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- What are Reactive.Calcs and How Do We Use Them Properly? DataFrame Best Practices
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Classroom Contents
Build Awesome Web Apps and Dashboards with Python - Full Shiny for Python Course
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- 1 - About Shiny for Python & Course Overview
- 2 - Intro to Shiny & Gallery Examples
- 3 - Getting Started with the Shinylive Playground
- 4 - Building a custom visualization with Shinylive
- 5 - Easily sharing the code/application for a Shinylive app
- 6 - Building a Shiny Express App locally VSCode
- 7 - How to run app if you're not using VSCode
- 8 - Further customization of our app adding title, using CSV data, dynamic input
- 9 - Deploying our Shiny app to the web
- 10 - Part 2 Overview
- 11 - Getting Started with Code Part 2
- 12 - Adding Shiny Components Inputs, Outputs, & Display Messages
- 13 - Creating an Additional Visualization Sales Over Time by City
- 14 - What are Reactive.Calcs and How Do We Use Them Properly? DataFrame Best Practices
- 15 - Creating an Additional Visualization Sales Over Time by City — Continued
- 16 - Filtering City Data with Select Inputs UI.Input_Selectize
- 17 - Rendering Shiny Inputs Within Text
- 18 - Quick Formatting Adjustments
- 19 - Understanding the Shiny Reactivity Model How Does Shiny Render Things?
- 20 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors
- 21 - Deploying Our Updated App!
- 22 - Advanced Concepts in Shiny Reactivity Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later & Other Resources
- 23 - Thank you to Posit Connect, Our Sponsor
- 24 - Part 3 Overview
- 25 - Using Shiny Templates to Get Started Fast
- 26 - Using Layout Components to Customize our Apps Cards, Sidebars, Tabs, etc.
- 27 - Adding a Sidebar within a Card
- 28 - Adding a Card with Tabs to Display Various Visualizations
- 29 - Structuring Data in Columns / Grids layout_columns & layout_column_wrap
- 30 - Final Touches & Tips Filling in Visualizations into our Tab Views
- 31 - Part 4 Overview
- 32 - Getting Setup with the Code cloning branch from GitHub
- 33 - Adding Matplotlib-based visualizations render.plot Shiny for Python decorator
- 34 - Create a Seaborn Heatmap Chart Sales Volume by Hour of the Day
- 35 - Creating Interactive Charts with Jupyter Widgets Plotly, Altair, Bokeh, Pydeck, & More… | render_widget decorator
- 36 - Implementing Folium for Location-Based Heatmaps render.ui decorator
- 37 - Enhancing DataFrames with Filters and Selection Modes render.data_frame, render.DataGrid, render.DataTable, etc.
- 38 - Additional Rendering Options, Final Touches and Next Steps
- 39 - Part 5 Overview
- 40 - Modifying HTML and CSS in Shiny
- 41 - Adding a Logo Image
- 42 - Styling Labels and Containers Aligning our Image w/ the Title — Custom Divs
- 43 - Customizing Altair Charts Gridlines, Font, Axis Labels, Etc.
- 44 - Customizing Plotly Visualizations
- 45 - Customizing Seaborn & Folium Heatmaps
- 46 - Final Touches, Clean Up, Recap and Next Steps
- 47 - Final words! Like & Subscribe pretty please!!
