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How to use bokeh.models

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by vigneshchennai74 dot icon Updated: Aug 24, 2023

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The history of the Bokeh model traces back to the early 20th century when photographers. Artists began experimenting with techniques to achieve captivating visual effects in their images. 

  

The concept of Bokeh evolved. It is leading to its incorporation into modern interactive data visualization. Today, Bokeh stands as a versatile and popular visualization library. The transformation of art into modern data visualization highlights its significance and impact. 

  

In photography, Bokeh comes in different forms. One of them is the circular blur used to highlight the main subject. To the artful creation of out-of-focus backgrounds. By manipulating aperture settings and lens characteristics, Photographers. It can craft different types of Bokeh that add depth and visual interest to their shots. The circular blur catches your eye, and the soft, blurry backgrounds give a dreamy feeling. 

  

Bokeh tracks down flexible applications in photography. It offers a scope of impacts that improve visual narrating. Bokeh makes photos look great and creates a nice atmosphere. Besides, it's useful for underlining the subject by isolating it from the foundation. Using Bokeh, photographic artists can convey profundity. It helps draw consideration and inspire feelings, hoisting the effect of their photos. 

  

To create beautiful bokeh photos, you need technical skills and creative thinking. Understand Bokeh and use tips to enhance its effect. Photographic artists can hoist the visual effects of their work. The capacity to create staggering bokeh photographs upgrades a photographic artist's portfolio. 

  

Overall, the journey of Bokeh from its creative start to its blend is a versatile example. The device inside the Bokeh library highlights its special credits. Bokeh stands apart as an intelligent representation library. That enables clients to make connections with plots and graphs. Its consistent coordination with Python and a wide variety of devices. 

  

Internet browsers are becoming more popular. They have a similar charm and make photos look better with Bokeh. The Bokeh model advances information representation with superior execution intelligence and vivid encounters. 

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

Code

Bokeh is a Python library for creating interactive and visually appealing web-based data visualizations and dashboards.

Bokeh RuntimeError: Models must be owned by only a single document, Selection(id='1057', ...) is already in a doc

Lines of Code : 28dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from bokeh.plotting import figure, show
from bokeh.layouts import row, gridplot
from bokeh.models import ColumnDataSource
from bokeh.io import output_file
import pandas as pd

feature_groups  = [['ciao'],['bye']]

df = pd.DataFrame.from_dict({'x':[0,1,2,3,4], 'y':[2,3,4,5,6]})
x_test = [0,1,2,3,4]
y_test = [2,3,4,5,6]

for features_columns in feature_groups:
    output_file('features_labels' + features_columns[0] +'.html')
    p = []
    source = ColumnDataSource(df)

    for k,f in enumerate(features_columns):
        p_k = figure(title=f)
        p_k.circle(x='x', y='y', line_width=2, source=source,fill_alpha=0.5,line_alpha=0.5)
        p_k.circle_cross( x=x_test, y=y_test, color='red',fill_alpha=0.5,line_alpha=0.5)
        p_k.circle_cross( x = x_test, y = y_test, color='green',fill_alpha=0.5,line_alpha=0.5)
        p_k.xaxis.axis_label = f
        p_k.yaxis.axis_label = 'ki'
        p.append(p_k)
    grid = gridplot(p, ncols=2)
    show(grid)
  1. Download and install VS Code on your desktop.
  2. Open VS Code and create a new file in the editor.
  3. Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
  4. Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
  5. To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac). The output of your code will appear in the VS Code output console.
  6. Paste the Bokeh code into your file in VS Code.
  7. Save the file with a meaningful name and the appropriate file extension for Python use (.py).
  8. Install Bokeh Library; Open your command prompt or terminal.
  9. Type the following command and press Enter: pip install bokeh
  10. Run the Code



I hope you have found this useful. I have added the version information in the following section.


I found this code snippet by searching "Bokeh RuntimeError: Models must be owned by only a single document, Selection(id='1057', ...) is already in a doc " in Kandi. you can try any use case.

Environment Tested

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


  1. The solution is created and tested using Vscode 1.77.2 version
  2. The solution is created in Python 3.7.15 version
  3. The solution is created in bokeh 3.2.1


Using this code, `bokeh` and `pandas` libraries collaborate to generate interactive visualizations of data points, allowing the user to explore and compare features within distinct groups through scatter plots presented in an organized grid layout. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us collaborate to generate interactive visualizations of data points in Python

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FAQ 

1. What is the Bokeh Model? How does it compare to other methods for visualizing data interactively? 

The Bokeh Model is a powerful Python library for interactive data visualization. This program is great for Python users. It has customizable interfaces and versatile graphics. This makes it different from other interactive visualization methods. 

  

2. How can I create a Custom Bokeh Model with an end-user interface? 

To make a special Bokeh Model, use Bokeh's widget library to create interactive parts. That caters to end users' specific needs. 

  

3. What are the advantages of using Python Bokeh for data visualization? 

Python Bokeh is a great choice for creating dynamic and engaging data visualizations. It has real-time interactivity and works well with modern web browsers. 

  

4. Are bar charts supported in Python Bokeh, or do I need another library? 

You can make bar charts with Python Bokeh using the `vbar` and `hbar` glyph methods. You do not need any extra libraries. 

  

5. Can we use a browser's Javascript runtime in Python when using the Bokeh Model? 

Bokeh can't run browser JavaScript, but it has a `CustomJS` callback to use it. 

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