/plotly

name: plotly
description: Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).

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](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](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](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](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]