1. Python Programming Frameworks

Python frameworks are a set of larger entities that provide structure and a group of tools to efficiently build applications. There are plenty of Python frameworks that are helpful for specialized tasks in development. The diverse range of frameworks catering to various domains and use cases makes Python programming more popular. This Python tutorial lists all the popular frameworks of Python that are widely used in various domains and are classified as per their technical usage. Further, we will also get to know why to use frameworks in Python and what is the difference between module, package, library, and framework i.e. organization and structure of code and lastly their types.

1.1. Types of Frameworks

Following is the general classification or types of all the popular frameworks in Python language.

  1. Web Development Frameworks
  2. Data Science and Machine Learning Frameworks
  3. Asynchronous Frameworks
  4. GUI Application Frameworks
  5. Testing Frameworks
  6. API Development Frameworks
  7. Other Specialized Frameworks

A Python Framework Flowchart

Tutorial Contents:

  1. What are Frameworks and their Types?
  2. Why to Use Frameworks in Python?
  3. Code Organization in Python Programming
  4. List of All Frameworks

2. Why to Use Frameworks in Python?

A framework in Python programming is helpful while developing different projects. Basically it aid developers by providing them with pre-coded solutions, so they do not have to develop everything from scratch. Further, the redundant operations in web development are fulfilled with pre-built code.

2.1. Benefits of Using a Python Framework

Using a framework in Python programming have several benefits and features that contribute to streamlined and efficient software development. The key advantages of using a Python framework are listed below:

  • Rapid Project Development
  • Code Reusability and Efficient Operations
  • Project Scalability
  • Consistency and Convention
  • Good Documentation
  • Security Features
  • Maintenance and Updates
  • Abstraction of Complexity
  • Flexibility and Extensibility
  • Testing Support
  • Performance Optimization
  • Open-source

3. Python Code Organization or Structure

In Python programming, we use various concepts to organize and structure our code. Usually, we do this with the help of different files and folders. There are 4 main concepts of code organization in Python.

  1. Module
  2. Library
  3. Package
  4. Framework

3.1. Python Module

A module in Python is a single file containing reusable code that may comprise functions, classes, and variables. Hence, related functionality is organized or grouped in a Python module, which is also easy to maintain and reuse. Example: my_module.py

3.2. Python Library

A library in Python is a collection of modules that consists of pre-written code, which can be reused in our projects. Usually, a Python library contains a set of functionalities that we can use in different projects. Example: math library

3.3. Python Package

A package in Python is a group of related modules organized in a directory or a folder. There may also be sub-packages in the directory, but the most important file required for a directory to be called a package is __init__.py. The __init__.py file is a must in every Python package. Example: requests package

3.4. Python Framework

The largest entity in Python code organization is the framework that contains structure and a set of tools, that helps in building applications. A Python framework help developers with guidelines and foundations to build applications efficiently. There are modules, packages, and libraries within a Python programming framework. Example: django framework

4. Popular Python Frameworks List

The below list contains various popular Python frameworks and is divided into various types or categories. Each category framework contains a specialized set of functionalities to work within that Python domain.

4.1. Web Development Frameworks

  • Django
  • Pyramid
  • TurboGears
  • Flask
  • Bottle
  • CherryPy
  • FastAPI
  • Tornado
  • Quart

4.2. Data Science and Machine Learning Frameworks

  • Pandas
  • Dask
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • Plotly

4.3. Asynchronous Frameworks

  • FastAPI
  • Tornado
  • Quart
  • asyncio(built-in library)
  • aiohttp(library)

4.4. GUI Application Frameworks

  • Tkinter (built-in)
  • PyQt
  • Kivy
  • Pygame

4.5. Testing Frameworks

  • unittest (built-in)
  • pytest
  • Behave
  • Pytest-BDD

4.6. API Development Frameworks

  • FastAPI
  • Flask-RESTful
  • Django REST

4.7. Other Specialized Frameworks

  • Scrapy
  • Beautiful Soup
  • Twisted
  • Scapy
  • Selenium
  • Fabric
  • Zappa
  • KivyMD
  • BeeWare
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