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 today’s 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 (Pandas, NumPy, etc.)
- Portable across different operating systems
- Presence of third-party modules
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Applications of Python Programming Language 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 a go-to programming language for most developers. Let’s discuss the top 10 Python applications:
Web Application Development
Web application development undeniably falls under the top Python applications due to its provisions for scalability, convenience, and security. It 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 datasets and data visualization. This is where Python’s libraries, such as NumPy, SciPy, Pandas, TensorFlow, Matplotlib, etc., come into play. While Pandas, NumPy, SciPy, etc. work with data, Seaborn and Matplotlib are used for data visualization through graphs.
Data Science is very popular and widespread, and the preferred language by most Data Scientists is Python. It is preliminary 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 more. This programming language supports the domain with its extensive libraries.
Python has proved to be a significant contributor to the gaming industry, owing to its numerous functionalities and add-ons. Game developers have adopted the language for the rapid prototyping of interactive video games. Here are the top Python frameworks used in game development:
- Pygame: Helps build fully featured games and multimedia programs
- PyKyra: A fast framework for game development and supports direct image reading, MPEG video, MP3, WAV, Ogg Vorbis, etc.
- Pyglet: Supports OpenGL graphics, UI event handling, windowing, loading images and videos, joysticks, and playing sounds and music
- PyOpenGL: Interoperable with several external GUI libraries for Python, including PyGame, Raw Xlib, PyQt, and many others
- Kivy: 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: Used for Python real-time applications in 3D games, simulations, experiments, visualizations, etc.
- Cocos2d: Helps build apps, games, and some cross-platform GUI-based interactive programs
- Python-Ogre: A rendering engine that has speed and flexibility and supports an impressive feature set for game development
- Ren’Py: Runs on every computer, has the ability to handle complex game logic, and used in digital storytelling due to its easy handling of word combinations, images, and sounds to create visual novels and life simulation games
- PySoy: A 3D game engine and supports Python 3
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Creating desktop GUIs is another one of the Python applications. 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 shouldn’t come as a surprise that embedded systems development has uses of Python as well. Engineers and students are rapidly becoming familiar with Python programming. Maker movement and Raspberry Pi can be considered the reasons for its popularity. Besides the language itself being very flexible and easy to learn, it has led to Python applications in microcontroller-based environments as well. Since Python is used to develop Embedded C software, it helps perform high-level applications on smaller devices.
One of the Python applications lies in simplifying the software development process for complex apps. Developers implement SCons for the purpose of build control. They carry out automated continuous compilation and testing using Buildbot and Apache Gump. Bug tracking and project management are done with Roundup and Trac. Data analysis and visualization are also possible through Python, simplifying the custom solution process.
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Business applications are significantly different from web applications. These applications serve the requirements of an organization rather than individual users. Organizations prefer Python due to their need for application scalability, extensibility, and readability. Tryton and Odoo are good examples of a platform that aids 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. This 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 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. Since its syntax is similar to English, it is an easy-to-learn programming language for beginners.
With a myriad of Python applications in the world of gaming, Data Science, Machine Learning, Deep Learning, Artificial Intelligence, etc., it is evident that its importance will increase more over the years.
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