Bokeh is a powerful Python library for creating interactive visualizations. One of its key features is its ability to handle user interactions with the plots. In this blog post, we will explore how to log and analyze user interactions in Bokeh.

Logging User Interactions

Logging user interactions can be useful for understanding how users are engaging with your visualizations. Bokeh provides a built-in logging feature that allows you to capture user interactions such as mouse clicks, hover events, and selections.

To enable logging, you need to import the bokeh.events module and then create a CustomJS callback that logs the event data. Here is an example:

from bokeh.plotting import figure, show
from bokeh.events import Tap

plot = figure()
plot.circle([1, 2, 3], [4, 5, 6])

def callback(event):
    print("User interaction:", event)

plot.js_on_event(Tap, CustomJS(code="console.log('tap event:', cb_obj.event);"))

show(plot)

In the above code snippet, we create a simple scatter plot with circles and define a callback function callback that prints out the event data. We then attach the Tap event to the plot using the js_on_event method and pass the CustomJS callback.

When you run the code and interact with the plot by clicking on the circles, you will see the event data logged in the console.

Analyzing User Interactions

Once you have logged the user interactions, you can analyze the data to gain insights into how users are interacting with your visualizations. This can help you make informed decisions for improving the user experience and optimizing your visualizations.

Here are a few examples of analyses you can perform on the logged data:

Counting User Interactions

You can count the number of times each type of user interaction occurs. For example, you can tally the number of mouse clicks, hover events, and selections. This will give you an overview of the most popular interaction types.

Tracking User Paths

By analyzing the sequence of user interactions, you can track the paths users take when exploring your visualizations. This can help you identify common navigation patterns and areas of interest within your plots.

Segmenting User Interactions

You can segment the user interactions based on different attributes such as user demographics or the specific visualization they interacted with. This can help you understand if certain groups of users have different preferences or behaviors.

Identifying Problematic Interactions

Analyzing the logged data can help you identify any problematic user interactions. For example, you may notice that certain tooltips are not being triggered often or that users are frequently selecting areas outside the intended regions. This information can guide you in making design improvements.

Bokeh provides various tools and techniques to help you analyze user interactions effectively. You can leverage the power of Python libraries such as Pandas and NumPy to perform advanced analyses on the logged data.

In conclusion, logging and analyzing user interactions in Bokeh can provide valuable insights into how users engage with your visualizations. By understanding user behavior, you can enhance the user experience and optimize your visualizations for maximum impact.

Happy logging and analyzing!