Plotly

Overview

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

Quick Start

pip install plotly

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

Plotly Express (px) -- High-Level API

Use for quick, standard visualizations with sensible defaults:

• Working with pandas DataFrames
• Creating common chart types (scatter, line, bar, histogram, etc.)
• Automatic color encoding and legends
• Minimal code (1-5 lines)

Graph Objects (go) -- Low-Level API

Use 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
• Precise control over individual components
• Specialized visualizations with custom shapes and annotations

Plotly Express returns Graph Objects figures, 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

Chart Types

Core Capabilities

1. Layouts and Styling

Subplots 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 for coordinated styling:

fig = px.scatter(df, x='x', y='y', template='plotly_dark')
# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white

Customization options: colors (discrete sequences, continuous scales), fonts, axes (ranges, ticks, grids), legends, margins, annotations, and shapes.

2. 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')

3. 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):

pip install kaleido

fig.write_image('chart.png')   # PNG
fig.write_image('chart.pdf')   # PDF
fig.write_image('chart.svg')   # SVG

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)

Graph Objects Core Classes

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=[1, 2, 3, 4], y=[10, 11, 12, 13],
    mode='lines+markers', name='Trace 1',
    line=dict(color='red', width=2, dash='dash'),
    marker=dict(size=10, color='blue', symbol='circle')
))

Plotly Express Common Parameters

fig = px.scatter(
    df, x="x", y="y",
    width=800, height=600,                            # Dimensions
    title="Figure Title",                              # Title
    labels={"x": "X Axis", "y": "Y Axis"},           # Axis labels
    color="category",                                  # Color by category
    color_discrete_map={"A": "red", "B": "blue"},     # Custom color mapping
    color_continuous_scale="Viridis",                  # Continuous scale
    category_orders={"category": ["A", "B", "C"]},    # Category ordering
    template="plotly_dark"                             # Theme template
)

Integration with Dash

For interactive web applications, use Dash (Plotly's web app framework):

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)