Build Awesome Web Apps and Dashboards with Python - Full Shiny for Python Course

Overview

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Learn to build, share, and deploy web apps and dashboards using the Shiny for Python framework in this comprehensive video course. Master the basics and advance to complex concepts as you create custom visualizations, implement interactive components, and explore various layout options. Discover how to integrate multiple visualization libraries like Matplotlib, Plotly, and Seaborn into a single dashboard. Dive into topics such as reactivity, data manipulation, and customization of HTML and CSS. Follow along with hands-on examples, from setting up your development environment to deploying your finished app. By the end of this course, gain the skills to create professional-looking, interactive web applications and data dashboards using Python.

Syllabus

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