new-plot-type

You add a new plot type by creating a class under your plotting types directory, implementing a create_figure method that consumes a Pandas dataframe, and registering it centrally via a PlotFactory interface. This enables custom visualizations for domain-specific metrics without modifying core logic.

A factory pattern for data visualization provides a centralized interface to register and instantiate custom plot types. You need it when extending a built-in plotting suite with domain-specific metrics, ensuring new visualizations are scalable and wire into the UI without altering core code.

To wire a custom Plotly plot into the UI, implement your create_figure method, register the new plot class in the PlotFactory, and add a UI entry point to expose the new visualization type for users to select and render.

Yes, you need a Python-based plotting stack utilizing Pandas and Plotly to create custom plots with a factory pattern. The plot implementation requires a create_figure method that directly consumes a dataframe to generate the visualization.

Yes, you can visualize domain-specific metrics without modifying core plotting logic by using a factory-based workflow. You create a dedicated plot type class, implement the required figure creation method, and register it centrally to instantiate it via the UI.

The limitation of extending visualizations via factory registration is that each new plot requires consistent registration in the PlotFactory and a corresponding UI entry. Additionally, you must provide unit and integration tests to ensure the custom plot reliably handles dataframe inputs.