From Streamlit to Flask: When and Why to Make the Jump

This post was originally published on my site . Introduction Creating apps in Streamlit feels like a natural extension for data work in Python . You've got your dataframes and charts, possibly already visualized in Jupyter Notebook. You decide to share your work as a web app, fire up Streamlit and... everything is so smooth . You're still in your known realm, operating on data. One method call and your table is displayed. Two more lines and you have a filter or a clickable button. It's less than a few hours and you've built a fully-fledged app. Everything is great, very idyllic... until it isn't . You start hitting a wall , you're literally battling the framework , trying to squeeze out more than it's meant to offer . A fantasy scenario? Not necessarily. This can be a reality of developing Streamlit applications. But it doesn't have to be a harsh reality. Streamlit can be a fantastic tool when you embrace its strengths , while being aware of its limitations . So in this guide we will t