25 Python Frameworks available to master by 2023 (r)

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In this piece this article, we'll go over the most well-known and effective Python frameworks that you should get to know in 2023.

What is the definition of a Python Framework?

Frameworks are designed to simplify the process of developing software by offering a general guideline on how we should build software while abstracting out many of the more difficult or repetitive jobs. The framework lets you concentrate on creating unique and customized application logic, rather than being forced to recreate the wheel.

After having the concept of frameworks crystalized Let's look at some of the varieties of Python frameworks.

Different Python Frameworks and Types

Python has a variety of frameworks for a variety of kinds of development. Let's to look at some of the frameworks.

Full-Stack Framework

Microframework

It's made to be light and easy to extend, making it a good option for smaller projects, or developers looking for more control over their code.

Asynchronous Frameworks

An asynchronous framework was designed to handle concurrency and parallelism, allowing programmers to develop applications that are able to perform a variety of tasks at once.

Python Package Manager (pip) Python Package Manager (pip)

A package is an independent and reuseable collection of programs that provide particular functions to accomplish a common task (web development, authentication, GUI). The frameworks we're about to see next are part of the Python standard library, meaning they're available as the pip package.

Install any Python package available on the Python Package Index (PyPI) by using the following commands:

pip install package-name

There is also the option of installing packages using a text file- for example, an requirements file to be used in a project -- with the below command.

pip install -r requirements.txt

Python Framework vs Python Library

A Python library is a collection of methods and functions that are used to perform specific tasks, such as parsing data or making a random number. The library's functions are generally limited in scope and is designed to function to aid in the development of the context of a bigger program.

It doesn't mean that you cannot extend the functionality of the framework by integrating different packages. These are often called plugins.

The reason to use the Python Framework?

There are several benefits to making use of the Python framework to develop applications. We'll look at a few of them:

• Frameworks help code organize: A well-designed framework helps to ensure that code is well-organized and maintainable. This makes it easy to read and change the source code later on.
• Frameworks are able to boost productivity: By providing pre-built components and tools and following industry-wide standards, frameworks allow developers to focus only on the distinctive elements of their application instead of focusing working on the basics.

Once you've got sufficient theoretical knowledge Let's look at the most popular 25 Python frameworks in 2023.

The top 25 Python frameworks to 2023

Below are the 25 most well-known Python frameworks, arranged in sections based on functionality.

Python Microframeworks

These are microframeworks that are light that are ideal for those who want to create a simple app quick or require minimal use of memory on your server.

Flask

Flask is a micro-framework that's lightweight and can be that allows you to build quick and simple web-based applications. It includes support for Jinja templates (a means of reuse HTML code) and request handling and the ability to signal applications.

In reality, it takes just 6 lines to start a Flask app:

From flask, import app from Flask = Flask(__name__)
 @app.route ("/")
 def hello_world() returning "Hello "

  Features:  

• Easy to carry and light weight
• High Flexibility
• It is built on top of Python standard library
• Views and routing support (controllers)
• Support for templating in Jinja
• Great documentation as well as a community
• Cookies handling support and authenticating the user

Bottle

Do you want to develop light web applications that have no other dependents? Bottle is a small Python microframework created to create small or medium-sized web apps. It doesn't include any external dependencies aside from the Python standard library,

Bottle is built upon the standard WSGI (Web Server Gateway Interface) standard. It is compatible with the majority of web-based servers as well as Python versions.

  Features:  

• Simple to master and easy to use
• Template system built-in
• Ideal for the development of APIs and web-based applications for small websites.
• Very little memory use
• forms, HTTP and routing provide support

CherryPy

The program has been in use since 2002 and has been widely adopted within production environments for a diverse range of websites, from those with basic functionality, to those that demand complex features. For example, both giant software companies Netflix and Hulu use CherryPy to form part of their infrastructure.

  Features:  

• Mature framework
• Handle HTTP requests
• Flexible and extensible plugin system
• Backend-only framework
• Object-oriented development
• Stable API

Python Web Frameworks

The following are comprehensive web frameworks with components for common web development tasks like database access, form handling as well as security. In contrast to microframeworks, Python web frameworks include everything you need right out of the box.

Django

Django is one of the most loved and well-known web frameworks in the Python ecosystem. It's a solid full stack framework with everything needed to make an entire web-based application.

