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For static publication figures use matplotlib or scientific-visualization.","Vunoverso\u002Fvunotrader","Vunoverso","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F209864562?v=4","01M6K3512KKS4YJM9CSVECAGPV","Isac Crivelli","01M6K351742MZWPMBJVY77HEVC",0,"MIT","https:\u002F\u002Fgithub.com\u002FVunoverso\u002Fvunotrader\u002Fblob\u002Fmain\u002Fn8n-skills-main\u002Fskills\u002Fskills\u002Fskills\u002Fplotly\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"dashboards","static","for","you","figures","zoom","scientific","info","use","interactive","web","Charts","matplotlib","analysis","library","visualization","presentations","hover","2026-11-10 10:12:30",false,"---\nname: plotly\ndescription: 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.\nlicense: MIT license\nmetadata:\n    skill-author: K-Dense Inc.\n---\n\n# Plotly\n\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\n\n## Quick Start\n\nInstall Plotly:\n```bash\nuv pip install plotly\n```\n\nBasic usage with Plotly Express (high-level API):\n```python\nimport plotly.express as px\nimport pandas as pd\n\ndf = pd.DataFrame({\n    'x': [1, 2, 3, 4],\n    'y': [10, 11, 12, 13]\n})\n\nfig = px.scatter(df, x='x', y='y', title='My First Plot')\nfig.show()\n```\n\n## Choosing Between APIs\n\n### Use Plotly Express (px)\nFor quick, standard visualizations with sensible defaults:\n- Working with pandas DataFrames\n- Creating common chart types (scatter, line, bar, histogram, etc.)\n- Need automatic color encoding and legends\n- Want minimal code (1-5 lines)\n\nSee reference\u002Fplotly-express.md for complete guide.\n\n### Use Graph Objects (go)\nFor fine-grained control and custom visualizations:\n- Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)\n- Building complex multi-trace figures from scratch\n- Need precise control over individual components\n- Creating specialized visualizations with custom shapes and annotations\n\nSee reference\u002Fgraph-objects.md for complete guide.\n\n**Note:** Plotly Express returns graph objects Figure, so you can combine approaches:\n```python\nfig = px.scatter(df, x='x', y='y')\nfig.update_layout(title='Custom Title')  # Use go methods on px figure\nfig.add_hline(y=10)                     # Add shapes\n```\n\n## Core Capabilities\n\n### 1. Chart Types\n\nPlotly supports 40+ chart types organized into categories:\n\n**Basic Charts:** scatter, line, bar, pie, area, bubble\n\n**Statistical Charts:** histogram, box plot, violin, distribution, error bars\n\n**Scientific Charts:** heatmap, contour, ternary, image display\n\n**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series\n\n**Maps:** scatter maps, choropleth, density maps (geographic visualization)\n\n**3D Charts:** scatter3d, surface, mesh, cone, volume\n\n**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge\n\nFor detailed examples and usage of all chart types, see reference\u002Fchart-types.md.\n\n### 2. Layouts and Styling\n\n**Subplots:** Create multi-plot figures with shared axes:\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))\nfig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)\n```\n\n**Templates:** Apply coordinated styling:\n```python\nfig = px.scatter(df, x='x', y='y', template='plotly_dark')\n# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white\n```\n\n**Customization:** Control every aspect of appearance:\n- Colors (discrete sequences, continuous scales)\n- Fonts and text\n- Axes (ranges, ticks, grids)\n- Legends\n- Margins and sizing\n- Annotations and shapes\n\nFor complete layout and styling options, see reference\u002Flayouts-styling.md.\n\n### 3. Interactivity\n\nBuilt-in interactive features:\n- Hover tooltips with customizable data\n- Pan and zoom\n- Legend toggling\n- Box\u002Flasso selection\n- Rangesliders for time series\n- Buttons and dropdowns\n- Animations\n\n```python\n# Custom hover template\nfig.update_traces(\n    hovertemplate='\u003Cb>%{x}\u003C\u002Fb>\u003Cbr>Value: %{y:.2f}\u003Cextra>\u003C\u002Fextra>'\n)\n\n# Add rangeslider\nfig.update_xaxes(rangeslider_visible=True)\n\n# Animations\nfig = px.scatter(df, x='x', y='y', animation_frame='year')\n```\n\nFor complete interactivity guide, see reference\u002Fexport-interactivity.md.\n\n### 4. Export Options\n\n**Interactive HTML:**\n```python\nfig.write_html('chart.html')                       # Full standalone\nfig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file\n```\n\n**Static Images (requires kaleido):**\n```bash\nuv pip install kaleido\n```\n\n```python\nfig.write_image('chart.png')   # PNG\nfig.write_image('chart.pdf')   # PDF\nfig.write_image('chart.svg')   # SVG\n```\n\nFor complete export options, see reference\u002Fexport-interactivity.md.