Python Frameworks: What Are The Top 5 Frameworks in Python


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What do Python programmers need to do to make their coding process easier? The answer is the structures or frameworks. By automating the implementation of redundant tasks, frameworks reduce development time and allow developers to focus heavily on application logic rather than the elements.

As one of the leading programming languages, there is no shortage of Python frameworks. Different frameworks have their own set of advantages and problems. Therefore, the selection process is based on project requirements and the developer’s preferences.

There are primarily three variants of Python frameworks, full-stack framework, micro-framework, and asynchronous framework. Before we discuss the best Python frameworks to use in 2022, let’s first take a brief look at the various types of Python frameworks.

Types of Python Frameworks

  • Full-Stack Framework

Such frameworks are a complete solution for all developer requirements. Form builders, form validation, and template layouts are generally available with a typical full-stack framework.

  • Microframework

These are lightweight frameworks that do not offer additional functionality and features, such as database abstraction layer, module validation, and specific tools and libraries. Developers using microframeworks have to manually add a lot of code and additional requirements.

  • Asynchronous Framework

Recently it has gained popularity, an asynchronous framework is a microframework that allows you to manage a large set of simultaneous connections. Typically, an asynchronous framework built for Python uses the programming language’s asynchronous library.

Top 5 Python Frameworks:


Asynchronous Framework AIOHTTP is a Python framework that relies heavily on the features of Python 3.5+, such as async and awaits. The Python framework uses Python’s asyncio library and is, therefore, an asynchronous framework. In addition to being a web server framework, AIOHTTP can also act as a client framework. Provides a request object and a router to allow query redirection to functions developed to handle them.


  • Lets you build views effectively
  • Middleware support
  • Connectable routing
  • Signals
  • It supports both WebSocket client and WebSocket server without Callback Hell

  • Bottle

Microframework Bottle creates a single source file for each developed application using it. It is one of the most prominent and widely used Python-based web frameworks. Microframework for Python was developed for building APIs. Aside from the standard Python library, Bottle has no dependencies required for building small web applications. One of the most important benefits of using a Bottle is that it allows developers to work closer to the hardware. In addition to creating simplistic apps for personal use, Bottle is well suited for learning web framework organization and prototyping.


  • Adapter support for third-party model engines and WSGI / HTTP servers
  • Allows easy access by cookies, data, file uploads and other HTTP-related metadata
  • Integrated HTTP server
  • Plug-in support for different databases
  • Provides request submission paths with support for URL parameters
  • CherryPy

Microframework CherryPy is a popular open-source object-oriented Python framework that follows a minimalist approach. The micro-framework is one of the oldest Python frameworks launched in June 2002.

Any CherryPy-based web application is a standalone Python application with its own built-in multi-threaded web server and runs on any operating system with Python support. Such an app can be deployed anywhere a Python app can run.

An Apache server is not required to run apps developed using CherryPy. The micro-framework allows developers to use any technology for data access, modelling, etc.


  • Several ready-to-use tools for authentication, caching, encryption, sessions, static content and more
  • A flexible integrated plugin system
  • Web server with HTTP / 1.1 compliant WSGI thread pool
  • Integrated support for coverage, profiling and testing
  • It offers simplicity to running multiple HTTP servers at the same time
  • Powerful configuration system
  • It works on Android
  • CubicWeb

Full-stack framework Developed and maintained by Logilab, CubicWeb is a free, semantic, open-source and Python-based web framework. Based on the data model, CubicWeb requires having the definition to develop a functional application.

Unlike other Python frameworks that use separate models, CubicWeb uses a cube model. Multiple cubes merge to instantiate with the help of a database, web server, and some configuration files.


  • Offers OWL (Web Ontology Language) and RDF (Resource Description Framework).
  • Reusable components
  • Security workflows
  • Simplify data-related queries with Relational Query Language (RQL) embedding.
  • Support for multiple databases

  • Dash

Microframework Dash is a Python-based open-source framework for building analytical web applications. It is an ideal Python framework for data scientists who are not very interested in the mechanics of web development.

Applications developed with Dash are web servers that run Flask and connect with JSON packets through HTTP requests. Their frontend renders components using ReactJS. Flask’s plug-ins are used to extend Dash’s capabilities.

Because the Dash apps appear in the web browser and can be deployed on servers, they are cross-platform and mobile-ready. Dash developers can retrieve the underlying Flask instance and all its configurable features.


  • Dash apps require very little standard code to get started
  • Error Handling (Dash Deployment Server)
  • A high degree of customization
  • LDAP integration (Dash Deployment Server)
  • Plug-in support
  • Simple interface for linking UI controls, including drop-down menus, charts, and sliders
  • URL routing (Dash Deployment Server)

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