Django uses the Model-view template (MVT) structure and has integrated features like managing databases, templating and administration panels, as well as views and signals (to handle the logic of your database backend). To master Django is advisable you have strong fundamentals of Python Object-oriented programming, as well as functional programming.

  Features:  

• A very secure framework
• Built-in admin panel
• Dynamically displays HTML
• Object-relational-mapper (ORM), lay out the database with Python classes
• Database querying with Python API
• Fantastic CLI tools
• Wide variety of extensions

FastAPI

FastAPI is a contemporary web framework that does something amazing to build APIs.

In the years since its launch in 2018it has swiftly gained popularity due to its great speed and ease of use. As per PyPi Stats, FastAPI has an impressive number of 9 million monthly downloads and is higher than even fully-stack frameworks, like Django.

  Features:  

• Uses open standards to define APIs including OpenAPI as well as JSON schema.
• Data validation and Serialization
• Automated documentation of your API
• Modern web framework
• Based on Python 3.6+ features like type declarations
• Typ validation with Pydantic thanks to Pydantic
• Support for synchronous programming

Pyramid

Pyramid is a open and extensible web framework that's well-suited for both large and small-scale applications. It's a great compromise between an ad-hoc framework like Flask and a fully-stack framework like Django.

Pyramid is packed with the common features you would need when creating the Python web-based app, however it allows you to focus on the specific components needed for your application.

  Features:  

• Lightweight and flexible
• Assistance for using the Mako template language
• Easy authentication and authorization implementation
• Request and response objects that are WSGI-compliant
• Simple testing and debugging with integrated testing tools and an interactive debugger
• Third-party libraries and plugins with easy integration

Tornado

Tornado is a free and open-source synchronous internet framework and networking library that allows web developers to build applications with Python. The original source code was created by FriendFeed which was a social media-based aggregator, which was later acquired from Facebook. It's now widely used in a variety of applications, including web services such as real-time analytics, other high-concurrency applications.

Tornado has been designed to handle many simultaneous connections. This makes it suitable for applications that require a long-lived connection to each user (e.g. chat applications bots, chat apps, or web scrapers).

  Features:  

• Scalable (can handle thousands of connected connections)
• Custom tornado templates
• Built-in user authentication
• WebSockets and Long-Polling capabilities
• Options for third party authentication
• Internationalization support

GUI Frameworks

The following Python graphic user interface (GUI) frameworks offer various tools and capabilities for the creation of desktop-based applications. They also provide the ability to support event-driven programming widgets, graphics, and widgets.

PyQt

PyQt is among the most well-known sets of Python bindings to the Qt application framework that is cross-platform. This framework perfectly combines the ease of Python as a general-purpose programming language with the power of the Qt application framework built in C++.

Be aware that PyQt has two license choices:

• GPLv3: It is free to download and use However, any person you distribute it to may redistribute it (probably without cost)
• Commercial It lets you make your code secret so that it's possible to make money from your creation, however you'll be required to purchase a licence for every developer who uses it (currently $550 for every developer)

  Features:  

• Supported by Riverbank
• Event-driven programming for handling user interactions
• Integration with frameworks and libraries
• Support for localization and internationalization
• Access to a large number of functions from the Qt library
• Support for multimedia, WebKit and WebEngine, database integration
• Cross-platform compatibility Windows, Linux, and macOS

Tkinter

Tkinter is part of the Python GUI framework. It's part of the standard library and you don't need to install any other dependencies.

Like PyQt (which utilizes Qt), Tkinter uses the graphical user interface toolkit called Tk. Tk also comes with a collection of instruments for the creation of different widgets, such as labels, buttons, text boxes, and menus.

Overall, Tkinter is a great way to quickly make tiny GUI programs, but due to its outdated look and interface, it could be better off choosing a beefier framework for bigger project.

  Features:  

• The widget is supported for the most common widgets in GUI applications.
• Event-driven programming for handling user interactions
• Inbuilt support for images and colors
• Compatibility across platforms with Windows, Linux, and macOS
• Part of the standard library
• Lightweight

Kivy

Kivy is an open source framework to create cross-platform mobile and desktop applications with Python. It's well suited for creating visual and interactive apps, like games, multi-touch apps, as well as its primary selling point is one single codebase for every major platform (Windows, Linux, macOS, iOS, and Android).

