There are many spheres of use for Python, but it is especially good for some of them. When this language started to be used by popular services such as YouTube, Quora, Pinterest, Facebook, Reddit, Instagram, Google, Netflix, and Spotify, it has become a new favorite language for software developers. We invite you to study the most popular niches of Python usage in this article.
Table of Contents
Development of Mobile Apps
Until recently, there have been almost no apps for Android in Python. However, many enthusiasts around the world have worked hard to adapt one of the best programming languages, which is mainly used for web development, to the mobile app industry. While most prefer to use alternative options in mobile development, many apps simply wouldn’t be able to work properly without Python. For example, the most popular photo service Instagram was written in Objective-C, but its server part is completely dominated by Python. Besides, it is also used on Yahoo, Red Hat, Dropbox, Pinterest, Quora, Mail.ru, and Yandex.
Over time, developers began to create full-fledged mobile apps in Python. In particular, Python software development for mobiles became possible thanks to two frameworks – Kivy and BeeWare:
- Kivy is an open-source library that is used to develop cross-platform applications. It allows you to create Python GUI apps that run on major platforms from Windows and Android to iOS and macOS.
- BeeWare offers a set of tools that you can use to write mobile and desktop apps in this language.
The key difference between Kivy and BeeWare is that the latter uses a platform-specific native UI toolkit and therefore it is preferred by many developers.
Web Development
Even though this language can be used for Android or iOS software creation, it gained the greatest popularity in web development. It is a very convenient language – it does not compile and looks like a plain text file before the launch. Besides, it requires less code, so it looks almost like ordinary English sentences.
There are many frameworks for web development in Python, some of which not only facilitate the process but also provide tools that allow you to make a finished site in just a couple of days. Such frameworks belong to the “full-stack” category. They’re cool, they have tons of tools that include everything required. For example, these are:
- Django – a high-level framework designed for fast and efficient development. The key principles of Django are DRY and rapid development that encourage reuse of the code (Instagram, Spotify).
- Pyramid – this framework is suitable for projects of any size. It has useful features for creating complex applications or scaling small sites for an increased load (Substance-D, Easy Blog Networks).
- TurboGears – an open-source framework for rapid development of data-driven applications (Apache Allura, Kamisons).
- Flask – this is a WSGI framework that is suitable for both simple websites and complex platforms. It is compatible with third-party libraries and has many extensions (Netflix, Lyft).
- Tornado – an asynchronous framework that is capable to simultaneously support many user connections. It is perfect for tasks that require a long-term connection with each user (Uploadcare).
These are only the most popular frameworks. Some are more flexible, others are easy to use; some are better for small projects, while others are better for large ones. However, all of them simplify the development process and allow you to make your projects, instead of creating database interfaces or making parsing requests.
Data Processing (Including Data Analysis, Machine Learning, and Visualization)
We live in a data age that is enriched with better computing power and more storage resources. While the data is growing, the real challenge is to make sense of all this info. Businesses and organizations are trying to tackle this by building intelligent systems using concepts and methodologies from Data Science, Data Mining, and Machine Learning.
There may be several situations when we need machines to make efficient data-based decisions. Below are some of the circumstances under which machine learning would be more effective:
- Lack of human experience;
- Dynamic scenarios;
- Difficulty in transferring expertise into computational tasks.
From recent times, Python has been increasingly used for data analysis, both in science and commerce. The simplicity of the language, as well as the wide variety of open source libraries, make it possible for developers to study any topics and draw conclusions based on the received results. For example, they use Python for analyzing the coronavirus spread around the world, the dynamic of sales on various days of the week, and student’s performance in various universities. Below you will find some other examples of its usage:
- Emotional analysis;
- Weather forecasting;
- Stock market analysis;
- Customer segmentation;
- Fraud detection and prevention;
- Product recommendation for a client.
Finally, Python can be efficiently used for data visualization, which is a big part of the work of data scientists. Data visualization helps you present large and complex datasets in a simple and visual way. Matplotlib is a popular data visualization library written in Python. By using this tool, you can easily build nice diagrams, histograms, bar plots, box plots, etc.
Writing of the Scripts
Python has also gained popularity with some large gaming projects. The following games have been partially developed in this language:
- Civilization IV;
- World of Tanks;
- Mount and Blade;
- Sims 4;
- Battlefield 2;
- EVE Online.
How is Python used in games? Mostly for writing scripts that launch scenes, characters interaction, and game events. Some developers have managed to adapt it even for graphics.
This language is often used for prototyping programs that are later rewritten in alternative languages. It is very convenient because the creation of such prototypes is very fast, it also helps to understand how the program will look like. Python can be used in all large projects as a prototyping tool, but what about the final version?
If your project does not apply machine learning, and the speed of loading is vital, then there is hardly a place for Python. However, a hybrid of different languages is often practiced, such as Python and C ++. This approach allows achieving both high development and program execution speed. While most of the code is written in Python, C ++ is applied for those sections that affect the execution speed.
In Conclusion
Python is one of the five most popular programming languages. It is used in a wide variety of IT areas such as web development, machine learning, mobile application, and even game creation. It is easy to learn, it has a pleasant syntax, and provides a set of tools sufficient for solving any problems. While it cannot fully replace Java and PHP as the key languages in web development, Python is still a number-one solution for machine learning.
