Python has become quite a popular programming language in the last several years. According to the 2021 Stack Overflow Developer Survey, Python is the world’s third most popular programming language, following JavaScript (#1) and HTML/CSS (#2). It beats PHP, Node.js, SQL, Ruby, and Perl.
Python allows developers to create complex software applications with a concise syntax and encourages rapid development. There’s an open source community where developers can find plenty of resources, like support, pre-built frameworks, and reusable code. This is particularly attractive because it cuts down on the amount of time required to build applications.
Python is excellent for new developers
There are so many different languages to choose from, and some have more benefits than others. For new developers, Python is the ideal programming language to learn. Even seasoned programmers struggle with code readability issues when using Java and various C languages. Python was created to solve this problem and make it possible to write code faster. New developers won’t have any trouble learning this amazing language and can start writing real code on the first day.
Well-known applications created with Python
If you haven’t heard of Python, you’re definitely aware of some of the applications powered by Python. Some of the most popular Python-based apps include:
- Quora
- BitTorrent
- DropBox
- Bit.ly
- Uber
- Wikipedia
- Lyft
- Disqus
- Spotify
Chances are, you’ve used at least one of these applications, and there will be many more popular Python-powered apps developed in the future. Also, some video game designers have made games in Python.
Many popular tech companies rely on Python to maintain libraries. For example, 21% of Facebook’s codebase is written in Python.
There’s no denying that Python has a great future and is quickly becoming the go-to programming language for projects concerning artificial intelligence, machine learning, and big data.
Python for data analytics and science
Data analytics is huge; approximately 58% of Python projects are for data analytics. The entire market is expected to double from what it was in 2018, to reach $103 billion by 2027.
There are roughly 72,000 Python libraries available in the Python Package Index, which makes it ideal for data analytics. There’s also Pandas, a specific data analysis library, which facilitates importing data and time-series analysis.
There are drawbacks to using Python for data analysis, but some companies find the pros outweigh the cons. For example, once a development team creates a project in Python, they can develop new projects more easily since they will already be familiar with the language. Companies benefit from consolidating all of their projects into one language, and for many, the choice is Python.
For science projects, SciPy supports scientific analysis and PyBrain allows developers to model and build neural networks.
When it comes to data science, many people need to manipulate research data, but they don’t have programming experience. Many of these people learn Python to aid in their research, reporting, and analysis. With machine learning algorithms to identify patterns, data scientists use Python to make predictions. Various functions, like data transformation and visualization, can be automated with libraries like NumPy, Pandas, and Matplotlib. Keep in mind, however, that many developers prefer Julia to Python for data science, but it all depends on the project.
Python for financial analysis
Since Python can handle big data, it’s also used for analyzing and computing financial data. For example, Python can handle the vast amount of data generated by stock markets to import price data and identify trading opportunities. Other uses in the financial sector include optimizing portfolios, managing risk, modeling and visualization, detecting fraud, and analyzing cryptocurrency.
Python powers AI projects
For artificial intelligence development projects, Python is preferred. AI developers love the readable code and large library of frameworks suited for machine learning. Large data sets are easily converted to graphs and reports. A research lab called OpenAI uses Python’s Pytorch for deep learning to train their AI systems.
Python frameworks
Software developers rely on frameworks to start building applications quickly. Why start building code from scratch when you can automate redundant tasks? A framework is a pre-made structure that provides a basic foundation of low-level details, like sockets, process management, and protocols. Using a framework saves time and allows developers to spend more time on application logic.
There are three different types of frameworks: full-stack, micro, and asynchronous. Each has pros and cons that make them ideal for certain projects.
Full-stack Python frameworks
A full-stack framework includes all the tools required to develop an application, including massive libraries. It’s an end-to-end solution that makes it easy to build the front and back end, including the user interface, template layouts, form generators, and the database.
Full stack frameworks come with an application server, database, and a web server, along with various tools to develop, test, and deploy applications. These frameworks are perfect for complex projects that require integrations.
Django is Python’s best full stack, scalable framework
The point of using a framework is to save time writing code, and Django delivers on this promise. Since it’s open source, the community continually releases plugins and pre-built, reusable code to make life easy for developers. There are extensive third-party libraries, which is why brands like YouTube and Spotify use Django.
With Django, developers don’t need to worry about database management or creating HTML templates. It also includes an object-relational mapper (ORM) for database interaction, an HTML template engine, session management support, URL routing, Authentication, and plenty of libraries.