\n\n## Common Workflows\n\n### Scientific Data Visualization\n\n```python\nimport plotly.express as px\n\n# Scatter plot with trendline\nfig = px.scatter(df, x='temperature', y='yield', trendline='ols')\n\n# Heatmap from matrix\nfig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')\n\n# 3D surface plot\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])\n```\n\n### Statistical Analysis\n\n```python\n# Distribution comparison\nfig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)\n\n# Box plot with all points\nfig = px.box(df, x='category', y='value', points='all')\n\n# Violin plot\nfig = px.violin(df, x='group', y='measurement', box=True)\n```\n\n### Time Series and Financial\n\n```python\n# Time series with rangeslider\nfig = px.line(df, x='date', y='price')\nfig.update_xaxes(rangeslider_visible=True)\n\n# Candlestick chart\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Candlestick(\n    x=df['date'],\n    open=df['open'],\n    high=df['high'],\n    low=df['low'],\n    close=df['close']\n)])\n```\n\n### Multi-Plot Dashboards\n\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(\n    rows=2, cols=2,\n    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),\n    specs=[[{'type': 'scatter'}, {'type': 'bar'}],\n           [{'type': 'histogram'}, {'type': 'box'}]]\n)\n\nfig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)\nfig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)\nfig.add_trace(go.Histogram(x=data), row=2, col=1)\nfig.add_trace(go.Box(y=data), row=2, col=2)\n\nfig.update_layout(height=800, showlegend=False)\n```\n\n## Integration with Dash\n\nFor interactive web applications, use Dash (Plotly's web app framework):\n\n```bash\nuv pip install dash\n```\n\n```python\nimport dash\nfrom dash import dcc, html\nimport plotly.express as px\n\napp = dash.Dash(__name__)\n\nfig = px.scatter(df, x='x', y='y')\n\napp.layout = html.Div([\n    html.H1('Dashboard'),\n    dcc.Graph(figure=fig)\n])\n\napp.run_server(debug=True)\n```\n\n## Reference Files\n\n- **plotly-express.md** - High-level API for quick visualizations\n- **graph-objects.md** - Low-level API for fine-grained control\n- **chart-types.md** - Complete catalog of 40+ chart types with examples\n- **layouts-styling.md** - Subplots, templates, colors, customization\n- **export-interactivity.md** - Export options and interactive features\n\n## Additional Resources\n\n- Official documentation: https:\u002F\u002Fplotly.com\u002Fpython\u002F\n- API reference: https:\u002F\u002Fplotly.com\u002Fpython-api-reference\u002F\n- Community forum: https:\u002F\u002Fcommunity.plotly.com\u002F\n\n","# Plotly\n\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\n\n## Quick Start\n\nInstall Plotly:\n```bash\nuv pip install plotly\n```\n\nBasic usage with Plotly Express (high-level API):\n```python\nimport plotly.express as px\nimport pandas as pd\n\ndf = pd.DataFrame({\n    'x': [1, 2, 3, 4],\n    'y': [10, 11, 12, 13]\n})\n\nfig = px.scatter(df, x='x', y='y', title='My First Plot')\nfig.show()\n```\n\n## Choosing Between APIs\n\n### Use Plotly Express (px)\nFor quick, standard visualizations with sensible defaults:\n- Working with pandas DataFrames\n- Creating common chart types (scatter, line, bar, histogram, etc.)\n- Need automatic color encoding and legends\n- Want minimal code (1-5 lines)\n\nSee reference\u002Fplotly-express.md for complete guide.\n\n### Use Graph Objects (go)\nFor fine-grained control and custom visualizations:\n- Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)\n- Building complex multi-trace figures from scratch\n- Need precise control over individual components\n- Creating specialized visualizations with custom shapes and annotations\n\nSee reference\u002Fgraph-objects.md for complete guide.\n\n**Note:** Plotly Express returns graph objects Figure, so you can combine approaches:\n```python\nfig = px.scatter(df, x='x', y='y')\nfig.update_layout(title='Custom Title')  # Use go methods on px figure\nfig.add_hline(y=10)                     # Add shapes\n```\n\n## Core Capabilities\n\n### 1. Chart Types\n\nPlotly supports 40+ chart types organized into categories:\n\n**Basic Charts:** scatter, line, bar, pie, area, bubble\n\n**Statistical Charts:** histogram, box plot, violin, distribution, error bars\n\n**Scientific Charts:** heatmap, contour, ternary, image display\n\n**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series\n\n**Maps:** scatter maps, choropleth, density maps (geographic visualization)\n\n**3D Charts:** scatter3d, surface, mesh, cone, volume\n\n**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge\n\nFor detailed examples and usage of all chart types, see reference\u002Fchart-types.md.\n\n### 2. Layouts and Styling\n\n**Subplots:** Create multi-plot figures with shared axes:\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))\nfig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)\n```\n\n**Templates:** Apply coordinated styling:\n```python\nfig = px.scatter(df, x='x', y='y', template='plotly_dark')\n# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white\n```\n\n**Customization:** Control every aspect of appearance:\n- Colors (discrete sequences, continuous scales)\n- Fonts and text\n- Axes (ranges, ticks, grids)\n- Legends\n- Margins and sizing\n- Annotations and shapes\n\nFor complete layout and styling options, see reference\u002Flayouts-styling.md.