A filter panel gives dashboard users a practical way to inspect only the data that matters. It groups controls for selecting field values, such as product category, customer segment, or state, instead of displaying every row from a large dataset. section 6.3
Figure 6.8 The filter panel from the UI mock
The UI mock shows the filter panel as a row of field-specific controls positioned above the dashboard data. section 6.3.1
A useful filter panel follows these rules:
- Provide one dropdown or multiselect for each field users need to filter. section 6.3.1
- Populate options from the data’s unique values rather than hardcoding them, so new products, categories, or groupings appear automatically. section 6.3.1
- Treat an empty selection as no filter for that field. section 6.3.1
- Apply selected values by retaining rows whose column values belong to the chosen values. section 6.3.1
Filtering customer sales
Suppose users select the 18–25 and 26–35 age groups and the states CA, TX, and FL. The displayed data keeps those matching rows while continuing to include every product when Product name has no selection. section 6.3.1
A Python implementation can collect each Streamlit multiselect result in a filters dictionary, then pass that dictionary to a separate filtering function. Keeping panel construction and data wrangling separate makes the visible UI easier to extend and test. section 6.3.1
