Debugging Streamlit apps

We broadly have two options for Streamlit development.

• Develop in Streamlit and st.write() as a debugger.
• Explore in Jupyter and then copy to Streamlit.

Developing in Streamlit

In the first option, we write our code directly in Streamlit as we're experimenting and exploring exactly what our application will do. We've basically been taking this option already, which works very well if we have less exploration work and more implementation work to do.

Pros:

• What you see is what you get
• No need to maintain both IPython and Python versions of the same app
• Better experience for learning how to write production code

Cons:

• A slower feedback loop (the entire app must run before feedback)
• A potentially unfamiliar development environment

Exploring in Jupyter and then copying to Streamlit

Another option is to utilize the extremely popular Jupyter data science product to write and test out...