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 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:

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:

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.

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.

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.

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)

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/