Bokeh Tutorial
Selected Reading
- Bokeh - Discussion
- Bokeh - Useful Resources
- Bokeh - Quick Guide
- Bokeh - Developing with JavaScript
- Bokeh - WebGL
- Bokeh - Extending Bokeh
- Bokeh - Embedding Plots and Apps
- Bokeh - Exporting Plots
- Bokeh - Using Bokeh Subcommands
- Bokeh - Server
- Bokeh - Adding Widgets
- Bokeh - Customising legends
- Bokeh - Styling Visual Attributes
- Bokeh - Plot Tools
- Bokeh - Layouts
- Bokeh - Filtering Data
- Bokeh - ColumnDataSource
- Bokeh - Pandas
- Bokeh - Annotations and Legends
- Bokeh - Axes
- Bokeh - Setting Ranges
- Bokeh - Specialized Curves
- Bokeh - Wedges and Arcs
- Bokeh - Rectangle, Oval and Polygon
- Bokeh - Circle Glyphs
- Bokeh - Area Plots
- Bokeh - Plots with Glyphs
- Bokeh - Basic Concepts
- Bokeh - Jupyter Notebook
- Bokeh - Getting Started
- Bokeh - Environment Setup
- Bokeh - Introduction
- Bokeh - Home
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Bokeh - WebGL
Bokeh - WebGL
When one has to use large datasets for creating visuapzations with the help of Bokeh, the interaction can be very slow. For that purpose, one can enable Web Graphics Library (WebGL) support.
WebGL is a JavaScript API that renders content in the browser using GPU (graphics processing unit). This standardized plugin is available in all modern browsers.
To enable WebGL, all you have to do is set output_backend property of Bokeh Figure object to ‘webgl’.
fig = figure(output_backend="webgl")
In the following example, we plot a scatter glyph consisting of 10,000 points with the help of WebGL support.
import numpy as np from bokeh.plotting import figure, show, output_file N = 10000 x = np.random.normal(0, np.pi, N) y = np.sin(x) + np.random.normal(0, 0.2, N) output_file("scatterWebGL.html") p = figure(output_backend="webgl") p.scatter(x, y, alpha=0.1) show(p)