plotly

# 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 [reference/plotly-express.md](reference/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 [reference/graph-objects.md](reference/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
```

## Core Capabilities

### 1. Chart Types

Plotly supports 40+ chart types organized into categories:

**Basic Charts:** scatter, line, bar, pie, area, bubble

**Statistical Charts:** histogram, box plot, violin, distribution, error bars

**Scientific Charts:** heatmap, contour, ternary, image display

**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series

**Maps:** scatter maps, choropleth, density maps (geographic visualization)

**3D Charts:** scatter3d, surface, mesh, cone, volume

**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge

For detailed examples and usage of all chart types, see [reference/chart-types.md](reference/chart-types.md).

### 2. Layouts and Styling

**Subplots:** Create multi-plot figures with shared axes:
```python
from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))
fig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)
```

**Templates:** Apply coordinated styling:
```python
fig = px.scatter(df, x='x', y='y', template='plotly_dark')
# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white
```

**Customization:** Control every aspect of appearance:
- Colors (discrete sequences, continuous scales)
- Fonts and text
- Axes (ranges, ticks, grids)
- Legends
- Margins and sizing
- Annotations and shapes

For complete layout and styling options, see [reference/layouts-styling.md](reference/layouts-styling.md).

### 3. Interactivity

Built-in interactive features:
- Hover tooltips with customizable data
- Pan and zoom
- Legend toggling
- Box/lasso selection
- Rangesliders for time series
- Buttons and dropdowns
- Animations

```python
# Custom hover template
fig.update_traces(
    hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'
)

# Add rangeslider
fig.update_xaxes(rangeslider_visible=True)

# Animations
fig = px.scatter(df, x='x', y='y', animation_frame='year')
```

For complete interactivity guide, see [reference/export-interactivity.md](reference/export-interactivity.md).

### 4. Export Options

**Interactive HTML:**
```python
fig.write_html('chart.html')                       # Full standalone
fig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file
```

**Static Images (requires kaleido):**
```bash
uv pip install kaleido
```

```python
fig.write_image('chart.png')   # PNG
fig.write_image('chart.pdf')   # PDF
fig.write_image('chart.svg')   # SVG
```

For complete export options, see [reference/export-interactivity.md](reference/export-interactivity.md).

## Common Workflows

### Scientific Data Visualization

```python
import plotly.express as px

# Scatter plot with trendline
fig = px.scatter(df, x='temperature', y='yield', trendline='ols')

# Heatmap from matrix
fig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')

# 3D surface plot
import plotly.graph_objects as go
fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])
```

### Statistical Analysis

```python
# Distribution comparison
fig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)

# Box plot with all points
fig = px.box(df, x='category', y='value', points='all')

# Violin plot
fig = px.violin(df, x='group', y='measurement', box=True)
```

### Time Series and Financial

```python
# Time series with rangeslider
fig = px.line(df, x='date', y='price')
fig.update_xaxes(rangeslider_visible=True)

# Candlestick chart
import plotly.graph_objects as go
fig = go.Figure(data=[go.Candlestick(
    x=df['date'],
    open=df['open'],
    high=df['high'],
    low=df['low'],
    close=df['close']
)])
```

### Multi-Plot Dashboards

```python
from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),
    specs=[[{'type': 'scatter'}, {'type': 'bar'}],
           [{'type': 'histogram'}, {'type': 'box'}]]
)

fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)
fig.add_trace(go.Histogram(x=data), row=2, col=1)
fig.add_trace(go.Box(y=data), row=2, col=2)

fig.update_layout(height=800, showlegend=False)
```

## Integration with Dash

For interactive web applications, use Dash (Plotly's web app framework):

```bash
uv pip install dash
```

```python
import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

fig = px.scatter(df, x='x', y='y')

app.layout = html.Div([
    html.H1('Dashboard'),
    dcc.Graph(figure=fig)
])

