Mastering Plotly Scatter Plots: Points and Lines

Scatter plots are essential tools for visualizing relationships between variables. In this guide, we'll explore how to create interactive scatter plots using Plotly's fig.add_scatter() method.

Understanding fig.add_scatter() Basics

The add_scatter() method is a versatile function in Plotly that allows you to create scatter plots with points, lines, or both. If you're new to Plotly, you might want to check out Mastering Plotly go.Figure() for Interactive Plotting first.

Creating a Simple Scatter Plot

import plotly.graph_objects as go
import numpy as np

# Generate sample data
x = np.linspace(0, 10, 20)
y = np.random.randn(20)

# Create figure
fig = go.Figure()

# Add scatter trace
fig.add_scatter(x=x, y=y, mode='markers', name='Random Points')

# Show the plot
fig.show()

Customizing Marker Appearance

You can customize markers using various parameters like size, color, and symbol. Let's explore these options to create more visually appealing plots.

# Create scatter plot with custom markers
fig = go.Figure()
fig.add_scatter(
    x=x,
    y=y,
    mode='markers',
    marker=dict(
        size=12,
        color='red',
        symbol='circle',
        line=dict(color='black', width=2)
    ),
    name='Customized Points'
)

Adding Lines to Scatter Plots

The mode parameter in add_scatter() allows you to add lines, points, or both. For more layout customization, check out Plotly Update Layout: Customize Figure Appearance.

# Create scatter plot with lines and markers
fig = go.Figure()
fig.add_scatter(
    x=x,
    y=y,
    mode='lines+markers',
    line=dict(color='blue', width=2),
    marker=dict(size=8, color='red'),
    name='Lines and Points'
)

Multiple Traces in One Plot

You can add multiple scatter traces to create comparative visualizations. This is particularly useful when comparing different datasets or trends.

# Generate two datasets
y2 = np.random.randn(20) + 2

fig = go.Figure()

# Add first trace
fig.add_scatter(x=x, y=y, mode='lines', name='Dataset 1')

# Add second trace
fig.add_scatter(x=x, y=y2, mode='markers', name='Dataset 2')

# Update layout
fig.update_layout(
    title='Multiple Traces Example',
    xaxis_title='X Axis',
    yaxis_title='Y Axis'
)

Advanced Customization Options

For more complex visualizations, you might want to explore Mastering Plotly add_trace for additional customization options.

# Create advanced scatter plot
fig = go.Figure()
fig.add_scatter(
    x=x,
    y=y,
    mode='markers',
    marker=dict(
        size=y*10,  # Dynamic marker size
        color=x,    # Color based on x values
        colorscale='Viridis',
        showscale=True
    ),
    hovertemplate='X: %{x}
Y: %{y}',
    name='Dynamic Visualization'
)

# Update layout with more customization
fig.update_layout(
    title={
        'text': 'Advanced Scatter Plot',
        'x': 0.5,
        'xanchor': 'center'
    },
    showlegend=True,
    hovermode='closest'
)

Best Practices and Tips

When creating scatter plots with Plotly, consider these important practices:

• Use appropriate marker sizes for your dataset scale
• Choose contrasting colors for multiple traces
• Include meaningful legends and axis labels
• Optimize hover information for better interactivity

Conclusion

The fig.add_scatter() method is a powerful tool for creating interactive scatter plots in Plotly. Whether you need simple point plots or complex visualizations, it provides the flexibility and features needed.

Remember to experiment with different parameters and combinations to create the most effective visualizations for your data storytelling needs.