plotly_skill

Overview

This skill exposes Plotly, a Python library for creating interactive, publication-quality visualizations across 40+ chart types. It supports quick, high-level plotting with Plotly Express and detailed, component-level control with Graph Objects. Use it to produce interactive HTML visualizations or export static images (PNG, PDF, SVG) for reports and presentations.

How this skill works

You feed Plotly pandas DataFrames or arrays and choose a high-level PX function for fast charts or construct Figures with graph_objects for precise control. The library builds interactive figures with built-in hover, zoom, selection, and animation features, then exports them as standalone HTML or static images (requires kaleido). It also integrates with Dash for embedding figures into interactive web apps and dashboards.

When to use it

• Quick exploratory plots from pandas with minimal code (Plotly Express).
• Creating publication-ready interactive figures for reports and presentations.
• Building custom, multi-trace or 3D scientific visualizations (graph_objects).
• Exporting interactive charts to standalone HTML or static images for distribution.
• Embedding interactive charts into Dash web applications and dashboards.

Best practices

• Start with Plotly Express for rapid prototyping, then switch to graph_objects to fine-tune layout and annotations.
• Keep data in tidy pandas DataFrames to leverage automatic encodings for color, facets, and animation frames.
• Use templates to enforce consistent styling across multiple figures (plotly_white, plotly_dark, etc.).
• Prefer interactive HTML for exploration and dashboards; export PNG/PDF for static publications (install kaleido for images).
• Limit overly dense markers/labels in interactive plots to maintain performance; use sampling or aggregation for large datasets.

Example use cases

• Scatter plot with trendline for experimental results using px.scatter with trendline='ols'.
• Time series dashboard with rangeslider and multiple subplots for financial analysis.
• 3D surface visualization of scientific simulation data using graph_objects.Surface.
• Choropleth maps showing geographic metrics and interactive hover tooltips.
• Multi-panel figure combining scatter, histogram, bar, and box plots for exploratory analysis.