1/13/2024 0 Comments Github python bokehThis is what requires the Python interpreter to be running.īokeh allows for pure JavaScript callbacks. The callbacks, which are functions that get executed to update information about the plot based on changes in the widgets (such as computing the values of a smooth curve, as we saw in our exploration of the theoretical model for fold change in gene expression, or selecting pieces of a Pandas data frame,Īs we saw in the dashboard for the facial recognition data), are in Python. It is this JavaScript library that handles all of the rendering, zooming, selecting, etc., as well has the widgets. In order to display the graphics and enable the interactions, Bokeh relies on its client-side JavaScript library,īokehJS. (Even though we used a Panel interface, the dashboards we have made are rendered with Bokeh.) As we have seen, Bokeh is a Python library for generating graphics that can be visualized and interacted with in the browser. We have been using Bokeh for rendering out plots and dashboards. But in many cases, with just a little JavaScript, you can make beautiful interactive graphics rendered in pure HTML with JavaScript. In some cases, you will just need the Python interpreter for sophisticated dashboards that do involved calculations. Machine (and have all of the necessary installations). If you wanted to include an interactive plot in a publication, it would be nice to be able to interact with the plots/dashboard directly in the browser without the reader having to launch a Jupyter notebook or serve it up on their local In order to interactively explore using the dashboard, you need to have a Python interpreter running, either in the Jupyter notebook or when you serve your dashboards using panel serve on the command line. There is a major drawback to this approach, though. I hope you recognize how powerful this is for exploring your data sets. We can also serve up a notebook to get only the data displays we like on their own browser tabs. Panel, with Bokeh output, allow us to write Python code that can update our displays as we adjust parameters or select variables. Griffin Chure’s templates for reproducible publishingĪs we have seen when building dashboards, we can build remarkable interactivity in our data displays, allowing for low-effort exploration of data sets.
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