![]() What's more, Bokeh powers your dashboards on Web browsers using JavaScript, all without you needing to write any JavaScript code.ĭashboards provide all your important information in a single page and are usually used for presenting information such as KPIs and sales results. Using Bokeh, you can create dashboards - a visual display of all your key data. So let the fun begin! What Is Bokeh?īokeh is a Python library for creating interactive visualizations for Web browsers. In this article, I'll walk you through the basics of Bokeh: how to install it, how to create basic charts, how to deploy them on Web servers, and more. ![]() However, what if you want to generate all these charts and graphics and let your users view them on Web browsers? Also, it would be useful if the users can interact with your charts dynamically and drill down into the details they want to see. These libraries are very useful for doing data exploration, as well as visualizing and generating graphics for reports. Most data analysts and scientists using Python are familiar with plotting libraries such as matplotlib, Seaborn, and Plotly. axis_label = "Disposition" legend = Legend ( items = ), ( "Asia", ), ( "Europe", ), ], location = "top_left" ) fig. circle ( x = autompg_df = "Europe" ], y = autompg_df = "Europe" ], fill_color = "blue", size = 12, fill_alpha = 0.5, line_width = 0 ) fig. circle ( x = autompg_df = "Asia" ], y = autompg_df = "Asia" ], fill_color = "green", size = 12, fill_alpha = 0.5, line_width = 0 ) c3 = fig. circle ( x = autompg_df = "North America" ], y = autompg_df = "North America" ], fill_color = "red", size = 12, fill_alpha = 0.5, line_width = 0 ) c2 = fig. title = "Regions" show ( fig )įrom bokeh.models import Legend fig = figure ( width = 600, height = 400, title = "hp vs displ per region" ) c1 = fig. circle ( x = autompg_df = "Europe" ], y = autompg_df = "Europe" ], fill_color = "blue", size = 12, fill_alpha = 0.5, line_width = 0, legend_label = "Europe" ) fig. circle ( x = autompg_df = "Asia" ], y = autompg_df = "Asia" ], fill_color = "green", size = 12, fill_alpha = 0.5, line_width = 0, legend_label = "Asia" ) fig. ![]() circle ( x = autompg_df = "North America" ], y = autompg_df = "North America" ], fill_color = "red", size = 12, fill_alpha = 0.5, line_width = 0, legend_label = "North America" ) fig. Below we are plotting the line chart of google stock prices over time.įig = figure ( width = 400, height = 400, title = "hp vs displ per region" ) fig. We'll not try the above-mentioned attributes on various plots through examples.
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