Editable DataFrames

So far in this book, we have assumed that we want the data used in these apps to be static. We have used mostly CSV files or programmatically generated datasets that remain unchanged by the users of our apps.

This is very often the case, but we might want to give users the ability to alter or edit the underlying data in a very user-friendly way. To help solve this, Streamlit released st.experimental_data_editor, a way to give users edit ability on top of an st.dataframe-style interface.

There are a massive number of potential apps for editing DataFrames, from using Streamlit as a quality control system to allowing for direct edits to configuration parameters to doing even more of the “what-if” analyses that we have done so far in this book. As a creator of many different apps in a work setting, I have noticed that people are often extremely comfortable with the everpresent spreadsheet and prefer that type of UI.

For this example, let...