Streamlit visualization use cases
Some Streamlit users are relatively experienced Python developers with well-tested workflows in visualization libraries of their choice. For these users, the best path forward is the one we’ve taken so far, which is to create graphs in our library of choice (Seaborn, Matplotlib, Bokeh, and so on) and then use the appropriate Streamlit function to write this to the app.
Other Streamlit users will have less experience in Pythonic graphing, and especially for these users, Streamlit offers a few built-in functions. We’ll start with built-in libraries and move on to learning how to import the most popular and powerful libraries for our Streamlit apps.
