Conclusion - a New Superpower

Six months ago I was amazed by how quick and easy it was to write interactive data web apps using Streamlit and without too much HTML knowledge. The advent of Streamlit Components blurs the line between the Python Data Science ecosystem and the modern frontend ecosystem even more, as it enables us to effortlessly:

• Render arbitrary frontend code inside an iFrame in your Streamlit app. This comes with support for hot-reloading during development, just like in modern frontend development;
• Communicate values back & forth between a Python script and a Javascript component, pushing for powerful data-driven web applications that combine data manipulation and machine learning in Python with custom interactive visualization and web-specific features in Javascript; and,
• Easily share those newly created components so that other Streamlit users can use them with a single Python call in their own apps!

Streamlit has just given a new superpower to the data science community and you can greatly contribute to it by creating and sharing your own interactive widget! Don’t hesitate to check the Featured Custom components gallery and customize your app even further.