Python Bokeh add_layout(): Enhance Plots with Annotations

In data visualization, adding contextual elements like annotations, legends, and titles is crucial for making your plots more informative and professional. Bokeh's add_layout() method provides this functionality.

Understanding add_layout() Basics

The add_layout() method is essential for adding various layout components to your Bokeh plots. It works with different types of annotations including titles, legends, and custom labels.

Before diving into complex examples, let's start with a basic plot setup and learn how to use Bokeh Figure for creating interactive plots.

Adding Titles to Plots

from bokeh.plotting import figure, show
from bokeh.models import Title

# Create a basic plot
p = figure(width=600, height=400)

# Add main title
p.title = Title(text="Main Plot Title", align="center")

# Add additional titles
p.add_layout(Title(text="Top Title", align="center"), "above")
p.add_layout(Title(text="Bottom Title"), "below")

# Add some sample data
p.line([1, 2, 3, 4], [2, 5, 3, 6])

show(p)

Working with Legends

Legends help viewers understand what different elements in your plot represent. The add_layout() method allows you to position legends flexibly.

from bokeh.models import Legend

# Create plot with multiple lines
p = figure(width=600, height=400)

# Add multiple lines with different colors
line1 = p.line([1, 2, 3, 4], [2, 5, 3, 6], line_color="blue", legend_label="Series 1")
line2 = p.line([1, 2, 3, 4], [3, 6, 2, 8], line_color="red", legend_label="Series 2")

# Customize legend position
p.legend.location = "top_left"
p.legend.click_policy = "hide"  # Makes legend items clickable

show(p)

Adding Custom Annotations

Custom annotations can highlight specific points or regions of interest in your visualization. You can combine this with scatter plots for more detailed data representation.

from bokeh.models import BoxAnnotation, Label

p = figure(width=600, height=400)

# Add a box annotation
box = BoxAnnotation(left=1.5, right=2.5, fill_color="yellow", fill_alpha=0.1)
p.add_layout(box)

# Add a text label
label = Label(x=2, y=5, text="Important Region", text_font_size="10pt")
p.add_layout(label)

# Plot data
p.line([1, 2, 3, 4], [2, 5, 3, 6])

show(p)

Combining Multiple Layout Elements

For complex visualizations, you might need to combine different types of layout elements. Let's create a comprehensive example that incorporates multiple features.

from bokeh.models import Arrow, NormalHead, ColumnDataSource

# Create more complex plot
p = figure(width=800, height=600, title="Comprehensive Plot Example")

# Add data
source = ColumnDataSource(data={
    'x': [1, 2, 3, 4],
    'y': [2, 5, 3, 6]
})

# Add main plot elements
line = p.line('x', 'y', source=source, line_width=2)

# Add arrow annotation
arrow = Arrow(end=NormalHead(size=10),
             x_start=2, y_start=4,
             x_end=2, y_end=5)
p.add_layout(arrow)

# Add multiple titles
p.add_layout(Title(text="Subtitle", text_font_style="italic"), "above")
p.add_layout(Title(text="Notes", text_font_size="10pt"), "below")

show(p)

Best Practices and Tips

Layout Positioning: Consider the visual hierarchy when adding multiple elements. Don't overcrowd your plot with too many annotations.

Responsiveness: Remember that layout elements should remain readable when the plot size changes. Test your visualizations at different screen sizes.

You can enhance your plots further by combining with bar charts and other visualization types.

Conclusion

The add_layout() method is a powerful tool for enhancing your Bokeh visualizations. By properly utilizing titles, legends, and annotations, you can create more informative and professional-looking plots.

Remember to maintain a balance between information and clarity, and always consider your audience when adding layout elements to your visualizations.