Build Awesome Web Apps & Dashboards with Python! (Full Shiny for Python Course)

In this course we learn how to build, share, & deploy web apps & dashboards using the Shiny for Python framework. We cover the basics up to advanced concepts.

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Video timeline!
0:00 - About Shiny for Python & Course Overview

Part 1: How to Build, Deploy, & Share a Python Application in 20 minutes!
2:14 - Intro to Shiny & Gallery Examples
3:24 - Getting Started with the Shinylive Playground
4:25 - Building a custom visualization with Shinylive
6:59 - Easily sharing the code/application for a Shinylive app
9:34 - Building a Shiny Express App locally (VSCode)
11:21 - How to run app if you're not using VSCode
11:58 - Further customization of our app (adding title, using CSV data, dynamic input)
18:56 - Deploying our Shiny app to the web

Part 2: How to make Interactive Python Dashboards! (Reactivity in Shiny)
23:50 - Part 2 Overview
24:53 - Getting Started with Code (Part 2)
26:00 - Adding Shiny Components (Inputs, Outputs, & Display Messages)
27:13 - Creating an Additional Visualization (Sales Over Time by City)
31:47 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices)
34:19 - Creating an Additional Visualization (Sales Over Time by City) — Continued
38:22 - Filtering City Data with Select Inputs (UI.Input_Selectize)
45:07 - Rendering Shiny Inputs Within Text
46:07 - Quick Formatting Adjustments
46:46 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?)
48:15 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors
51:52 - Deploying Our Updated App!
53:11 - Advanced Concepts in Shiny Reactivity (Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later) & Other Resources

Brought to you by Posit Connect!
58:28 - Thank you to Posit Connect, Our Sponsor

Part 3: How to make your Python Dashboard look Professional! (Layouts in Shiny)
59:47 - Part 3 Overview
1:01:15 - Using Shiny Templates to Get Started Fast
1:03:08 - Using Layout Components to Customize our Apps (Cards, Sidebars, Tabs, etc.)
1:07:42 - Adding a Sidebar within a Card
1:12:25 - Adding a Card with Tabs to Display Various Visualizations
1:17:22 - Structuring Data in Columns / Grids (layout_columns() & layout_column_wrap())
1:26:17 - Final Touches & Tips (Filling in Visualizations into our Tab Views)

Part 4: How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard!
1:33:13 - Part 4 Overview
1:34:51 - Getting Setup with the Code (cloning branch from GitHub)
1:43:28 - Create a Seaborn Heatmap Chart (Sales Volume by Hour of the Day)
1:48:12 - Creating Interactive Charts with Jupyter Widgets (Plotly, Altair, Bokeh, Pydeck, & More…) | render_widget decorator
2:02:02 - Additional Rendering Options, Final Touches and Next Steps

Part 5
2:04:24 - Part 5 Overview
2:06:22 - Modifying HTML and CSS in Shiny
2:12:16 - Adding a Logo Image
2:14:58 - Styling Labels and Containers (Aligning our Image w/ the Title — Custom Divs)
2:24:52 - Customizing Altair Charts (Gridlines, Font, Axis Labels, Etc.)
2:32:21 - Customizing Plotly Visualizations
2:41:48 - Customizing Seaborn & Folium Heatmaps
2:48:47 - Final Touches, Clean Up, Recap and Next Steps

Conclusion
2:51:41 - Final words! Like & Subscribe pretty please!!

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[vishnuchandran]

Hi Keith, I have watched your tutorials for Pandas (older and newer ones). I really find your teaching very effective for me to understand. It would be really helpful if you could do some Python tutorials for cloud computing.

[shivam_dxrk]

Hi Keith, wanted to say that your content is for me the best in the Python YT space, I just wish you can help us with using Open Source LLMs and a complete Hugging Face tutorial which doesn't use Open AI key, since they dont provide them anymore.

If I may, some other topics would also love to learn from you would be Git/github tutorial, Docker, Python + SQL project since there aren't many

Anyways thanks for all the hard work you do, may God Bless you 🙏

[raphaelmatthew]

Saving all your videos in order to watch and practice later 😊

[rohitrathod]

Hey Keith, I'm enjoying watching your videos and always love your techniques for solving problems. I am starting my data science journey, but I am really confused about which technologies I should learn. Could you please create a video on the roadmap for data science so that more people like me can get a clear view on the path to becoming a data science developer like you? Until you upload the video, could you please give me some starting topics so I can start my journey in the right direction?

[SalehGoodarzian]

Hi Keith, I have been watching your video for many years now and I've learned a lot from you. Just wanted to thank you again...just a quick question. Your videos have high quality. How did you record your screen and yourself? Which software did you use? I would be thankful if you could let me know. Best wishes and regards

[ularkadutdotnet]

nice, btw do you have tutorial to run shiny on server using docker ?

[Kira-vsnp]

great tutorial, I'm learning a lot. Thanks for your efforts.

[ghostofuchiha]

Hi Keith,
I just want to know how does a ML model handle data once its deployed in production.? Like when we build a model we scale the data, remove nulls, transform it and then use it, but how does all this happen in already deployed models? Because a normal day to day life will have all the uncleaned data. Pleas help, I m really confused. I can build the ml, dl, transformers etc but am confused how is data preprocessing tackled after model is deployed .

Basically how is all preprocessing captured in the model to be used after deployment, is it through columntransformers and pipelines or are there any other steps or is it under mlops umbrella ?

[MudassirHussain-djfv]

How can we add authentication management in shiny python apps?

[williansuarez]

¡Excelente!, ¿podrías darle un poco mas de zoom al código?

[anuj]

I hate shiny name. She is our neighbour's dog who barks whole day.😤

[patcher]

Why so many web frameworks like there is already streamlit which is popular for ml ai dashboard and easy for deployment