This makes it easy to incorporate Bokeh plots into your existing Python workflows. Integration with other tools: Bokeh is tightly integrated with the Python data science stack, and it works well with libraries like NumPy, Pandas, and Scikit-learn.Plotly, on the other hand, is a bit more prescriptive in terms of how you create your plots, and it provides a more structured API. It has a lower-level API that allows you to define your plots in code, and you can also use the high-level charts interface to create complex plots with minimal code. Flexibility: Bokeh provides more flexibility in terms of how you can create and customize your plots.However, each of these libraries has its own strengths and use cases, and the choice of which one to use depends on the specific requirements of your project.īokeh and Plotly are both powerful Python libraries for creating interactive visualizations, but they have some differences in terms of their strengths and use cases: Seaborn and Matplotlib are great libraries for creating static visualizations, but they don’t have the same level of interactivity and web-based capabilities as Bokeh. Support for different platforms: Bokeh has support for Jupyter Notebooks, standalone HTML files, and server-based applications, which gives you more options for how you want to present and share your visualizations.Easy customization: Bokeh has a wide range of built-in styling options, but it also allows you to easily customize the look and feel of your plots by modifying the CSS or using your own custom JavaScript code.Large data support: Bokeh is optimized for handling large datasets and can render millions of data points quickly and efficiently.Web-based: Bokeh plots can be easily embedded in web applications, which can be useful if you want to create dashboards or other web-based data applications.This makes it easier to explore your data and communicate insights to others. Interactive visualization: Bokeh allows you to create interactive plots with features like hover tooltips, pan and zoom, and selection tools.It has some advantages over other popular Python visualization libraries like Seaborn and Matplotlib: Bokeh is a Python library for creating interactive visualizations for the web.
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