Unlike PyQt, Kivy is free and open-source, isn't an extension of an external UI library, and comes under MIT license. This means that you can ship and monetize your Kivy applications.

  Features:  

• Toolkit open-source for the creation of multi-touch apps
• Assistance for a variety of platforms that include desktops and mobiles
• Support built-in for graphics, animations, and UI elements
• Integrates with other libraries and frameworks

PySide

Pyside is a toolkit that's open source that allows you to build multi-platform programs. It is based upon PyQt which uses the Qt library (similar to the PyQt library) and allows access to a wide range of features.

Pyside is also able to support different input techniques and event-driven programming and includes integrated support for graphics and multimedia.

Because it's licensed under LGPL you can develop proprietary software much more quickly. It's backed by the Qt firm and works with Linux, macOS, and Windows.

  Features:  

• LGPL licensed
• Backed by the Qt Company
• The application supports the display of videos, images, and other media types in your application
• Three options to work in conjunction with Qt4 and Qt6.

PySimpleGUI

PySimpleGUI is gaining traction among the Python community for its simple and simple API.

It's an excellent choice to build simple, easy-to-use graphic user interfaces using Python and lets you include a GUI to your already working scripts very effortlessly. PySimpleGUI wraps the capabilities of four different GUI libraries, PySide, Tkinter, wxPython and Remi.

  Features:  

• Great documentation
• Easy to master and easy to make use of
• Full set of ready-to-use widgets
• Assistance for Python 3.4+

Python Machine Learning Frameworks

scikit-learn

scikit-learn is the most well-known machine learning library that is utilized in both the tech industry and also in the academic world.

It offers efficient tools to perform basic ML tasks. Additionally, it's constructed on top of scientific libraries like Numpy or SciPy.

  Features:  

• Free and open source
• Highly efficient tools for data mining and analysis of data
• Offers a variety of methods for classification as well as regression, clustering and dimension reduction
• A vibrant community, and active development that is an extensive documentation of the API as well as tutorials

TensorFlow

TensorFlow is a free-of-cost platform that allows you to build and deploy model-based machine learning. Developed by the Google Brain team in 2015, it's an extremely robust library that can be used to deep-learning, offering a wide range of tools for creating and training neural networks.

TensorFlow can be used for a variety of tasks, such as the processing of images and languages, speech recognition, as well as predictive analytics.

  Features:  

• Tools to build neural networks
• The application supports mobile and web deployments using TensorFlow.js as well as TensorFlow Lite
• Tools to visualize and troubleshoot Tools for debugging and visualization, such as TensorBoard
• It is available in Python, C, and C++

PyTorch

Together with TensorFlow, PyTorch (developed by Facebook's AI research group) is one of the most popular tools used in creating deep-learning models. It can be used for various tasks, like computer vision as well as natural language processing and generative models.

  Features:  

• Extensive documentation and a large group of developers
• Simple to grasp, using easy-to-use tools
• A more Pythonic style than the other frameworks.
• Open-source and free

Keras

Keras is a high-level deep-learning framework capable of running over TensorFlow, Theano, and CNTK. It was developed by Francois Chollet back in 2015. It is designed to provide users with a user-friendly and simple interface for creating and implementing deep-learning models.

Keras gives you the foundational elements for building machine learning models quickly. That's why it's widely used in data science competitions like Kaggle's.

  Features:  

• Simple and user-friendly interface for creating and implementing deep-learning models
• Assistance in building and training neural networks by using a broad array of different architectures and optimizers
• It includes a robust ecosystem of libraries and programs to deep learn, such as Keras Tuner as well as KerasRL
• The ability to run on various backends like TensorFlow, CNTK, or Theano
• Help for distributed computing
• Built-in support for model visualization as well as troubleshooting

Python Scientific Computing Frameworks

The frameworks allow students, scientists, as well as researchers complete their tasks without focusing on the details at the bottom of the calculation.

Scientific computing frameworks often include functions for linear algebra, optimization as well as interpolation, integration and many other routine tasks of the field of scientific computing.