app.run_server(debug=True)
```

## Reference Files

- **[plotly-express.md](reference/plotly-express.md)** - High-level API for quick visualizations
- **[graph-objects.md](reference/graph-objects.md)** - Low-level API for fine-grained control
- **[chart-types.md](reference/chart-types.md)** - Complete catalog of 40+ chart types with examples
- **[layouts-styling.md](reference/layouts-styling.md)** - Subplots, templates, colors, customization
- **[export-interactivity.md](reference/export-interactivity.md)** - Export options and interactive features

## Additional Resources

- Official documentation: https://plotly.com/python/
- API reference: https://plotly.com/python-api-reference/
- Community forum: https://community.plotly.com/

{
  "format": "aiskill/v1",
  "skill": {
    "name": "plotly",
    "slug": "plotly",
    "version": "1.0.0",
    "description": "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.",
    "category": "Research",
    "tags": [
      "research-science",
      "data-viz"
    ],
    "platforms": [
      "chatgpt",
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    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT LICENSE"
  },
  "prompts": {
    "system": "# Plotly\r\n\r\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\r\n\r\n## Quick Start\r\n\r\nInstall Plotly:\r\n```bash\r\nuv pip install plotly\r\n```\r\n\r\nBasic usage with Plotly Express (high-level API):\r\n```python\r\nimport plotly.express as px\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame({\r\n    'x': [1, 2, 3, 4],\r\n    'y': [10, 11, 12, 13]\r\n})\r\n\r\nfig = px.scatter(df, x='x', y='y', title='My First Plot')\r\nfig.show()\r\n```\r\n\r\n## Choosing Between APIs\r\n\r\n### Use Plotly Express (px)\r\nFor quick, standard visualizations with sensible defaults:\r\n- Working with pandas DataFrames\r\n- Creating common chart types (scatter, line, bar, histogram, etc.)\r\n- Need automatic color encoding and legends\r\n- Want minimal code (1-5 lines)\r\n\r\nSee [reference/plotly-express.md](reference/plotly-express.md) for complete guide.\r\n\r\n### Use Graph Objects (go)\r\nFor fine-grained control and custom visualizations:\r\n- Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)\r\n- Building complex multi-trace figures from scratch\r\n- Need precise control over individual components\r\n- Creating specialized visualizations with custom shapes and annotations\r\n\r\nSee [reference/graph-objects.md](reference/graph-objects.md) for complete guide.\r\n\r\n**Note:** Plotly Express returns graph objects Figure, so you can combine approaches:\r\n```python\r\nfig = px.scatter(df, x='x', y='y')\r\nfig.update_layout(title='Custom Title')  # Use go methods on px figure\r\nfig.add_hline(y=10)                     # Add shapes\r\n```\r\n\r\n## Core Capabilities\r\n\r\n### 1. Chart Types\r\n\r\nPlotly supports 40+ chart types organized into categories:\r\n\r\n**Basic Charts:** scatter, line, bar, pie, area, bubble\r\n\r\n**Statistical Charts:** histogram, box plot, violin, distribution, error bars\r\n\r\n**Scientific Charts:** heatmap, contour, ternary, image display\r\n\r\n**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series\r\n\r\n**Maps:** scatter maps, choropleth, density maps (geographic visualization)\r\n\r\n**3D Charts:** scatter3d, surface, mesh, cone, volume\r\n\r\n**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge\r\n\r\nFor detailed examples and usage of all chart types, see [reference/chart-types.md](reference/chart-types.md).\r\n\r\n### 2. Layouts and Styling\r\n\r\n**Subplots:** Create multi-plot figures with shared axes:\r\n```python\r\nfrom plotly.subplots import make_subplots\r\nimport plotly.graph_objects as go\r\n\r\nfig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))\r\nfig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)\r\n```\r\n\r\n**Templates:** Apply coordinated styling:\r\n```python\r\nfig = px.scatter(df, x='x', y='y', template='plotly_dark')\r\n# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white\r\n```\r\n\r\n**Customization:** Control every aspect of appearance:\r\n- Colors (discrete sequences, continuous scales)\r\n- Fonts and text\r\n- Axes (ranges, ticks, grids)\r\n- Legends\r\n- Margins and sizing\r\n- Annotations and shapes\r\n\r\nFor complete layout and styling options, see [reference/layouts-styling.md](reference/layouts-styling.md).