Creating a simple Range Slider in Bokeh

Bokeh is a powerful data visualization library in Python that helps to create interactive and unique visualizations for the web. Bokeh supports various rendering techniques and provides a wide range of built-in tools for creating complex visualizations with multiple glyphs. This document will guide you through the process of creating a plot with multiple glyphs using Bokeh. This plot combines different glyphs to display multiple data series in a single plot that provides a more efficient way to understand the relationship between different variables.

What are the key benefits of range slider?

• Interactive − RangeSlider provides an interactive way for users to adjust the range of a plot, which can be particularly useful for exploring data and identifying trends.

• Range control − The RangeSlider allows users to control the range of the data displayed on a plot, making it easier to zoom in on specific areas of interest.

• Easy to use − RangeSlider is easy to use and can be added to a Bokeh plot with just a few lines of code.

• Customizable − RangeSlider can be customized in terms of appearance and behavior, allowing developers to create sliders that fit their specific needs.

• Integration with other widgets − RangeSlider can be integrated with other Bokeh widgets to create more complex and interactive visualizations.

Prerequisites

Before we dive into the task few things should is expected to be installed onto your system −

List of recommended settings −

• pip install pandas, bokeh

• It is expected that the user will have access to any standalone IDE such as VS-Code, PyCharm, Atom or Sublime text.

• Even online Python compilers can also be used such as Kaggle.com, Google Cloud platform or any other will do.

• Updated version of Python. At the time of writing the article I have used 3.10.9 version.

• Knowledge of the use of Jupyter notebook.

• Knowledge and application of virtual environment would be beneficial but not required.

• It is also expected that the person will have a good understanding of statistics and mathematics.

Step-by-Step Guide

Importing Bokeh and relevant libraries

We start by importing the necessary libraries. We will be using `bokeh.plotting` to create a figure and `bokeh.layouts` to arrange the slider and the figure.

Syntax

from bokeh.plotting import figure
from bokeh.layouts import layout
import numpy as np
from bokeh.models import Range1d
from bokeh.models.widgets import RangeSlider
from bokeh.io import show

Creating Data

Create data for the slider by defining start and end values for the range. We will use a NumPy array to specify the range values.

Syntax

start = 1
end = 10
values = np.arange(start, end+1, 1)

Creating a RangeSlider

Now, we will create a RangeSlider object by passing in the range values, initial range and step size. We will set the minimum and maximum range values for the slider.

Syntax

range_slider = RangeSlider(start=start, end=end, step=1, value=(start, end))

Creating a Figure

Next, we create a figure object and add a line graph. We will then specify the range and domain of the line graph and position it on the page using a layout object.

Syntax

plot = figure()
plot.line(x=[1,2,3], y=[1,2,3], line_width=2)
plot.x_range = Range1d(1, 3)
plot.y_range = Range1d(-1, end+1)

Creating a Layout

Finally, we arrange the slider and the figure using a layout object.

Syntax

layout = layout([[plot], [range_slider]])

Displaying the Slider and Figure

To view the slider and the figure, we use the `show` function.

Syntax

show(layout)yout = layout([[plot], [range_slider]])

Example

from bokeh.plotting import figure
from bokeh.layouts import layout
import numpy as np

start = 1
end = 10
values = np.arange(start, end+1, 1)

from bokeh.models.widgets import RangeSlider

range_slider = RangeSlider(start=start, end=end, step=1, value=(start, end))
plot = figure()
plot.line(x=[1,2,3], y=[1,2,3], line_width=2)
plot.x_range = range_slider
plot.y_range = (-1, end+1)

layout = layout([[plot], [range_slider]])
from bokeh.io import show
show(layout)

The output for the above code snippet will produce a range slider with the values 1 to 10.

Final Program,Code

from bokeh.plotting import figure
from bokeh.layouts import layout
import numpy as np
from bokeh.models import Range1d
from bokeh.models.widgets import RangeSlider
from bokeh.io import show

start = 1
end = 10
values = np.arange(start, end+1, 1)

range_slider = RangeSlider(start=start, end=end, step=1, value=(start, end))
plot = figure()
plot.line(x=[1,2,3], y=[1,2,3], line_width=2)
plot.x_range = Range1d(1, 3)
plot.y_range = Range1d(-1, end+1)

layout = layout([[plot], [range_slider]])
show(layout)

Output

In the above section we can see the output of the range slider in bokeh and also example for better understanding.

Conclusion

In this tutorial, you have learned how to create a simple range slider in Bokeh. You created a RangeSlider, a Figure, and a Layout, to display a range of values in a line chart while allowing the user to select a range of values. Bokeh provides a lot of flexibility and customization options to create dynamic and interactive data visualizations. This tutorial serves as a starting point to build more complex interactive visualizations with Bokeh.