![]() ![]() An administrator can upload images to the DSS UI by going to Global Shared Code > Static Web Resources > +Add. Images can be stored in DATADIR/local/static. You can plot images in a Jupyter notebook or display them in a webapp. To plot images in Bokeh, the image path specified must be relative to your “current location”. In a Jupyter notebook (use a “Export notebook” scenario step) subplots import makesubplots import pandas as pd pio. Bokeh provides a powerful platform to generate interactive plots using HTML5 canvas and WebGL, and is ideally suited towards interactive exploration of data. In a DSS recipe (use a regular “Build” scenario step) Plotlys Python graphing library makes interactive, publication-quality graphs. This call to dataiku.insights code can be: You can refresh the charts automatically on a dashboard by using a scenario to re-run the above piece of code. ![]() Bokeh is a Python library that generates interactive visualizations. save_bokeh ( "my-bokeh-plot", f )įrom the Dashboard, you can then add a new “Static” insight, select the my-bokeh-plot insight Refreshing charts on a dashboard ¶ The purpose of this blog post is to go over some of the basics of plotting with Bokeh. Each chart will retain full zoom/pan/select/export capabilities įrom dataiku import insights # f is a Bokeh figure, or any object that can be passed to show() insights. If you want to use Bokeh controls on a DSS Dashboard, use a Bokeh webapp.Įach Bokeh figure can become a single insight in the dashboard. This does not include the capability to include controls. The ipywidgets-based projects provide tighter integration with Jupyter, while some other approaches give only limited interactivity in Jupyter (e.g. When you change these controls, the Bokeh chart can react dynamically.ĭocumentation for this is available here: Displaying Bokeh charts on a dashboard ¶īokeh charts generated using Python code can be shared on a DSS dashboard using the “static insights” system. Jupyter notebooks: Most InfoVis libraries now support interactive use in Jupyter notebooks, with JavaScript-based plots backed by Python. You can use interactive controls (sliders, inputs, …) that are displayed in the notebook. circle (,, size = 10 ) show ( f )Ī complete documentation for the usage of Bokeh in a Jupyter notebook can be found at Using interactive controls in the Jupyter notebook ¶ API Node & API Deployer: Real-time APIsįrom otting import figure f = figure () f.Automation scenarios, metrics, and checks.Using interactive controls in the Jupyter notebook.Displaying charts in a Jupyter notebook.Creating a Bokeh interactive web application. ![]()
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