displaying-streamlit-data

To visualize a dataframe in Streamlit, you display interactive tables using st.dataframe or editable formats with st.data_editor, allowing users to directly manipulate and review data within the application.

The best way to create interactive charts in Streamlit is using native functions for basic line, bar, and scatter charts, or integrating Altair to build complex, customized visualizations for analytical dashboards.

Yes, you can display key performance metrics in Streamlit using the st.metric function, which supports KPI displays complete with delta indicators and sparklines to show trends like month-over-month growth.

Yes, Streamlit supports Altair for complex data visualizations, enabling you to build advanced chart types and layered graphical representations beyond the native Streamlit charting functions.

To format and edit tables in a Streamlit dashboard, use the st.data_editor component to render configurable dataframes that support direct user editing and custom cell formatting.

Use Altair instead of native Streamlit charts when your data visualization requires complex layered graphics, specific custom formatting, or advanced interactive charting features that native line and bar charts cannot provide.