Shiny for Python?
HTML-код
- Опубликовано: 12 сен 2024
- In this video, we will take a look at the new Shiny library that had extended from the R programming language into the Python ecosystem.
Shiny is a framework for creating web applications that had historically been available in the R ecosystem and only recently is now released to Python.
📖 Read about Shiny for Python shiny.rstudio....
🕹️ App Examples shinylive.io/p...
-----
⭕ Support my work:
🌟 Subscribe to this channel (Coding Professor) / @codingprofessor
🌟 Subscribe to my other channel Data Professor www.youtube.co...
🌟 Join the Newsletter of Data Professor newsletter.data...
🌟 Buy me a coffee www.buymeacoff...
⭕ Recommended Books:
🌟kit.co/datapro...
✅ Python Basics: A Practical Introduction to Python 3 amzn.to/3awdWgm
✅ Learn Python Programming (The no-nonsense, beginner's guide) amzn.to/2RFpSpn
✅ Learn to Program with Minecraft amzn.to/3x2MujZ
✅ Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners amzn.to/2QzkyDs
Python icon in thumbnail image created by Flat Icon www.flaticon.c...
⭕ Disclaimer:
Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel's contents.
Thank you very much for this one. I'm looking forward to a video in which you demonstrate how to make the most of it.
Thanks. More examples about open source low code products is appreciated ;)
Shinny+Python is super fast, even faster than streamlit. Thanks
Streamlit is slow because it has to reload the whole Python script or module supplied to it for any user interaction. I use streamlit-folium for visualizing 50000 geolocations with user to pull out sensor measurement time series at each location. The constant rerunning for 10 seconds after zooming or dragging action is killing me already at testing.
Hi Prof! Thank you for yet another insightful video, really appreciate you sharing your knowledge! This might be an amateur question but if possible please direct me. I want to ask more of a statistics-based question in Data Science... when building models and dealing with different data types and outputs I think it will be extremely valuable to go through feature selection methods in Python, using different Encoders as well as the statistical test that is used for each type. I have found this quite difficult to build intuition with and would appreciate any insights you may have. Thank you once again!
One thing to consider and why shiny for R is imho most powerful. It allows for asynchronous programming/subprocessing, which Python dash or streamlit do not. At least streamlit not compatible with asyncio. not that I know of. Not that easy like shiny + CallR. Might be wrong, but on top R vectorized operations do not need any additional library language like Pandas / Numpy which in fact is not native Python language. You need to apply vectorize in numpy to make Python work vectorized. On the contrary, vectorization is just R DNA and all goes so smooth with Shiny in R. Might be wrong…
If possible would you give a short tutorial on shap or lime for deep learning models?
Hi. Thank so much for the video. I just have a doubt...how can i display de web app if i am in Jupyter Notebook? Thank you again for your response.
Can you make the example how to upload file to GDrive using colab. But we can change name and convert to zip or another extension file.
Access denied with the following error:
Cannot retrieve the public link of the file, you may need to change the permission to ' Anyone with the link's, or have had many accesses
How to resolve this?