Mutate Tabular Data with Streamlit

Learn to append data to a DataFrame with Streamlit.

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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.

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