NumPy

NumPy is a computational science library that is designed to handle multiple-dimensional arrays and matrices to handle numbers. Also, it offers a vast variety of mathematical functions that can use the arrays.

NumPy is the foundation block of many different Python libraries and frameworks used in data science, including SciPy, Pandas, scikit-learn as well as Tensorflow.

  Features:  

• Handles multi-dimensional arrays with ease
• The support for mathematical tools, including linear algebra routines, Fourier transforms, and random generators of numbers
• A vast library of mathematical operations
• Speedier than the standard Python operation (some implementations are built in C)

SciPy

SciPy offers an array of algorithms and functions that are built upon NumPy. It helps to perform common scientific and engineering tasks like optimizing, signal processing, linear algebra, integration and much more.

  Features:  

• Open-source and free
• Formulates algorithms and functions to aid in Engineering and scientific tasks
• Active community as well as great documentation

Pandas

Pandas is an extremely flexible and extensible open-source library that can be used for analysis of data in Python. It offers high-performance data structures (i.e. the well-known DataFrame) as well as analytics tools which make it simple to work with structured data.

  Features:  

• High-performance data structures, such as DataFrame and Series
• Assistance for writing and reading data into and out of various formats, such as CSV, Excel, and SQL
• Allows analysis of data from real-time
• Based on software for data science such as scikit-learn

Matplotlib

Matplotlib is an extensively used tool for data visualization in Python. It provides an object-oriented API that allows embedding plots in applications.

It's made to be incredibly customizable, and it provides many options for creating plots, charts as well as visualizations. Because of this, Matplotib is commonly used in machine learning, as well as research computing.

  Features:  

• Support for 2D and 3D plotting
• Can prompt interactive figures
• Usually, Jupyter notebooks are embedded as well as GUI applications
• Extensive documentation and a vivid community

Python Testing Frameworks

If you're working with test-driven development (TDD) The following frameworks are a must in your process.

Pytest

Pytest is a widely adopted testing framework designed to be easy to work with and expand. It helps you to write elegant tests in both tiny and complicated Python codebases.

  Features:  

• Simple to learn and utilize
• Over 800 plugins are in use
• Sets a workflow to create unit tests.
• Detailed error information

Unittest

Unittest is a built-in Python testing framework that is built on the xUnit test practices. It is a Pythonic and object-oriented approach to developing automated test cases and suits of tests.

  Features:  

• Simple and easy-to-use interface to write and run tests
• Tests for parameterization, fixtures, and markers
• Works in conjunction with pytest or nose
• It is part of the Python Standard Library

nose2

nose2 is the successor to the nose testing framework, with the major difference is that it supports the most recent Python versions.

nose2 expands unittest's built-in library and provides a more powerful and flexible method of writing and running tests. It's an extensible tool which means you're able to utilize various third-party and built-in plugins to your advantage.

  Features:  

• Extensible with plugins
• Based on the built-in unittest
• Python 3 Support Python 3

Asynchronous Frameworks

This kind of Python frameworks provide a set of tools and libraries that enable developers to write asynchronous code with greater ease and efficient manner.

asyncio

asyncio is at the heart of a number of other frameworks for asynchronous communication. It allows developers to write concurrent code using the async/await syntax. it is designed to manage hundreds of simultaneous connections.

asyncio provides a single API for multiple transport protocols such as TCP, UDP, SSL/TLS, and subprocess communication.

  Features:  

• Built into Python
• Basis for many other Python frameworks
• Included APIs for controlling subprocesses and to perform networking I/O
• Great documentation
• Part of Python standard library

Aiohttp

Aiohttp is an Asynchronous HTTP client/server platform built on top of the asyncio program. It offers a straightforward and user-friendly interface to manage HTTP request and response, as well as support for sessions, middleware and other web development tools.

  Features:  

• The HTTP protocol is both server-side and client-side.
• Handles a high number of simultaneous connections
• Lets you build Web applications that are synchronous

Summary

Python frameworks may help improve code organization, boost efficiency, and also make the software development process simpler and faster.

If you're a Python developer, your job isn't to make yourself expert in all of the Python frameworks we presented in the previous article, but pick the frameworks that interest you the most and create projects using them that really excite you, and master those specific frameworks in full.

What Python frameworks have worked with? What's your favorite? Share your thoughts in the comments section below.

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