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plotlylisted

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.
Yuuqq/research-grade-skills · ★ 0 · AI & Automation · score 73
Install: claude install-skill Yuuqq/research-grade-skills
# Plotly Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types. ## Quick Start Install Plotly: ```bash uv pip install plotly ``` Basic usage with Plotly Express (high-level API): ```python import plotly.express as px import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 4], 'y': [10, 11, 12, 13] }) fig = px.scatter(df, x='x', y='y', title='My First Plot') fig.show() ``` ## Choosing Between APIs ### Use Plotly Express (px) For quick, standard visualizations with sensible defaults: - Working with pandas DataFrames - Creating common chart types (scatter, line, bar, histogram, etc.) - Need automatic color encoding and legends - Want minimal code (1-5 lines) See [references/plotly-express.md](references/plotly-express.md) for complete guide. ### Use Graph Objects (go) For fine-grained control and custom visualizations: - Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts) - Building complex multi-trace figures from scratch - Need precise control over individual components - Creating specialized visualizations with custom shapes and annotations See [references/graph-objects.md](references/graph-objects.md) for complete guide. **Note:** Plotly Express returns graph objects Figure, so you can combine approaches: ```python fig = px.scatter(df, x='x', y='y') fig.update_layout(title='Custom Title') # Use go methods on px figure fig.add_hline(y=10) # Add shapes ```