Django is a free framework for Python-based web applications that uses the MVC design pattern.
Django ORM Recipes is a book about working with Django ORM and Django models. Django ORM is one of the key pillars of Django.
Django Rest Framework (DRF) is a library that works with standard Django models to create a flexible and powerful API for a project.
Django CMS is a modern web publishing platform built on Django, a web application framework "for perfectionists with deadlines".
Channels is a project that takes Django and extends it beyond HTTP to handle WebSockets, chat protocols, IoT protocols, and more.
ASGI (Asynchronous Server Gateway Interface) is the spiritual successor to WSGI, designed to provide a standard interface between asynchronous web servers, platforms, and Python applications.
Python Social Auth is an easy-to-configure social authentication/registration mechanism with support for multiple platforms and authentication providers.
The best way to get Django DRY forms. Create reusable programmatic layouts from components with full control over the rendered HTML without writing HTML in templates. All this without breaking the standard Django way of working, so it works great with any other forms application.
Select2 provides a custom select field with support for search, tags, remote datasets, infinite scrolling, and many other commonly used options.
Stores the history of models and allows you to view / cancel changes from the admin panel.
In this article, we'll look at the differences between Django's class-based views (CBV) and function-based views (FBV). We'll compare and contrast and dive into the pros and cons of each approach (along with Django's built-in generic class-based views). By the end, you should have a good understanding of when to use one over the other.
In programming, the term constant refers to names representing values that don’t change during a program’s execution. Constants are a fundamental concept in programming, and Python developers use them in many cases. However, Python doesn’t have a dedicated syntax for defining constants. In practice, Python constants are just variables that never change.
This tutorial looks at how to speed up CPU-bound and IO-bound operations with multiprocessing, threading, and AsyncIO.
Here we'll look at how to use Pyenv to manage and install different versions of Python, and Poetry to manage packages and virtual environments.
In this article, you'll glue everything together as you develop a single project from start to finish. After developing the basic project, you'll: Wire up CI/CD with GitHub Actions, Configure coverage reporting with CodeCov, Publish the package to PyPi and the docs to Read the Docs, Update PyPI and Read the Docs via GitHub Actions
Virtual Environments are isolated Python environments that have their own site-packages. Basically, it means that each virtual environment has its own set of dependencies to third-party packages usually installed from PyPI.
The Python HTTP library requests is probably my favourite HTTP utility in all the languages I program in. It's simple, intuitive and ubiquitous in the Python community. Most of the programs that interface with HTTP use either requests or urllib3 from the standard library.
Decorators are wrappers around Python functions (or classes) that change how these classes work. A decorator abstracts its own functioning as far away as possible. The Decorator notation is designed to be as minimally invasive as possible. A developer can develop his code within his domain as he is used to and only use the decorator to extend the functionality. Because this sounds very abstract, let’s look at some examples.
Python is a very dynamic language by nature. Variables do not need to be declared and can be added as attributes almost everywhere.
In Python, some objects like strs or lists can sliced. For example, you can get the first element of a list or a string.