Would you like to understand the main limitations of the Streamlit Python library for building data web apps, before picking it up for a project?

In this video, I'll cruise through 5 obstacles that regularly come back within the community and multiple ways/hacks/techniques to work around those limits. You'll find out:
how to hack markdown and iframe to style your app
if it's possible to alter the Streamlit rerun event loop and Tornado Backend server
thoughts to build a desktop app from your Streamlit app
where is the ipywidgets integration into Streamlit at

After you watch me show you all those advanced techniques, you should have a good idea of Streamlit's limits and ways to go around Streamlit's "run from top to bottom philosophy" to meet your ends.

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Links
Is Streamlit useful: https://discuss.streamlit.io/t/isstr...
Partial reruns: https://github.com/streamlit/streamli...
Using PyInstaller for Streamlit: https://discuss.streamlit.io/t/using...
ipywidgets in Streamlit?: https://discuss.streamlit.io/t/ipywid...

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00:00 Intro
00:45 Fixed Branding
04:38 Looper
08:31 Hidden Tornado Server
12:17 Not an Executable
17:02 Refuse ipywidgets
20:30 Creativity in Constraints

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"Instead of freaking out about these constraints, embrace them. Let them guide you. Constraints drive innovation and force focus. Instead of trying to remove them, use them to your advantage." 37Signals, Getting Real: The Smarter, Faster, Easier Way to Build a Web Application

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#streamlit #python #datascience #dataapps