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Data visualization is a crucial aspect of data analysis, allowing us to communicate insights effectively and make informed decisions. In the world of Python, one library that has gained significant traction for creating interactive and web-ready visualizations is Bokeh.
In this article, we’ll dive into the fundamentals of Bokeh and explore its capabilities through practical examples.
What is Bokeh?
Bokeh is an open-source Python library that provides a high-level interface for creating interactive visualizations in modern web browsers. Unlike traditional plotting libraries, Bokeh renders its visualizations using HTML5 Canvas and JavaScript, making them highly responsive and suitable for deployment on the web. This feature sets Bokeh apart from other libraries, as it enables seamless integration of visualizations into web applications or interactive dashboards.
Getting Started with Bokeh
Before we delve into the code examples, let’s set up our environment. Bokeh can be installed using pip, the Python package installer:
pip install bokeh
