How does this skill solve the Plotly dashboard issue?

It replaces the fragile in-place FigureWidget update pattern with a more reliable 'recreate-on-update' approach wrapped in an ipywidgets Output widget.

Is this fix compatible with VS Code Jupyter notebooks?

Yes, it is specifically optimized for the VS Code renderer by using the 'notebook' renderer setting and proper widget output management.

Why does Plotly FigureWidget throw a KeyError: 'uid'?

This is a known bug in Plotly 6.x occurring when shapes are modified during a JavaScript-to-Python trace delta sync, specifically inside the basewidget.py handler.

Will this approach work with large datasets?

Yes, the skill includes patterns for pre-computing histograms and subsampling large datasets to ensure the dashboard remains responsive even when recreating figures.

Interactive Plotly FigureWidget Fix

Name: Interactive Plotly FigureWidget Fix
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Resolves KeyError: 'uid' errors when updating interactive Plotly FigureWidget shapes and sliders in VS Code.

About

This skill provides a battle-tested workflow for building stable interactive dashboards in VS Code using Plotly and ipywidgets. It specifically addresses a critical bug in Plotly 6.x where modifying shapes within a FigureWidget triggers a 'uid' KeyError during trace delta synchronization. By implementing a 'recreate-on-update' pattern paired with ipywidgets Output containers, developers can maintain responsive, multi-plot dashboards with synchronized thresholds and high stability, bypassing the limitations of standard FigureWidget batch updates.

Key Features

0 GitHub stars
Implementation of ipywidgets Output and clear_output for clean rendering
Efficient data subsampling and pre-computation patterns
Optimized 'Recreate Figure' workflow for VS Code compatibility
Fixes Plotly 6.x FigureWidget 'uid' KeyError bug
Interactive dashboard synchronization for multiple linked plots

Use Cases

Building QC dashboards for single-cell genomics or scientific datasets
Creating interactive threshold sliders for real-time data filtering
Developing linked multi-plot visualizations in VS Code Jupyter notebooks

About

Key Features

0 GitHub stars
Implementation of ipywidgets Output and clear_output for clean rendering
Efficient data subsampling and pre-computation patterns
Optimized 'Recreate Figure' workflow for VS Code compatibility
Fixes Plotly 6.x FigureWidget 'uid' KeyError bug
Interactive dashboard synchronization for multiple linked plots

Use Cases

Building QC dashboards for single-cell genomics or scientific datasets
Creating interactive threshold sliders for real-time data filtering
Developing linked multi-plot visualizations in VS Code Jupyter notebooks