How to use themes in bokeh

Bokeh Themes is a feature in the Bokeh library, which is a Python data visualization library. You can change the appearance of your visualizations by using Bokeh Themes. You can use it to make your data visualizations look attractive and consistent. This enhances the look and feel of your interactive web-based plots and applications. This feature enables you to create coherent and pleasing data representations. The Bokeh library offers various types of themes. Visualizations in an application can apply those themes.  

Some of these themes include:  

• Default Theme: Bokeh's default theme provides a clean and simple look for visualizations. Using a white background and basic styling accomplishes it.  
• Dark Theme: This offers a dark background with light-colored text and plots. This is suitable for applications that focus on data presentation in low-light environments.  
• Light Theme: This theme uses light backgrounds and darker text. You can make it easier to see by creating a more traditional look in well-lit places.  
• Minimal Theme: This provides a stripped-down, minimalistic appearance by reducing visual elements. This results in cleaner and more focused visualization.  
• Business Theme: It is designed for creating professional-looking visualizations. A balanced color palette and layout accomplish this. This layout is suitable for presentations or reports.  
• Modern Theme: This offers a sleek and contemporary appearance. Elements like bold typography and vibrant colors incorporate that.  
• Gradient Theme: This theme introduces gradients to the backgrounds and other elements. Those add a dynamic and engaging aspect to the visualizations.  
• Classic Theme: This offers traditional styling with neutral colors and design elements.  
• Custom Themes: You can build custom themes by specifying attributes. Such as colors, fonts, and other visual properties to match your app's branding.  

In apps, creators use several techniques to create bokeh effects. 

• CSS and Styling: One common approach is to use CSS and styling to create bokeh effects.   
• Background Images: Incorporating background images with bokeh patterns can create a realistic effect.   
• JavaScript Libraries: Bokeh.js provide interactive and dynamic bokeh effects.   
• CSS can apply blur filters to certain UI elements to mimic bokeh characteristics.   
• Canvas and SVG: You can create bokeh-like patterns and shapes using canvas elements.   
• Transparency and Layers: Simulates the layered depth of field in bokeh photography.   
• Particle Effects: Implementing particle systems can create dynamic effects where particles move around.   
• WebGL Shaders: For more advanced and intricate bokeh effects, use WebGL shaders. You can render lights and blurs with greater control.   

The Bokeh library is a powerful tool for creating interactive visualizations. People use it to apply bokeh effects. Here are some tips for using the Bokeh Themes library to enhance the bokeh effects in your apps:  

• Importing the Necessary Modules: Make sure you have imported the required modules.   
• Creating a Theme: Use the theme function to create and apply a theme to your Bokeh plot.   
• To customize the theme, you can change the design's colors, fonts, and lines.   
• Defining Visual Elements: You can adjust their properties to create an appealing composition.  
• Applying Transitions: This can help create dynamic bokeh effects.   
• Incorporating Layouts: This can enhance the aesthetic and interactivity of your application.  
• Responsive Design: Ensure that your bokeh effects and visuals are responsive. It is across different screen sizes and devices.   
• Documentation and Examples: Take advantage of the official Bokeh documentation and examples.   

The Bokeh Themes library is a powerful tool. Users use these tools to customize the visual appearance of Bokeh plots and apps. Its main points include:  

• Customization: Allows you to change the look and feel of your plots and apps. Modifying elements like fonts, colors, backgrounds, and more, do this.  
• Consistency: With themes, you can maintain a consistent design. Many plots and apps do this, enhancing the user experience and brand identity.  
• Ease of Use: Applying a theme is as simple as importing it and using the curdoc().theme method. This makes experimenting with different styles and iterating on the design easy.  
• Adaptability: This works with various output formats. This includes HTML files, Bokeh server apps, and Jupiter notebooks.  
• Community Themes: This offers a collection of pre-defined themes created by the community. That community can be a starting point for customization.   

