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Are you ready to start exploring the world of API development with Django? In this tutorial, we will begin an exploration of how to leverage Django together with the Django REST framework, a robust and versatile framework for creating efficient and scalable APIs. This guide is designed to provide you with a clear and structured approach to Django REST API, ensuring that both beginners and experienced developers can navigate and utilize this powerful tool with ease.

Performance metrics in computer science are typically based on time and space complexity. Time complexity deals with the application's execution time, while space complexity pertains to the memory it consumes during execution. For Django, performance relates to the speed at which a server processes user requests and returns results. The quicker the response, the better the user experience.

The Python web framework Django is an excellent choice for building performant web apps. Disqus, Pinterest, and Instagram are all built on a Django foundation. Django offers simplicity, flexibility, reliability, scalability, and all the tools you need to build a web app from admin to authentication to ORM. But this “batteries included” approach has disadvantages. It can hide a lot of the true complexity of an app from developers. Django ORM is a great example.

Do you need complete control over your production environment? If so, you might want to skip the Platform as a Service (PaaS) offerings and deploy to your own server instead. This article describes deploying a Django application to an Ubuntu server at Linode.

If you've ever wondered if there is a better way to build and consume APIs, you should check out GraphQL—it's better than REST in many cases. This article discusses how to work with GraphQL in Django.

Docker offers many benefits, but it can be tricky to get started—especially when you need to deploy several components. In this article, Muhammed Ali shows how to use Docker and Docker Compose to containerize an application built with Django and React.

Job queues are a powerful tool for processing background work in your Django applications. This article teaches about job queues and workers—and how to implement them with Django Q and Celery.

Python is insanely popular among machine learning enthusiasts these days. Hence, anyone developing a machine learning model normally turns to Python. The real challenge arises at the deployment stage because you can use many frameworks. Figuring out which Python framework to use may add to the confusion. This post discusses two popular machine learning frameworks, Flask and Django. We’ll also compare them side by side, so that you can make the right choice.

Automating your test suite helps get your code to production faster. In this tutorial, you'll learn how to write unit tests for Django and run them with GitHub Actions.

Learn how to create and manage user permissions in your Django apps.