Plotly

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

Quick Start

Install Plotly:

uv pip install plotly

Basic usage with Plotly Express (high-level API):

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:

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](https://github.com/davila7/claude-code-templates/blob/HEAD/cli-tool/components/skills/scientific/plotly/reference/chart-types.md).

### 2. Layouts and Styling

**Subplots:** Create multi-plot figures with shared axes:

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:

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](https://github.com/davila7/claude-code-templates/blob/HEAD/cli-tool/components/skills/scientific/plotly/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

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](https://github.com/davila7/claude-code-templates/blob/HEAD/cli-tool/components/skills/scientific/plotly/reference/export-interactivity.md).

### 4. Export Options

**Interactive HTML:**

fig.write_html('chart.html') # Full standalone

fig.write_html('chart.html', include_plotlyjs='cdn') # Smaller file

**Static Images (requires kaleido):**

uv pip install kaleido

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](https://github.com/davila7/claude-code-templates/blob/HEAD/cli-tool/components/skills/scientific/plotly/reference/export-interactivity.md).

## Common Workflows

### Scientific Data Visualization

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

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

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

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):

uv pip install dash

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)