Mutate Tabular Data with Streamlit

The need for mutation

In data work, occasions might arise when we’ve collected some data but realize we require more after processing has begun. This would probably require generating a new DataFrame that combines the old and new data.

Fortunately, Streamlit provides a way to append data to an existing DataFrame. The add_rows() method allows us to do this through a process called mutation.