Developers choose Django because it makes projects cost-effective since it saves so much time. Django also comes with an excellent security system that helps developers avoid costly problems later down the road. For example, it comes with built-in protection against cross-site scripting, clickjacking, SQL injection, and request forgery. It’s also frequently updated and vulnerabilities are patched fast.
There is a reason YouTube, Instagram, and The Washington Post use Django. This framework can be used to create powerful content management systems, social media websites, custom CRM systems, document management applications, and mobile applications. With an ORM layer, it’s easy to migrate a Django project to any database by altering the code just a little.
Best of all, you get a pre-made administrator user interface with Django. With other frameworks, you have to build this yourself. Django automatically generates an admin panel for every project, and of course, it is fully customizable.
TurboGears is another full-stack python framework
Coming in at a close second to Django is TurboGears. Based on Ruby, this particular framework was built according to the Model-View-Controller (MVC) pattern. Developers have the ability to repurpose business logic cross-platform, which drastically reduces their time spent writing code.
For those who don’t like full-stack frameworks, there is talk of a minimal version of TurboGears that will specifically help developers build software applications faster, while maintaining all the features people love.
Micro Python frameworks
Micro frameworks are popular amongst developers who like to maintain more control over their code and processes. Typically, a micro framework will be excellent in a specific area, and that’s why developers are loyal to their favorites.
A micro framework doesn’t come with tools or extensive libraries. By definition, a micro framework has a library smaller than 5k. You won’t find features like a database abstraction layer or form validation like you will with other frameworks. However, these can be added with extensions.
Some developers prefer a micro framework so they can more easily track the modular structure of their applications. The best way to get clean, maintainable code is to separate the presentation layer from the application logic.
Flask is Python’s most popular micro framework
Although it’s small, Flask is scalable, which makes it ideal. Developers tend to prefer to start small and scale, which is perfectly doable with Flask since it modulates the codebase as it gets bigger. This micro framework is capable of processing numerous requests, and you can see this in action since Pinterest moved from Django to Flask without issue.
Perhaps the biggest reason developers love Flask is because you can change just about anything, which isn’t true for Django. Developers can control the codebase in its entirety and select any components for their applications. Flask is highly flexible and easy to use, even for new programmers. The documentation is extensive and well organized.
What sells developers on Flask is often the ability to perform tests without using a separate framework. This isn’t the case with many frameworks, even popular ones.
Asynchronous Python frameworks
An asynchronous framework uses Python’s asyncio library to run concurrent processes. These frameworks are specifically designed to manage multiple concurrent processes.
Sanic is the most popular asynchronous framework
Although Sanic doesn’t support Python versions before 3.6, it’s easy to use right out of the box, which makes it easy and fast to deploy without getting stuck reading loads of documentation. The syntax is easy to read and looks like any other framework.
When it comes to asynchronous frameworks, Sanic is the most loved. It has all the features developers need, including class-based views, streaming, cookies, versioning, sockets, routing, blueprints, middleware, static files, and more. It doesn’t provide database support, templating, or queues, but these can be added by using existing Python async libraries.
Tornado is a unique asynchronous Python framework
While technically not a framework, Tornado is made up of asynchronous modules used to create several different frameworks, including tornado.template, tornado.routing, and tornado.web, among others.
Many developers love Tornado, even though it hasn’t become mainstream due to the lack of WSGI support.
Other asynchronous frameworks
There are many other good async frameworks for Python:
- Vibora
- Quart
- FastAPI
Each of these frameworks have pros and cons, but are perfect in their own right for specific projects. With Python, developers have plenty of choices.
Why use Python?
With a handful of great programming languages, why is Python becoming the number one choice for development projects? We’ve already discussed some of the benefits in passing, but here’s a list of characteristics that make Python appealing to both engineers and developers:
- Clean, easy-to-read code. Instead of using punctuation, Python uses English words and has line breaks that make blocks of code readable. Developers only need to look at the code to know what it does.
- Open-source. The entire Python source code is available for download and can be modified in any way.
- A large library. Python’s standard library Is big, which means developers don’t need to write all their basic code from scratch. Various pre-made modules make up a solid foundation to work with.
- Python can be extended. When needed, Python can be written in C++ and other programming languages, and tools can be customized by adding low-level modules to the interpreter.
- Python is portable. As a cross-platform language, Python doesn’t need to be modified to run on other platforms. Using a Python interpreter, the same code can run on any operating system.
While Python can be a great programming language there are gotchas and side effects from using Python that you should definitely be aware of.
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