---
name: plotly-74e9b4e39941
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.
license: MIT license
metadata:
    skill-author: K-Dense Inc.
risk: unknown
source: community
---

# Plotly

Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types.

## When to Use
- You need interactive charts with hover, zoom, pan, or web embedding.
- You are building dashboards, exploratory analysis notebooks, or presentations that benefit from rich interaction.
- You want to choose between Plotly Express and Graph Objects for the same visualization task.

## 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 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 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.

### 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.

### 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.

### 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.

## 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** - High-level API for quick visualizations
- **graph-objects.md** - Low-level API for fine-grained control
- **chart-types.md** - Complete catalog of 40+ chart types with examples
- **layouts-styling.md** - Subplots, templates, colors, customization
- **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/

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.


---
Source attribution: ASoudy
Original project: https://github.com/ASoudy/AGS/blob/cd1d94757d590093507ef8ac26299eab8bc20478/AGAS/plugins/antigravity-awesome-skills-claude/skills/plotly/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Antigravity User

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: ASoudy
Original project: https://github.com/ASoudy/AGS/blob/cd1d94757d590093507ef8ac26299eab8bc20478/AGAS/plugins/antigravity-awesome-skills-claude/skills/plotly/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Marketing, Product Management
Roles: Data Analyst, Data Scientist, Frontend Developer, Marketing Analyst, Product Manager
Specialties: API, Backend, Cross Platform, Frontend
Tools: Plotly
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: eae58bd64404adfc4fa125d29886e4e6ce79f9387a10ebd84516c4839ba19ba2