Bokeh save interactive plot. io import export_svg export_svg(plot, filename="plot. By combining the ease of generating interactive, high-dimensional visualizations with the First steps 1: Creating a line chart # With just a few lines of Python code, Bokeh enables you to create interactive, JavaScript-powered visualizations displayable in a web browser. To get started using Bokeh to make your visualizations, see the User Guide. All examples so far have used the show() function to save your visualization to an HTML file. One of the major design principles of HoloViews is that the declaration of data is completely independent from the plotting implementation. 15 Interactive Data Visualization with Bokeh In the previous two lessons, you learned how to visualize data using pandas ’ high-level plotting tools for quick insights, and matplotlib for more detailed and customized charting. Bokeh provides a powerful platform to generate interactive plots using HTML5 canvas and WebGL, and is ideally suited towards interactive exploration of data. When using the Jupyter NB "download to" HTML function located in the toolbar, everything but the interactive Bokeh plots export well, also the static Bokeh plots (static plots are 'interactive' as well, but the underlying data You can export an SVG plot in several ways: With code: Use the export_svg() utility function that lets you save a plot or a layout of plots as a single SVG file. To customize the file Bokeh creates for your visualization, import and call the output_file() function. This makes it a great candidate for building web-based dashboards and applications. dhgx rz7uh 0m todoot umzb qyz 2vay6i fumw 7q0mf xiww