The unique aspects of the Bokeh Themes library are:  

• Python-Based Customization: Bokeh Themes empowers developers to customize themes using Python code. Those familiar with Python can make it more accessible.  
• Interactive Plots and Widgets: Allows you to control the appearance of interactive elements. The elements are like sliders, buttons, and tooltips. We use those elements to maintain a consistent style throughout the user interaction.  
• Integration with Bokeh Ecosystem: This library integrates with the broader Bokeh ecosystem. This provides a unified solution for styling both static plots and interactive applications.  
• Flexibility: It gives you granular control over different plot components. You can fine-tune the visual aspects of your plots according to your needs.  

In conclusion, the Bokeh Themes library is an essential tool. People use it to create appealing and consistent data visualizations and applications. Its customization capabilities, ease of use, and integration make it unique. It is a powerful choice for developers to enhance the aesthetics of their projects.  

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 of Python.

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. Install spectral - pip install spectral.
5. Create a new Python file on your IDE.
6. Copy the snippet using the 'copy' button and paste it into your Python file.
7. Remove the line no:7 "output_nootbook()" from the code and then save.
8. Run the current file to generate the output.

I hope you found this useful.

I found this code snippet by searching for 'Bokeh Themes with yaml file' 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 2022.3.1
2. The solution is created in Python 3.11.1 Version
3. bokeh v2.4.3 Version
4. spectral v0.23.1 Version

Using this solution, we can able to use themes in bokeh in Python with 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 themes in bokeh in Python.

Dependent Libraries

You can search for any dependent library on 'bokeh' and 'spectral'.

Support

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

FAQ:  

1. What are bokeh plots, and what themes do they use?  

Bokeh plots are interactive visualizations created using the Bokeh library in Python. They often focus on providing pleasing and informative visual representations of data. Bokeh plots can use various themes to control their appearance. It includes default themes. Those themes are themes with light and dark color schemes and custom themes. Those themes allow you to define your color palettes and styles. Themes play a role in g the look and feel of the plots, making them appealing and aligned with the intended design.   

2. How can I create a scatter chart using the Bokeh library?  

To create a scatter chart using the Bokeh library, you can follow these steps:  

• Install Bokeh if you haven't already:  

pip install Bokeh  

• Import the necessary modules:  

from Bokeh.plotting import figure, show from bokeh.io import output_file  

• Prepare your data. Create lists for x and y coordinates:  

x = [1, 2, 3, 4, 5] y = [6, 7, 2, 4, 8] Set up the output file for the chart (optional): output_file("scatter_chart.html")  

• Create a Bokeh figure and add the scatter plot:  

p = figure (title="Scatter Chart Example", x_axis_label="X-axis", y_axis_label="Y-axis") p.scatter(x, y, size=10, color="blue", legend_label="Data Points")  

• Show the plot:  

show(p)  

This code will generate a scatter chart using the Bokeh library.   

3. What is the process of importing pandas into my project?  

To use the Pandas library in your Python script, add this line of code: import pandas as pd. This allows you to access the library using the alias "pd." This allows you to use its functions and classes throughout your project.   

4. How do I use the figure import to customize my bokeh plot?  

To customize a Bokeh plot using the figure import, follow these steps:  

• Import the necessary modules:  

from Bokeh.plotting import figure, show  

• Create a figure object:  

p = figure ()  

• Add glyphs (visual elements) to the figure using methods like line, circle, rect, etc.   

For example: p.line(x= [1, 2, 3], y= [4, 5, 6], line_color="blue", line_width=2) p.circle(x= [1, 2, 3], y= [4, 5, 6], size=10, fill_color="red", line_color="black")  

• Customize the plot properties using various methods. For instance:  

p.title.text = "Custom Bokeh Plot" p.xaxis.axis_label = "X-axis Label" p.yaxis.axis_label = "Y-axis Label"  

• Show the plot using the show function:  

show(p)  

5. What options exist for changing text color in a bokeh plot?  

In Bokeh, you can change the text color in a plot using the text_color property. 

For example: from Bokeh.plotting import figure, show  

# Create a figure p = figure ()  

# Add text with custom color p.text(x= [1, 2, 3], y= [1, 2, 3], text= ["Text 1", "Text 2", "Text 3"], text_color="red")  

# Show the plot show(p).  

You can also use the text_color property within other Bokeh elements. Those elements are like labels, annotations, and legends to change their text color.