\r\n\r\n### 3. Interactivity\r\n\r\nBuilt-in interactive features:\r\n- Hover tooltips with customizable data\r\n- Pan and zoom\r\n- Legend toggling\r\n- Box/lasso selection\r\n- Rangesliders for time series\r\n- Buttons and dropdowns\r\n- Animations\r\n\r\n```python\r\n# Custom hover template\r\nfig.update_traces(\r\n    hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'\r\n)\r\n\r\n# Add rangeslider\r\nfig.update_xaxes(rangeslider_visible=True)\r\n\r\n# Animations\r\nfig = px.scatter(df, x='x', y='y', animation_frame='year')\r\n```\r\n\r\nFor complete interactivity guide, see [reference/export-interactivity.md](reference/export-interactivity.md).\r\n\r\n### 4. Export Options\r\n\r\n**Interactive HTML:**\r\n```python\r\nfig.write_html('chart.html')                       # Full standalone\r\nfig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file\r\n```\r\n\r\n**Static Images (requires kaleido):**\r\n```bash\r\nuv pip install kaleido\r\n```\r\n\r\n```python\r\nfig.write_image('chart.png')   # PNG\r\nfig.write_image('chart.pdf')   # PDF\r\nfig.write_image('chart.svg')   # SVG\r\n```\r\n\r\nFor complete export options, see [reference/export-interactivity.md](reference/export-interactivity.md).\r\n\r\n## Common Workflows\r\n\r\n### Scientific Data Visualization\r\n\r\n```python\r\nimport plotly.express as px\r\n\r\n# Scatter plot with trendline\r\nfig = px.scatter(df, x='temperature', y='yield', trendline='ols')\r\n\r\n# Heatmap from matrix\r\nfig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')\r\n\r\n# 3D surface plot\r\nimport plotly.graph_objects as go\r\nfig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])\r\n```\r\n\r\n### Statistical Analysis\r\n\r\n```python\r\n# Distribution comparison\r\nfig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)\r\n\r\n# Box plot with all points\r\nfig = px.box(df, x='category', y='value', points='all')\r\n\r\n# Violin plot\r\nfig = px.violin(df, x='group', y='measurement', box=True)\r\n```\r\n\r\n### Time Series and Financial\r\n\r\n```python\r\n# Time series with rangeslider\r\nfig = px.line(df, x='date', y='price')\r\nfig.update_xaxes(rangeslider_visible=True)\r\n\r\n# Candlestick chart\r\nimport plotly.graph_objects as go\r\nfig = go.Figure(data=[go.Candlestick(\r\n    x=df['date'],\r\n    open=df['open'],\r\n    high=df['high'],\r\n    low=df['low'],\r\n    close=df['close']\r\n)])\r\n```\r\n\r\n### Multi-Plot Dashboards\r\n\r\n```python\r\nfrom plotly.subplots import make_subplots\r\nimport plotly.graph_objects as go\r\n\r\nfig = make_subplots(\r\n    rows=2, cols=2,\r\n    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),\r\n    specs=[[{'type': 'scatter'}, {'type': 'bar'}],\r\n           [{'type': 'histogram'}, {'type': 'box'}]]\r\n)\r\n\r\nfig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)\r\nfig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)\r\nfig.add_trace(go.Histogram(x=data), row=2, col=1)\r\nfig.add_trace(go.Box(y=data), row=2, col=2)\r\n\r\nfig.update_layout(height=800, showlegend=False)\r\n```\r\n\r\n## Integration with Dash\r\n\r\nFor interactive web applications, use Dash (Plotly's web app framework):\r\n\r\n```bash\r\nuv pip install dash\r\n```\r\n\r\n```python\r\nimport dash\r\nfrom dash import dcc, html\r\nimport plotly.express as px\r\n\r\napp = dash.Dash(__name__)\r\n\r\nfig = px.scatter(df, x='x', y='y')\r\n\r\napp.layout = html.Div([\r\n    html.H1('Dashboard'),\r\n    dcc.Graph(figure=fig)\r\n])\r\n\r\napp.run_server(debug=True)\r\n```\r\n\r\n## Reference Files\r\n\r\n- **[plotly-express.md](reference/plotly-express.md)** - High-level API for quick visualizations\r\n- **[graph-objects.md](reference/graph-objects.md)** - Low-level API for fine-grained control\r\n- **[chart-types.md](reference/chart-types.md)** - Complete catalog of 40+ chart types with examples\r\n- **[layouts-styling.md](reference/layouts-styling.md)** - Subplots, templates, colors, customization\r\n- **[export-interactivity.md](reference/export-interactivity.md)** - Export options and interactive features\r\n\r\n## Additional Resources\r\n\r\n- Official documentation: https://plotly.com/python/\r\n- API reference: https://plotly.com/python-api-reference/\r\n- Community forum: https://community.plotly.com/",
