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How to use the widgets in Bokeh

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by vinitha@openweaver.com dot icon Updated: Aug 23, 2023

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A Bokeh widget refers to an interactive GUI element the Bokeh library provides. It is a popular Python library for data visualization.


Designers create Bokeh for web development to create interactive interface visualizations. Users can interact with data visualizations using Bokeh widgets in real time. They offer interactive controls and input options used to change the displayed data.  

 

Some common Bokeh widgets include:  

  • bokeh sliders  
  • simple buttons  
  • dropdown menus  
  • text inputs  
  • text arguments  
  • checkboxes and more.  

 

Users use bokeh widgets for interactive data visualization. It allows users to control and explore data plots in web-based bokeh applications. There are various bokeh effects in photography and video.  

 

Some common bokeh styles include: 

  • Soft Bokeh  
  • Circular Bokeh  
  • Hexagonal or Polygonal Bokeh  
  • Creamy Bokeh  
  • Busy or Busy Bokeh  
  • Dramatic Bokeh  
  • Custom Bokeh Shapes  

 

You can use photography techniques to capture Bokeh in different scenarios: 

  • Bokeh in Portraits (Photos of People)  
  • Bokeh in Still Life and Macro Photography  
  • Bokeh in Landscape Photography  

 

Smartphones have small image sensors compared to DSLRs or mirrorless cameras. In Bokeh, single-selection and multi-selection widgets are interactive elements. Users can use new plots and interactive charts to interact with the data frame display area. These widgets enable users to select specific data points, data providing exploratory experience. 

 

In conclusion, Bokeh is a powerful tool for photographers. It helps them create stunning images and enhance compositions. The concept of Bokeh remains highly sought-after among the first smartphone photographers.  

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we are using bokeh library

linking JS bokeh widget

PythondotLines of Code : 18dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from bokeh.io import show
from bokeh.layouts import column
from bokeh.models import CustomJS, Select
from bokeh.models.widgets import Slider

options = ['a', 'b', 'c']

init_idx = 0
select = Select(title="Option:", options=options, value=options[init_idx])
slider = Slider(title="Index", value=init_idx, start=0, end=len(options) - 1, step=1)

select.js_on_change('value', CustomJS(args=dict(slider=slider),
                                      code="slider.value = cb_obj.options.indexOf(cb_obj.value);"))
slider.js_on_change('value', CustomJS(args=dict(select=select),
                                      code="select.value = select.options[cb_obj.value];"))

show(column(select, slider))

Instructions


Follow the steps carefully to get the output easily.


  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install bokeh- pip install bokeh
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your Python file.
  6. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for ' How to use the widgets in Bokeh ' in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.11.1 Version
  3. bokeh 3.1.1 Version



Using this solution, we can use the widgets in Bokeh in simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use the widgets in Bokeh.

Dependent Library


bokehby bokeh

Python doticonstar image 17667 doticonVersion:Currentdoticon
License: Permissive (BSD-3-Clause)

Interactive Data Visualization in the browser, from Python

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            bokehby bokeh

            Python doticon star image 17667 doticonVersion:Currentdoticon License: Permissive (BSD-3-Clause)

            Interactive Data Visualization in the browser, from Python
            Support
              Quality
                Security
                  License
                    Reuse

                      You can search for any dependent library on Kandi like ' bokeh'.

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                      2. For further learning resources, visit the Open Weaver Community learning page


                      FAQ:  

                      1. What is the end-user interface of Bokeh Widgets?  

                      The end-user interface of Bokeh widgets is a web-based GUI. It allows users to interact with data visualizations dynamically. You can add bokeh widgets to web apps or use them in HTML pages. It provides a user-friendly way to explore and analyze data.  

                       

                      2. How can I use a data table widget to create an interactive dashboard?  

                      Bokeh provides a Data Table widget that displays data in a tabular format. You can combine it with other Bokeh widgets to create a fully interactive dashboard.  

                       

                      Here's a step-by-step guide to building an interactive dashboard with a data table:   

                      • Install Bokeh   
                      • Import Required Libraries   
                      • Create or load the data you want to display in the data table.   
                      • Create a Column DataSource   
                      • Create a Data Table   
                      • Add Interactivity   
                      • Arrange the Widgets   
                      • Show the Dashboard   
                      • Run the Dashboard  

                       

                      3. What are Bokeh - Annotations, and how do they work?  

                      Bokeh annotations are graphical elements used to enhance data visualizations. Using labels and shapes helps people understand data by highlighting important points. These annotations help users see patterns in the data. It makes the visualizations more informative and insightful.   

                       

                      Bokeh provides a range of annotation types, including:   

                      • Labels  
                      • Arrows   
                      • Box Annotations   
                      • Span Annotations and more  

                       

                       4. How can I create interactive dashboards with Bokeh Widgets?  

                      Bokeh provides a high-level API for creating interactive web-based dashboards. Here's a step-by-step guide to building an interactive dashboard with Bokeh widgets:  

                      • Install Bokeh  
                      • Import Required Libraries  
                      • Prepare Your Data  
                      • Create a Column DataSource  
                      • Create Interactive Widgets  
                      • Create Data Visualization  
                      • Arrange Widgets and Visualization  
                      • Run the Dashboard  

                       

                      5. Is there a library that supports using Bokeh Widgets in Jupyter Notebooks?  

                      Yes, there is a library designed to support the usage of Bokeh widgets in Jupyter Notebooks. They call the library "Bokeh Server for Jupyter". It allows you to create interactive Bokeh visualizations with widgets. It works directly within Jupyter Notebook environments. 

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