12-04, 21:00–21:30 (UTC), Data/ Data Science Track
Shiny for Python is an efficient and reactive application framework that will be able to grow with your application needs.
As your shiny application grows, you may find yourself needing more custom behaviors and potentially reusing and sharing
your custom behaviors with others.
You may also find your existing applications to be overly complex and had to see the overall structure of the application.
Here are some tips on writing better Shiny Applications and leveling up your code.
As data scientists, we don't typically come from software engineering where we need to think about the
robustness of our code.
When we have to create larger and more complex data applications that will be used and grows over time,
the maintainability of the codebase becomes more important.
This talk will talk about 5 tips when creating shiny for python applications,
and also introduce shiny modules as a way to make your code easier to reason with and also a way to make custom shiny functions more potable.
Outline:
- 0-5: Shiny for Python introduction
- 5-10: Primer about shiny reactivity
- 10-12: Example application and problem I tried to solve
- 12-27: Tips for your shiny apps with examples
- 27-30: Putting it all together / Conclusion
No previous knowledge expected
Daniel is a Lecturer at the University of British Columbia and Data Science Educator at Posit, PBC. He believes that data science artifacts are useful when the information can be shared across stakeholders, and enjoys learning and teaching the tools that enable deploying and sharing these data products.