    "user_template": ""
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The plotly/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/plotly",
    "exported_at": "2026-11-03T07:40:55.891Z"
  }
}

format: aiskill/v1
skill:
  name: plotly
  slug: plotly
  version: 1.0.0
  description: >-
    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.
  category: Research
  tags:
    - research-science
    - data-viz
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT LICENSE
prompts:
  system: "# Plotly\r\n\r\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\r\n\r\n## Quick Start\r\n\r\nInstall Plotly:\r\n```bash\r\nuv pip install plotly\r\n```\r\n\r\nBasic usage with Plotly Express (high-level API):\r\n```python\r\nimport plotly.express as px\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame({\r\n    'x': [1, 2, 3, 4],\r\n    'y': [10, 11, 12, 13]\r\n})\r\n\r\nfig = px.scatter(df, x='x', y='y', title='My First Plot')\r\nfig.show()\r\n```\r\n\r\n## Choosing Between APIs\r\n\r\n### Use Plotly Express (px)\r\nFor quick, standard visualizations with sensible defaults:\r\n- Working with pandas DataFrames\r\n- Creating common chart types (scatter, line, bar, histogram, etc.)\r\n- Need automatic color encoding and legends\r\n- Want minimal code (1-5 lines)\r\n\r\nSee [reference/plotly-express.md](reference/plotly-express.md) for complete guide.\r\n\r\n### Use Graph Objects (go)\r\nFor fine-grained control and custom visualizations:\r\n- Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)\r\n- Building complex multi-trace figures from scratch\r\n- Need precise control over individual components\r\n- Creating specialized visualizations with custom shapes and annotations\r\n\r\nSee [reference/graph-objects.md](reference/graph-objects.md) for complete guide.\r\n\r\n**Note:** Plotly Express returns graph objects Figure, so you can combine approaches:\r\n```python\r\nfig = px.scatter(df, x='x', y='y')\r\nfig.update_layout(title='Custom Title')  # Use go methods on px figure\r\nfig.add_hline(y=10)                     # Add shapes\r\n```\r\n\r\n## Core Capabilities\r\n\r\n### 1. Chart Types\r\n\r\nPlotly supports 40+ chart types organized into categories:\r\n\r\n**Basic Charts:** scatter, line, bar, pie, area, bubble\r\n\r\n**Statistical Charts:** histogram, box plot, violin, distribution, error bars\r\n\r\n**Scientific Charts:** heatmap, contour, ternary, image display\r\n\r\n**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series\r\n\r\n**Maps:** scatter maps, choropleth, density maps (geographic visualization)\r\n\r\n**3D Charts:** scatter3d, surface, mesh, cone, volume\r\n\r\n**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge\r\n\r\nFor detailed examples and usage of all chart types, see [reference/chart-types.md](reference/chart-types.md).\r\n\r\n### 2. Layouts and Styling\r\n\r\n**Subplots:** Create multi-plot figures with shared axes:\r\n```python\r\nfrom plotly.subplots import make_subplots\r\nimport plotly.graph_objects as go\r\n\r\nfig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))\r\nfig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)\r\n```\r\n\r\n**Templates:** Apply coordinated styling:\r\n```python\r\nfig = px.scatter(df, x='x', y='y', template='plotly_dark')\r\n# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white\r\n```\r\n\r\n**Customization:** Control every aspect of appearance:\r\n- Colors (discrete sequences, continuous scales)\r\n- Fonts and text\r\n- Axes (ranges, ticks, grids)\r\n- Legends\r\n- Margins and sizing\r\n- Annotations and shapes\r\n\r\nFor complete layout and styling options, see [reference/layouts-styling.md](reference/layouts-styling.md).