First steps 9: Using widgets#
In the previous first steps guides, you used different sources and structures to import and filter data.
In this section, you will add interactive widgets to your plots.
Widgets are additional visual elements that you can include in your visualization. Use widgets to display additional information or to interactively control elements of your Bokeh document, for example:
This Bokeh document combines a plot with three widgets:
Divwidget to display HTML text
Spinnerwidget to select a numeric value
RangeSliderwidget to adjust a range
See Examples in the user guide for a complete list of Bokeh’s widgets.
Follow these steps to recreate the example from above:
from bokeh.models import Div, RangeSlider, Spinner
Set up your data, your figure, and your renderer:
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] y = [4, 5, 5, 7, 2, 6, 4, 9, 1, 3] p = figure(x_range=(1,9), width=500, height=250) points = p.circle(x=x, y=y, size=30, fill_color="#21a7df")
Divobject and pass it some HTML code as a string:
div = Div( text=""" <p>Select the circle's size using this control element:</p> """, width=200, height=30, )
spinner = Spinner( title="Circle size", # a string to display above the widget low=0, # the lowest possible number to pick high=60, # the highest possible number to pick step=5, # the increments by which the number can be adjusted value=points.glyph.size, # the initial value to display in the widget width=200, # the width of the widget in pixels )
To link the value generated by the spinner to the
sizeproperty of your glyph, use the
"value") that you want to link to your circle glyphs. The second attribute is the glyph you want to link to your spinner (
points.glyph). The third argument is the property of your glyph that you want to link to your spinner’s
spinner.js_link("value", points.glyph, "size")
Create a RangeSlider object:
range_slider = RangeSlider( title="Adjust x-axis range", # a title to display above the slider start=0, # set the minimum value for the slider end=10, # set the maximum value for the slider step=1, # increments for the slider value=(p.x_range.start, p.x_range.end), # initial values for slider )
To link the values generated by the RangeSlider to the existing plot, use the
js_link()function again. This time, however, you need to assign two values at once: the beginning of the plot’s x-axis and the end of its x-axis. The RangeSlider returns a tuple of those two values. Therefore, you need to use the
js_link()function to tell Bokeh which part of the Tuple to assign to either the start or the end of the plot’s x-axis:
range_slider.js_link("value", p.x_range, "start", attr_selector=0) range_slider.js_link("value", p.x_range, "end", attr_selector=1)
Create a layout with all the elements of your dashboard and display it in a browser:
layout = layout([ [div, spinner], [range_slider], [p], ]) show(layout)
This is what the completed example looks like:
from bokeh.layouts import layout from bokeh.models import Div, RangeSlider, Spinner from bokeh.plotting import figure, show # prepare some data x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] y = [4, 5, 5, 7, 2, 6, 4, 9, 1, 3] # create plot with circle glyphs p = figure(x_range=(1, 9), width=500, height=250) points = p.circle(x=x, y=y, size=30, fill_color="#21a7df") # set up textarea (div) div = Div( text=""" <p>Select the circle's size using this control element:</p> """, width=200, height=30, ) # set up spinner spinner = Spinner( title="Circle size", low=0, high=60, step=5, value=points.glyph.size, width=200, ) spinner.js_link("value", points.glyph, "size") # set up RangeSlider range_slider = RangeSlider( title="Adjust x-axis range", start=0, end=10, step=1, value=(p.x_range.start, p.x_range.end), ) range_slider.js_link("value", p.x_range, "start", attr_selector=0) range_slider.js_link("value", p.x_range, "end", attr_selector=1) # create layout layout = layout( [ [div, spinner], [range_slider], [p], ] ) # show result show(layout)
To learn more about Bokeh’s widgets and building interactive dashboards, see Adding widgets in the user guide.
To learn about creating interactive legends, see interactive legends in the user guide.
To learn more about combining widgets and plots into layouts, see Creating layouts in the user guide.
Using widgets with a Bokeh server#
Use this command on a command line to start Bokeh in server mode:
You can use Bokeh Server to build complex dashboards and interactive applications. Some of the capabilities of the Bokeh server include:
UI widgets and plot selections that drive computations and plot updates
Streaming live data to automatically updating plots
Intelligent server-side downsampling of large datasets
Sophisticated glyph re-writing and transformations for “Big Data”
Plot and dashboard publishing for wider audiences
This is an example of a simple Bokeh server app. Adjust the sliders in the widgets on the left to change the sine wave on the right:
Find the source code for this Bokeh server app in the Bokeh repository on GitHub. For more examples of Bokeh server applications, see the Server App Examples section of the Gallery.
For information on using the server and writing Bokeh server plots and apps, see Running a Bokeh server in the user guide.
For more examples of Bokeh server applications, see the Server App Examples section of the Gallery.
An easy way to create more complex dashboards is to use Panel (https://panel.holoviz.org). Panel is a separate Python library that uses Bokeh and Bokeh server in the background.
Congratulations, you have completed all of Bokeh’s first steps guides!
More information about Bokeh is available in the user guide. For more in-depth information about all aspects of Bokeh, see the reference guide.
For more examples of what you can do with Bokeh, check the Gallery and the examples directory in Bokeh’s GitHub repository.
Finally, on the Bokeh community page, you can find links and information about asking for help and contributing to Bokeh and the Bokeh community.