Python Bokeh Layout: Organize Plots with row() & column()

When creating complex data visualizations with Bokeh, organizing multiple plots effectively is crucial. The row() and column() functions provide powerful tools for arranging plots in horizontal and vertical layouts.

Understanding Basic Layout Functions

Bokeh's layout functions help create sophisticated dashboard-like visualizations. These functions are essential for presenting multiple plots in a clean, organized manner that enhances data interpretation.

Let's explore how to use these layout functions with practical examples. First, we'll need to import the necessary modules:

from bokeh.layouts import row, column
from bokeh.plotting import figure, show
import numpy as np

# Create sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.tan(x)

# Create three different plots
p1 = figure(width=300, height=300, title="Sine")
p1.line(x, y1, line_color="blue")

p2 = figure(width=300, height=300, title="Cosine")
p2.line(x, y2, line_color="red")

p3 = figure(width=300, height=300, title="Tangent")
p3.line(x, y3, line_color="green")

Creating Horizontal Layouts with row()

The row() function arranges plots horizontally. This is particularly useful when you want to compare multiple plots side by side.

# Arrange plots horizontally
layout = row(p1, p2, p3)
show(layout)

Creating Vertical Layouts with column()

For vertical arrangements, use the column() function. This works well when you want to stack plots on top of each other.

# Arrange plots vertically
layout = column(p1, p2, p3)
show(layout)

Combining row() and column() for Complex Layouts

You can create more complex layouts by nesting row() and column() functions. This allows for flexible grid-like arrangements.

# Create a complex layout
layout = column(
    row(p1, p2),
    p3
)
show(layout)

Customizing Layout Spacing

Both layout functions accept spacing parameters to fine-tune the appearance. You can adjust the gap between plots using the spacing parameter.

# Add custom spacing
layout = row(p1, p2, p3, spacing=20)  # 20 pixels between plots
show(layout)

Responsive Layouts

For responsive designs that adapt to different screen sizes, you can use the sizing_mode parameter. This is particularly useful for web applications.

# Create responsive layout
layout = row(p1, p2, p3, sizing_mode="stretch_width")
show(layout)

Best Practices and Tips

When working with layouts, consider these important points:

• Keep plot sizes consistent within layouts for better visual appeal
• Use appropriate spacing to prevent overcrowding
• Consider the overall aspect ratio of your final visualization

For more advanced styling options, you might want to check out Python Bokeh Theme: Style Plots with Custom Themes.

To enhance your layouts with interactive features, explore Python Bokeh HoverTool: Add Interactive Tooltips Guide.

Conclusion

The row() and column() functions in Bokeh provide flexible tools for creating professional-looking visualizations. By combining these layouts, you can create complex, interactive dashboards.

Remember to consider your data presentation needs when choosing between horizontal and vertical layouts, and don't hesitate to experiment with nested combinations for more sophisticated arrangements.