alterlab-plotly

Plotly

Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types. Examples target plotly >= 6 and run on plotly 7.1 (plotly.js 4); see "Plotly 7 changes" below if you are upgrading older code.

When to Use This Skill

Use this skill when the chart must be explored or shared interactively:

• Hover tooltips, zoom/pan, legend toggling, rangesliders, dropdowns, or animation frames
• Standalone HTML or web-embedded figures, and Dash dashboards
• Rotatable 3D plots (surface, scatter3d, mesh, volume)
• Exploratory analysis where readers need to inspect individual points

Does NOT Trigger

Scenario	Use Instead
Static, print-ready figure with no interactivity	alterlab-matplotlib
Journal-formatted multi-panel figure (column widths, Okabe-Ito palette, significance stars)	alterlab-scientific-viz
Quick static statistical plot from a DataFrame (heatmap, pair plot, distributions)	alterlab-seaborn
Flowchart, pathway, or architecture diagram	alterlab-scientific-schematics / alterlab-mermaid

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 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 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, MapLibre-backed; the *_mapbox functions and traces were removed in plotly 7)

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

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

For detailed examples and usage of all chart types, see references/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 references/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 references/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 >= 1 and Chrome):

uv pip install kaleido
plotly_get_chrome    # one-time: installs a Chrome build if the machine has none

fig.write_image('chart.png', scale=3)   # PNG (700x500 default size -> 2100x1500 px)
fig.write_image('chart.pdf')            # PDF
fig.write_image('chart.svg')            # SVG

Without Chrome, write_image raises "Kaleido requires Google Chrome to be installed". Set export defaults with plotly.io.defaults (e.g. pio.defaults.default_scale = 2); the old pio.kaleido.scope settings no longer exist.

For complete export options, see references/export-interactivity.md.

Common Workflows

Scientific Data Visualization

import plotly.express as px

# Scatter plot with trendline (trendline='ols'/'lowess' requires statsmodels:
#   uv pip install statsmodels)
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(debug=True)  # Dash >= 3 (current 4.x): run_server() was removed; use app.run()

Plotly 7 changes (August 2026)

Plotly 7.0 moved to plotly.js 4 and removed long-deprecated APIs. When older code fails:

• px.scatter_mapbox / density_mapbox / choropleth_mapbox / line_mapbox and the go.*mapbox traces are gone — use px.scatter_map, px.density_map, px.choropleth_map, px.line_map (MapLibre, map_style=, no token).
• Deprecated figure-factory functions were removed (create_annotated_heatmap, create_distplot, create_gantt, create_facet_grid, create_scatterplotmatrix, create_violin, create_candlestick, create_ohlc, and others). Use Plotly Express instead: px.imshow(z, text_auto=True), px.histogram(..., marginal='rug'), px.timeline, px.scatter_matrix, px.violin, go.Candlestick/go.Ohlc. ff.create_dendrogram, create_quiver, create_streamline, create_table, create_trisurf, and create_ternary_contour remain.
• Static export supports only Kaleido >= 1 (no Orca), and the engine= argument to write_image/to_image was removed.
• Color strings: rgb()/rgba() with 0–1 fractional channels and hsv() are no longer supported; newer forms such as rgba(255 0 0 / 0.5), #ff0000aa, and oklch() now work.
• New go.Quiver trace for vector fields.

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/