Скачать или смотреть How to make Interactive Python Dashboards! (Reactivity in Shiny)

This is a quick-start guide to Shiny for Python, part 2 of a multi-part series.

Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this.

In this video, we'll build off of the last tutorial where we learned the basics of building, sharing, and deploying a Shiny app in Python. This video specifically focuses on reactivity in Shiny. You can watch this video as a standalone, but it may be helpful to watch the previous video (   • How to Build, Deploy, & Share a Python App...  ).

We'll cover:
⬡ Creating toggle options for dynamic visualizations
⬡ Understanding Shiny's reactivity model
⬡ Implementing various input selectors
⬡ Building reactive components and visualizations
⬡ Using reactive calculations and effects
⬡ Adding and formatting plots with Plotly
⬡ Documentation walkthrough to learn more about reactivity (reactivity.effect, reactivity.event, reactivity.isolate, reactivity.invalidate_later, etc…)

Video Resources:
Video #1:    • How to Build, Deploy, & Share a Python App...  
Starter Code (from end of video #1): https://github.com/KeithGalli/shiny-p...
Final App: https://keithgalli.shinyapps.io/final...

Shiny Resources:
Shiny for Python Homepage: https://shiny.posit.co/py/
Component Gallery: https://shiny.posit.co/py/components/
Express Documentation: https://shiny.posit.co/py/api/express/
Gordon Shotwell’s “How does Shiny Render Things?”:    • How does Shiny render things?  | Gordon Sh...  
Joe Cheng’s “Shiny Programming Practices”:    • Shiny Programming Practices || Joe Cheng |...  

Stay tuned for part 3, where we'll explore how to make your dashboard look more professional (layouts in Shiny).

Video by ‪@KeithGalli‬

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Video Timeline!
0:00 - Intro & Overview
1:01 - Getting Started with Code
2:08 - Adding Shiny Components (Inputs, Outputs, & Display Messages)
3:21 - Creating an Additional Visualization (Sales Over Time by City)
7:55 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices)
10:27 - Creating an Additional Visualization (Sales Over Time by City) — Continued
14:30 - Filtering City Data with Select Inputs (UI.Input_Selectize)
21:15 - Rendering Shiny Inputs Within Text
22:15 - Quick Formatting Adjustments
22:54 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?)
24:23 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors
28:00 - Deploying Our Updated App!
29:19 - Advanced Concepts in Shiny Reactivity (Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later) & Other Resources

All videos in the series:
Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny):    • How to Build, Deploy, & Share a Python App...  
Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny):    • How to make Interactive Python Dashboards!...  
Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny):    • How to make your Python Dashboard look Pro...  
Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python):    • How to combine Matplotlib, Plotly, Seaborn...  
Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python):    • How to Perfect Your Python Dashboard with ...