\r\n\r\n### 3. Interactivity\r\n\r\nBuilt-in interactive features:\r\n- Hover tooltips with customizable data\r\n- Pan and zoom\r\n- Legend toggling\r\n- Box/lasso selection\r\n- Rangesliders for time series\r\n- Buttons and dropdowns\r\n- Animations\r\n\r\n```python\r\n# Custom hover template\r\nfig.update_traces(\r\n    hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'\r\n)\r\n\r\n# Add rangeslider\r\nfig.update_xaxes(rangeslider_visible=True)\r\n\r\n# Animations\r\nfig = px.scatter(df, x='x', y='y', animation_frame='year')\r\n```\r\n\r\nFor complete interactivity guide, see [reference/export-interactivity.md](reference/export-interactivity.md).\r\n\r\n### 4. Export Options\r\n\r\n**Interactive HTML:**\r\n```python\r\nfig.write_html('chart.html')                       # Full standalone\r\nfig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file\r\n```\r\n\r\n**Static Images (requires kaleido):**\r\n```bash\r\nuv pip install kaleido\r\n```\r\n\r\n```python\r\nfig.write_image('chart.png')   # PNG\r\nfig.write_image('chart.pdf')   # PDF\r\nfig.write_image('chart.svg')   # SVG\r\n```\r\n\r\nFor complete export options, see [reference/export-interactivity.md](reference/export-interactivity.md).\r\n\r\n## Common Workflows\r\n\r\n### Scientific Data Visualization\r\n\r\n```python\r\nimport plotly.express as px\r\n\r\n# Scatter plot with trendline\r\nfig = px.scatter(df, x='temperature', y='yield', trendline='ols')\r\n\r\n# Heatmap from matrix\r\nfig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')\r\n\r\n# 3D surface plot\r\nimport plotly.graph_objects as go\r\nfig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])\r\n```\r\n\r\n### Statistical Analysis\r\n\r\n```python\r\n# Distribution comparison\r\nfig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)\r\n\r\n# Box plot with all points\r\nfig = px.box(df, x='category', y='value', points='all')\r\n\r\n# Violin plot\r\nfig = px.violin(df, x='group', y='measurement', box=True)\r\n```\r\n\r\n### Time Series and Financial\r\n\r\n```python\r\n# Time series with rangeslider\r\nfig = px.line(df, x='date', y='price')\r\nfig.update_xaxes(rangeslider_visible=True)\r\n\r\n# Candlestick chart\r\nimport plotly.graph_objects as go\r\nfig = go.Figure(data=[go.Candlestick(\r\n    x=df['date'],\r\n    open=df['open'],\r\n    high=df['high'],\r\n    low=df['low'],\r\n    close=df['close']\r\n)])\r\n```\r\n\r\n### Multi-Plot Dashboards\r\n\r\n```python\r\nfrom plotly.subplots import make_subplots\r\nimport plotly.graph_objects as go\r\n\r\nfig = make_subplots(\r\n    rows=2, cols=2,\r\n    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),\r\n    specs=[[{'type': 'scatter'}, {'type': 'bar'}],\r\n           [{'type': 'histogram'}, {'type': 'box'}]]\r\n)\r\n\r\nfig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)\r\nfig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)\r\nfig.add_trace(go.Histogram(x=data), row=2, col=1)\r\nfig.add_trace(go.Box(y=data), row=2, col=2)\r\n\r\nfig.update_layout(height=800, showlegend=False)\r\n```\r\n\r\n## Integration with Dash\r\n\r\nFor interactive web applications, use Dash (Plotly's web app framework):\r\n\r\n```bash\r\nuv pip install dash\r\n```\r\n\r\n```python\r\nimport dash\r\nfrom dash import dcc, html\r\nimport plotly.express as px\r\n\r\napp = dash.Dash(__name__)\r\n\r\nfig = px.scatter(df, x='x', y='y')\r\n\r\napp.layout = html.Div([\r\n    html.H1('Dashboard'),\r\n    dcc.Graph(figure=fig)\r\n])\r\n\r\napp.run_server(debug=True)\r\n```\r\n\r\n## Reference Files\r\n\r\n- **[plotly-express.md](reference/plotly-express.md)** - High-level API for quick visualizations\r\n- **[graph-objects.md](reference/graph-objects.md)** - Low-level API for fine-grained control\r\n- **[chart-types.md](reference/chart-types.md)** - Complete catalog of 40+ chart types with examples\r\n- **[layouts-styling.md](reference/layouts-styling.md)** - Subplots, templates, colors, customization\r\n- **[export-interactivity.md](reference/export-interactivity.md)** - Export options and interactive features\r\n\r\n## Additional Resources\r\n\r\n- Official documentation: https://plotly.com/python/\r\n- API reference: https://plotly.com/python-api-reference/\r\n- Community forum: https://community.plotly.com/"
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The plotly/ folder contains SKILL.md
        plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/plotly
  exported_at: "2026-11-03T07:40:55.891Z"