Python libraries for web applications (Flask and Django)

Here’s an overview of some popular Python libraries for building web applications:

• Flask: A lightweight and flexible web framework. It’s great for building small to mid-sized apps. Some of its main features are as follows:
  • It has a minimal and intuitive core that can be easily extended. This makes it suitable for anyone looking to create web applications.
  • It supports extensions and integrates with many Python libraries. This allows you to easily expand its functionality.
  • It has built-in support for routing, templates, file uploading, and more, which means it’s quite easy to build APIs and render HTML templates.
  • It’s suited for both small and large-scale projects. Many big companies such as Airbnb and Pinterest use Flask.

Let’s look at some situations when we should use Flask:

• Building a small to mid-sized website or web app
  • When we need more flexibility and control than what a microframework provides
  • The need to support extensions and integrations with other libraries
  • When we’re familiar with Python and want a framework that’s easy to get started with
• Django: A high-level Python web framework that encourages rapid development and clean design. It’s suited for large projects and applications. Some of its main features are as follows:
  • It has an object-relational mapper (ORM) that supports multiple databases.
  • It has a permission system and built-in support for users, groups, permissions, and more.
  • It scales well and is used by huge companies such as Instagram, Netflix, Spotify, and others.
  • It has a lot of add-on libraries and integrations available.
  • It enforces a certain structure and best practices. This can be good for newcomers but can also be restrictive.

Let’s look at some situations when we should use Django:

• For building a large and complex website or web application
  • When we need a structured and scalable framework
  • When we require advanced features such as authentication, permissions, administrators, and more out of the box
  • When we want a framework that scales well and can support a lot of traffic and load
  • When we prefer a rigid structure and enforceable best practices

In summary, choose Flask for small to mid-sized web apps and Django for large, complex web applications with advanced features.

If you want to write web applications with beautiful and useful dashboards easily, choose Streamlit. This, among all the tools and solutions we’ve described so far, is an important Python web application framework for several key reasons:

• Convenience and ease of use: Streamlit simplifies building web apps in Python dramatically. It handles complex tasks such as setting up servers, routing, templating, databases, and more under the hood so that developers can focus on their application logic. This makes the development process faster and more accessible.
• Integrated Python support: Streamlit allows you to build entire web applications using only Python code. No additional templates, routing setup, or database configuration is required. Developers can utilize all of Python’s libraries and features, and there is no need to learn additional languages or frameworks.
• Rapid prototyping: Streamlit’s convenient and lightweight nature makes it ideal for quickly building prototypes, dashboards, demos, and minimum viable products. New ideas can be converted into shareable web applications very quickly.
• Reusable components: Streamlit has a library of pre-built reusable frontend and backend components such as dropdowns, file uploaders, search bars, pagination, and more. These help speed up development and ensure a consistent user experience.
• Cloud deployment: Streamlit web apps can be deployed on major cloud providers, including AWS, GCP, Azure, and Docker. This provides scalable infrastructure, global reach, and the flexibility to adapt resource usage as an application grows. Deployment is simplified through seamless integration.
• Interactive dashboards: Streamlit specializes in interactive applications and dashboards. It is used by data scientists and analysts to create insightful exploratory data analysis tools and reports. Users can filter, sort, pivot, search, select features, and examine data from multiple angles.
• Community and support: Streamlit has an active community and team behind it that supports usage questions, contributes to documentation, accepts pull requests, and addresses security issues promptly. The project is also backed by major tech companies and researchers.

In summary, Streamlit provides an easy-to-use, integrated, and scalable framework for building and deploying full web applications in Python. It allows you to convert ideas into apps faster, enables shortcutting low-level setup, facilitates prototyping and experimentation, enables interactive visualizations and dashboards, and supports customization and contribution from an open community. Overall, Streamlit makes web development in Python more accessible, productive, and impactful.

So far, we’ve covered the main Python web application libraries and their specificities. Since we’ll be focusing on Streamlit in this book, let’s see what it’s capable of.