To maintain interactivity within the notebook, use the %notebook magic command or the output_notebook() function from Bokeh’s io module. Loss of interactivity in Jupyter Notebooks: When using the file_html function within a Jupyter Notebook, the interactive plot may no longer be displayed in the notebook itself. To ensure the best compatibility, advise users to view the HTML files in up-to-date browsers like Google Chrome, Mozilla Firefox, or Microsoft Edge. Ĭompatibility with web browsers: Although Bokeh visualizations generally work well across modern web browsers, some older browsers may not fully support the necessary JavaScript features.To mitigate this issue, consider using Bokeh server applications, which can handle large datasets more efficiently, or explore data aggregation techniques to reduce the size of the data being visualized. This can lead to slow loading times when opening the file in a browser. Large file sizes: Bokeh visualizations can sometimes produce large HTML files, particularly when working with extensive datasets or complex plots. Here are some common challenges and their potential solutions: While Bokeh’s file_html function is a powerful tool for generating standalone HTML files containing interactive visualizations, users may encounter some issues or limitations during its use.
The file can be opened in any web browser, and the interactive plot can be viewed and shared without the need for a Python environment.
Bokeh python html code#
This code creates a standalone HTML file named “bokeh_line_plot_example.html” containing the line plot. With open("bokeh_line_plot_example.html", "w") as f: Use the file_html function to export the plot to an HTML file: html = file_html(p, CDN, "Bokeh Line Plot Example").P.line(x, y, legend_label="Line", line_width=2) Generate a Bokeh line plot: p = figure(title="Simple Line Plot", x_axis_label="x", y_axis_label="y").Import required libraries: from otting import figure.
Here’s an example of using the file_html function to create a simple line plot: This can be done using the file_html function, which takes a Bokeh plot object and generates an HTML file with the necessary JavaScript and CSS to display the interactive visualization. One of the most convenient ways to share your Bokeh visualizations is by exporting them to a standalone HTML file. Bokeh’s flexibility allows users to customize the appearance and interactivity of the plots, making it a popular choice for data visualization projects. The library provides a versatile set of tools to build various types of plots, such as line, bar, scatter, and pie charts. Bokeh Overviewīokeh is designed for creating interactive visualizations that can be embedded in web applications or displayed in Jupyter Notebooks. In this article, we will walk through an example of using Bokeh’s file_html function to generate an HTML file containing an interactive visualization. It enables users to generate aesthetically pleasing and responsive graphics with minimal effort. The Google API Key will be stored in the Bokeh Document JSON.Bokeh is a powerful Python library for creating interactive visualizations for web browsers.
To plot glyphs over a Google Map, use the function gmap().įor the function to work, you must pass it a Google API Key and configure the Google Map underlay GMapOptions. Tile Source for Stamen Toner Service which includes only labels Google Maps # Tile Source for Stamen Toner Background Service which does not include labels Stamen Toner Labels # Tile Source for Stamen Toner Service Stamen Toner Background # Tile Source for Stamen Terrain Service Stamen Toner # Tile Source for CartoDB Tile Service Esri World Imagery # Representative samples of the most common tile providers are shown below. Interactively as an xyzservices.providers module. The available built-in tile providers are listed in the xyzservices documentation or Import xyzservices.providers as xyz from otting import figure, show # range bounds supplied in web mercator coordinates p = figure ( x_range = ( - 2000000, 6000000 ), y_range = ( - 1000000, 7000000 ), x_axis_type = "mercator", y_axis_type = "mercator" ) p.
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