experiments, visualizations, etc.
- Cocos2d: It helps to build apps, games, and some cross-platform GUI-based interactive programs.
- Python-Ogre: It is a rendering engine that has speed and flexibility and supports an impressive feature set for game development.
- Ren’Py: It runs on every computer, has the ability to handle complex game logic, and is used in digital storytelling due to its easy handling of word combinations, images, and sounds to create visual novels and life simulation games.
- PySoy: It is a 3D game engine and supports Python 3.
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Creating desktop GUIs is another application of Python. Python is also used in the programming of desktop applications. Its Tkinter library helps with the development of UIs. Kivy, wxWidgets, PyQt, etc., are used to build applications on several platforms.
It should not come as a surprise that embedded systems development also has used Python. Engineers and students are rapidly becoming familiar with Python programming. Maker movement and Raspberry Pi can be considered to be the reasons for Python’s popularity. The language itself is very flexible and easy to learn has led to the application of Python in microcontroller-based environments as well. Python helps to perform high-level applications on smaller devices since it is used to develop Embedded C software.
Simplifying the software development process for complex apps is one of the applications of Python. Developers implement SCons for the purpose of building control. They carry out automated continuous compilation and testing by using Buildbot and Apache Gump. Bug tracking and project management are done with Roundup and Trac. Data visualization and analysis are also possible through Python simplifying the custom solution process.
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Business applications are significantly different from web applications. Business applications serve the requirements of organizations rather than individual users. Organizations prefer Python due to their need for application scalability, extensibility, and readability. Tryton and Odoo are good examples of platforms that aid in developing such business applications.
Business applications can get complicated as security and database handling capabilities are of utmost importance. Python can handle multiple database requests at once.
Web Scraping Applications
When it comes to extracting large amounts of data from websites, Python is a winner. The process of web scraping has proved to be very useful for pulling valuable information and making smart decisions. MechanicalSoup, Selenium, and Python Requests are some of the tools that are used to develop such applications. Python makes it easy to write software that can handle large amounts of data.
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Python language applications are also seen in the education sector. Python is such a huge part of the programming world today that it is considered a core language requirement for students in schools and colleges. Python is an easy-to-learn programming language for beginners since its syntax is similar to English.
With a myriad of Python applications in the world of gaming, data science, machine learning, deep learning, artificial intelligence, etc., it is evident that Python’s importance will increase over the years.
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CAD stands for Computer-Aided Designing. The CAD applications help Product Designers and Engineers to design precision products. Python helps in working with functions, objects, and their representation which can be quite difficult in the absence of a programming language like Python. The enterprise-grade features offered by Python such as macro recording, robot simulation, technical drawing modules, etc. help in working on CAD applications. Commonly used CAD tools offered by Python are Fandango, PythonCAD, FreeCAD, and more.
Image Processing and Graphic Design Applications
The role of Machine Learning, Deep Learning, and Neural Networks in image processing has increased manifolds. Languages like Python offer a range of libraries that help Data Scientists in their initial preparatory phase before the final data analysis. The top and most commonly used image processing libraries offered by Python are OpenCV, Scikit-Image, and Python Imaging Library(PIL). Some other common image processing apps using Python are GIMP, Corel PaintShop, Blender, and Houdini.
Now, coming to the usage of Python in Graphic Design Applications, it is used in a variety of graphic design projects for creating unique and aesthetically pleasing projects. Python plays an important role in the high interactiveness and visual appeal of graphic design and in offering unique features to the users.
Python is an all-around tool irrespective of the field you are into. Hence, getting well-versed with this tool can be rewarding for your career. In this blog, we discussed the applications of Python in various fields. We hope that by now you are clear about how Python makes life easier for Data Scientists, Software Developers, Product Designers, and others.