Python is the go-to programming language for Data Science. Besides its inherent simplicity, what makes Python most appealing is that it is backed by a wide range of Python frameworks.
Python frameworks offer a well-defined structure for app development. Since they can automate the implementation of some standard solutions, they not only reduce the development time significantly but also allow Developers to focus on the core application logic instead of routine elements. Long story short – they make the job of Developers much easier and make Python one of the best programming language.
Types of Python Framework
Essentially, Python frameworks are categorized into three types:
1. Full-Stack Framework
A full-stack framework, also known as enterprise framework, is the one-stop solution for all development needs. These have built-in libraries configured to work seamlessly together. They support the development of databases, frontend interfaces, and backend services.
2. Microframework
Microframeworks are lightweight, minimalistic web application frameworks that have limited functionalities and features. Usually, microframeworks offer only those components that are required for building an application. They lack many additional functionalities and features like database abstraction layer, form validation, web template engine, authentication functionality, authorization, input validation, and input sanitation.
3. Asynchronous Framework
The asynchronous framework is the latest to join the Python framework bandwagon. It is a unique microframework that lets Developers handle and manage large sets of concurrent connections. These frameworks feed on Python’s Asyncio library.
Things to consider while choosing a Python framework
Python has consequently ranked as the number one programming language loved by Data Scientists and Developers alike. There are multiple reasons which make Python the best programming language for Data Science. So, rest assured, there’s no shortage of Python frameworks. However, the abundance of Python frameworks might also become overwhelming while choosing the right framework for your application.
Here are two things you should consider while deciding on a Python framework:
- First, evaluate the size and complexity of your project. If you have to develop a large system packed with features and requirements, a full-stack framework will be the right choice. On the contrary, if the project at hand is small and straightforward, you can work with a microframework.
- The second aspect to consider is whether or not the framework allows the scope for scaling both vertically and horizontally. This is particularly necessary when building a project that will run on multiple servers, handle a huge traffic load, and support the addition of new features for functionality optimization.
Top 7 Python Frameworks
1. Django
Django is an open-source, full-stack Python framework. It follows the DRY (Don’t Repeat Yourself) principle. Django comes equipped with a vast line of ready-to-use libraries. Some of its most exceptional features are authentication, URL routing, template engine, object-relational mapper (ORM), and database schema migrations. Together, these make Django highly scalable, fast, and versatile.
Django is compatible with a host of databases including PostgreSQL, MySQL, SQLite, and Oracle. Since it uses ORM to map objects to database tables, the same code works with different databases. You can even shift from one database to another without any difficulty. Django allows you to develop any kind of application you desire – from large web applications to small-scale projects.
2. Pyramid
Another open-source Python framework on our list is Pyramid. It runs on Python 3 and aims to accomplish as much as possible with minimal complexity. Perhaps the best feature of Pyramid is its ability to run well with both small and large applications. Some of the key highlights of this framework include Routes, HTML form validation and generation, text-based templating, URL mapping based on Routes configuration via WebHelpers, and URL dispatch.
Pyramid boasts of an active community that’s continually contributing to enrich the framework.
3. TurboGears
TurboGears is an open-source, data-driven, full-stack Python framework. It incorporates some of the best components of other Python frameworks and comes with many useful libraries. It allows developers to build data-driven web applications very fast.
TurboGears has a user-friendly templating engine and a robust ORM. In addition to these, it allows plenty of scope for flexibility. It uses components such as Genshi, Repoze, SQLAlchemy, and WebOb to facilitate easy development of apps that require database connectivity.
4. Web2py
Web2py is a highly scalable, open-source full-stack Python framework. It comes with its individual web-based IDE that includes a code editor, debugger, and a one-click deployment feature.
Web2py is a potent data handling tool. Although it allows developers to design and build dynamic web content in Python, it has no support for Python. However, it can run seamlessly on any web hosting platform that provides support either for Python or Java & Python. Also, it has built-in data security for preventing common vulnerabilities like cross-site scripting, injection flaws, and malicious file execution.
5. CherryPy
CherryPy is one of the oldest open-source, object-oriented Python microframeworks. Following a minimalistic approach, CherryPy is designed for extensibility. It includes mechanisms for hook points and extensions. Moreover, the “cherry” on top is that any CherryPy-based web application is a standalone Python application having its unique embedded multi-threaded web server.
Despite being a microframework, CherryPy isn’t extremely limiting in functionality. It lets you work with any technology of your choice for templating, data access, etc. Plus, it can handle sessions, statics, cookies, file uploads, and everything else that a web framework can.
6. Flask
Flask is a Python microframework available under the BSD license. It drew inspiration from the Sinatra Ruby framework. Flask requires Jinja2 template and Werkzeug WSGI toolkit to run. It has a lightweight and modular design that makes it easily adaptable to a wide range of development needs.
With Flask, you can build a solid web application foundation that can scale to use any kind of extensions. It includes a built-in development server and fast debugger, has integrated support for unit testing, and allows for RESTful request dispatching. Flask is compatible with Google App Engine.
7. Sanic
Sanic is an Asynchronous framework built on top of uvloop. It is a simple Python framework developed explicitly for offering fast HTTP responses via asynchronous request handling. Since Sanic supports asynchronous request handlers, it is compatible with Python 3.5’s ‘Async’ and ‘Await’ functions. This helps to enhance its speed further.
Sanic is one of the most preferred asynchronous frameworks by developers since it can read and write cookies; allows different types of logging, has plugin support, and supports blueprints for sub-routing within an application, among other things.
Conclusion
As you can see, each Python framework comes with its own distinct features and capabilities to cater to varying app development needs. The key is to identify which framework will best suit your project, and that shouldn’t be tough – we’ve told you how to do that as well!
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Rohit Sharma
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