Bokeh is a powerful data visualization library in Python that allows you to create interactive and visually appealing plots. It provides a wide range of tools and features to enhance your data visualization experience. One such tool is the Bokeh brush and data selection tool, which enables users to selectively interact with specific data points or regions on a plot.

Brush Tool

The brush tool in Bokeh allows users to select data points by dragging a brush region over the plot. This is particularly useful when you want to analyze specific subsets of data or identify patterns within certain regions. To enable the brush tool in Bokeh, you can use the BoxSelectTool or LassoSelectTool from the bokeh.models module.

For example, let’s say we have a scatter plot of x and y coordinates and we want to select a specific cluster of points using the brush tool. Here’s how you can do it:

from bokeh.plotting import figure, show
from bokeh.models import BoxSelectTool
from bokeh.sampledata.iris import flowers

# Create a scatter plot
p = figure(title="Iris Flower Scatter Plot", plot_width=800, plot_height=400)

# Add data points to the plot
p.circle(x='petal_length', y='sepal_length', source=flowers, size=8)

# Enable the brush tool
p.add_tools(BoxSelectTool())

# Display the plot
show(p)

The above code demonstrates how to enable the BoxSelectTool on a scatter plot of Iris flower data. Once you run the code, you can click and drag the mouse to create a rectangular selection region around the desired data points.

Data Selection Tool

Bokeh also provides a data selection tool that allows users to interactively select and highlight specific regions of data on a plot. This is particularly useful when you want to analyze specific trends or compare subsets of data. To enable the data selection tool in Bokeh, you can use the TapTool or HoverTool from the bokeh.models module.

For example, let’s say we have a line plot of time series data and we want to highlight specific sections of the plot based on user interaction. Here’s how you can do it:

from bokeh.plotting import figure, show
from bokeh.models import TapTool, BoxEditTool
import numpy as np

# Generate some random time series data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create a line plot
p = figure(title="Time Series Data", plot_width=800, plot_height=400)
p.line(x, y)

# Enable the data selection tool
p.add_tools(TapTool(renderers=[p.renderers[0]], callback=None))

# Display the plot
show(p)

In the above code, we’ve enabled the TapTool on a line plot of random time series data. By clicking on different parts of the plot, you can interactively highlight specific regions.

Conclusion

The brush and data selection tools in Bokeh offer powerful capabilities to analyze and interact with data on your plots. By enabling these tools, you can easily explore subsets of data, identify patterns, and compare different regions of interest. Bokeh’s interactivity features make it an excellent choice for creating dynamic and engaging visualizations in Python.