From Rshiny to PyShiny or Streamlit

Introduction to Shiny

Shiny empowers data scientists by transforming their analyses into interactive web applications, making data exploration, visualization, and model deployment more accessible to a broader audience. By using Shiny, data scientists can bridge the gap between analysis and action, making their insights more accessible, interactive, and impactful. A very informative guide is here by Hadley_Wickham. You can deploy the app on Shinyapp.io (and of course embed it in your blog), and an example from Alber_Rapp is:

From RShiny to PyShiny

Shiny is mainly used in R and integrated with RMarkdown, but transferring the syntax from Rshiny to Shiny for Python is seamless. The R and Python Shiny packages are quite similar, and as a result if you know how to build a Shiny app in R you are well on your way to building one in Python. All of the main components of Shiny like reactivity, rendering functions, and modules are the same, and both packages use the same JavaScript code. There are, however, a few differences that you need to keep in mind in order to build effective Shiny applications in Python.

This documentation contains a detailed comparison.

Introduction to Streamlit

Streamlit is an open-source Python library that is the most popular “Shiny” for Python users. There are tutorials in documentation and also in video. An example is here and you can embed it in your blog.