Creating Interactive Graphs with Bokeh in Python

Learn how to plot mouse-hovering interactive graphs using `Bokeh` in Python. This guide breaks down the steps to create step function graphs with tooltips for deeper insights.
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Creating Interactive Graphs with Bokeh in Python: A Beginner's Guide

Are you looking to visualize data in Python with interactive graphs? Bokeh is an excellent library for this purpose, allowing you to create stunning visualizations easily. In this guide, we will explore how to plot a mouse-hovering interactive graph in Python using Bokeh. We'll guide you step-by-step, so even if you're just starting out with Bokeh, you'll be able to follow along comfortably.

The Problem

Imagine you have three data lists representing time, events, and their corresponding values, and you want to create a time series graph. You wish to have:

A continuous series on the x-axis representing time (x list).

Marked values on the y-axis which correspond to different categorical events (y list).

Tooltips that pop up when you hover over the plotted points, displaying the corresponding value from the c list.

Let’s examine how we can achieve this using Bokeh.

The Solution

To create the desired interactive graph, we will be using Bokeh’s plotting functionalities. Here is a step-by-step breakdown of the solution:

Step 1: Import Necessary Libraries

First, we need to import the necessary libraries to create our plot.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Prepare Your Data

Define your data in lists. Here, we have three lists for x, y, and c:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Set Up the Figure

Create a new figure using Bokeh’s figure() method. You can customize the aesthetics like title, height, width, etc.

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Step 4: Customize Grid and Axes

You might want to turn off the grid lines or customize the axes. Here’s how:

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Step 5: Plot Lines and Circles

We need to plot our data points as both a line and as circles. The circles will serve as the hover points.

[[See Video to Reveal this Text or Code Snippet]]

Step 6: Set Tooltips for Circles

To limit tooltips to show only when hovering over specific points, we tell Bokeh to use circles as the only hover renderers.

[[See Video to Reveal this Text or Code Snippet]]

Step 7: Display the Plot

Finally, use the show() function to display your interactive graph.

[[See Video to Reveal this Text or Code Snippet]]

Complete Code Example

Putting everything together, here’s the complete code:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Creating interactive graphs with hover capabilities in Python using Bokeh is a straightforward process that can significantly enhance your data visualization. By following the steps outlined in this guide, you can display your data dynamically, making it more engaging and informative.

Now, get started with your own datasets and start visualizing!