\n\n### 3. Interactivity\n\nBuilt-in interactive features:\n- Hover tooltips with customizable data\n- Pan and zoom\n- Legend toggling\n- Box\u002Flasso selection\n- Rangesliders for time series\n- Buttons and dropdowns\n- Animations\n\n```python\n# Custom hover template\nfig.update_traces(\n    hovertemplate='\u003Cb>%{x}\u003C\u002Fb>\u003Cbr>Value: %{y:.2f}\u003Cextra>\u003C\u002Fextra>'\n)\n\n# Add rangeslider\nfig.update_xaxes(rangeslider_visible=True)\n\n# Animations\nfig = px.scatter(df, x='x', y='y', animation_frame='year')\n```\n\nFor complete interactivity guide, see reference\u002Fexport-interactivity.md.\n\n### 4. Export Options\n\n**Interactive HTML:**\n```python\nfig.write_html('chart.html')                       # Full standalone\nfig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file\n```\n\n**Static Images (requires kaleido):**\n```bash\nuv pip install kaleido\n```\n\n```python\nfig.write_image('chart.png')   # PNG\nfig.write_image('chart.pdf')   # PDF\nfig.write_image('chart.svg')   # SVG\n```\n\nFor complete export options, see reference\u002Fexport-interactivity.md.\n\n## Common Workflows\n\n### Scientific Data Visualization\n\n```python\nimport plotly.express as px\n\n# Scatter plot with trendline\nfig = px.scatter(df, x='temperature', y='yield', trendline='ols')\n\n# Heatmap from matrix\nfig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')\n\n# 3D surface plot\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])\n```\n\n### Statistical Analysis\n\n```python\n# Distribution comparison\nfig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)\n\n# Box plot with all points\nfig = px.box(df, x='category', y='value', points='all')\n\n# Violin plot\nfig = px.violin(df, x='group', y='measurement', box=True)\n```\n\n### Time Series and Financial\n\n```python\n# Time series with rangeslider\nfig = px.line(df, x='date', y='price')\nfig.update_xaxes(rangeslider_visible=True)\n\n# Candlestick chart\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Candlestick(\n    x=df['date'],\n    open=df['open'],\n    high=df['high'],\n    low=df['low'],\n    close=df['close']\n)])\n```\n\n### Multi-Plot Dashboards\n\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(\n    rows=2, cols=2,\n    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),\n    specs=[[{'type': 'scatter'}, {'type': 'bar'}],\n           [{'type': 'histogram'}, {'type': 'box'}]]\n)\n\nfig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)\nfig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)\nfig.add_trace(go.Histogram(x=data), row=2, col=1)\nfig.add_trace(go.Box(y=data), row=2, col=2)\n\nfig.update_layout(height=800, showlegend=False)\n```\n\n## Integration with Dash\n\nFor interactive web applications, use Dash (Plotly's web app framework):\n\n```bash\nuv pip install dash\n```\n\n```python\nimport dash\nfrom dash import dcc, html\nimport plotly.express as px\n\napp = dash.Dash(__name__)\n\nfig = px.scatter(df, x='x', y='y')\n\napp.layout = html.Div([\n    html.H1('Dashboard'),\n    dcc.Graph(figure=fig)\n])\n\napp.run_server(debug=True)\n```\n\n## Reference Files\n\n- **plotly-express.md** - High-level API for quick visualizations\n- **graph-objects.md** - Low-level API for fine-grained control\n- **chart-types.md** - Complete catalog of 40+ chart types with examples\n- **layouts-styling.md** - Subplots, templates, colors, customization\n- **export-interactivity.md** - Export options and interactive features\n\n## Additional Resources\n\n- Official documentation: https:\u002F\u002Fplotly.com\u002Fpython\u002F\n- API reference: https:\u002F\u002Fplotly.com\u002Fpython-api-reference\u002F\n- Community forum: https:\u002F\u002Fcommunity.plotly.com\u002F\n\n",{"name":5,"license":47,"metadata":48,"description":7},"MIT license",{"skill-author":49},"K-Dense Inc.","main","7e79672230b8ee6813996db1d2154d897b4a90d4","n8n-skills-main\u002Fskills\u002Fskills\u002Fskills\u002Fplotly","https:\u002F\u002Fgithub.com\u002FVunoverso\u002Fvunotrader","https:\u002F\u002Fvunotrader.vercel.app","https:\u002F\u002Fgithub.com\u002FVunoverso","2026-04-05 05:14:44",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Vunoverso\u002Fvunotrader --skill plotly",{"tool":63,"command":64},"bunx","bunx skills add Vunoverso\u002Fvunotrader --skill plotly",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Vunoverso\u002Fvunotrader --skill plotly","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FVunoverso\u002Fvunotrader\u002Fblob\u002Fmain\u002Fn8n-skills-main\u002Fskills\u002Fskills\u002Fskills\u002Fplotly\u002FSKILL.md",true,[],[72,106,126,141,166,183],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":86,"publishedAt":42},"01M6K88E93RTFNS0KMZ9Z8ZKQ7","native-data-fetching","Use when implementing or debugging ANY network request, API call, or data fetching. 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