displaying-streamlit-data

Display dataframes in Streamlit by configuring column types such as Currency, pinning important fields, and adding sparklines to metrics. This presents tabular data clearly and shows KPIs with temporal context without building custom visualization plumbing.

The best way to build interactive charts in Streamlit for exploratory data analysis is using native charts for simple visualizations or Altair and PyECharts for custom encodings and layered charts. This enables selection modes and event handlers for dashboard building.

Yes, you can enable editable tables and selection behavior in Streamlit dataframes. The configuration supports formatting columns, hiding technical fields, enabling lightweight data entry, and setting up selection modes with event handlers for interactivity.

Yes, Streamlit supports Altair and PyECharts for custom dashboard visualizations. Use these libraries when you need custom encodings, layered charts, or interactive behaviors beyond the native line, bar, scatter, and area charts available for simple exploratory views.

Show KPI metrics with temporal context in a Streamlit app by adding inline trend sparklines to metric displays. This helps users explore, interpret, and act on insights by providing visual temporal trends alongside key performance indicators.