plotly-interactive-guide
Guide to Plotly.py for interactive scientific visualizations in Python
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill plotly-interactive-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Plotly Interactive Visualization Guide ## Overview Plotly.py is a high-level, interactive graphing library for Python with over 18K stars on GitHub. Built on top of plotly.js (which itself uses D3.js and WebGL), Plotly enables researchers to create publication-quality interactive figures directly from Python code. The library integrates seamlessly with pandas DataFrames, NumPy arrays, and the broader scientific Python ecosystem. What sets Plotly apart for academic researchers is its Plotly Express module, which provides a concise, high-level API for creating complex visualizations in a single function call. Researchers can go from a pandas DataFrame to a fully interactive figure in one line of code, then customize it further as needed. Every Plotly figure is inherently interactive, supporting hover tooltips, zoom, pan, and selection out of the box. Plotly also offers Dash, a framework for building analytical web applications entirely in Python. This allows researchers to create interactive dashboards for exploring experimental data, sharing results with collaborators, or building supplementary interactive materials for publications without needing front-end development skills. ##
- Overview
- Plotly Express for Quick Research Figures
- Scatter Plot with Regression
- Box Plot with Individual Data Points
- Violin Plot for Distribution Comparison
- Graph Objects for Fine-Grained Control
- Error Bar Plot for Experimental Results
- Heatmap for Correlation Analysis
- 3D and Specialized Scientific Plots
- 3D Surface Plot for Response Surfaces
- Animated Time-Series for Temporal Data
- Exporting for Publications
- Dash for Interactive Research Dashboards
- References
What does the plotly-interactive-guide skill do?
Guide to Plotly.py for interactive scientific visualizations in Python
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill plotly-interactive-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.