contains ( category_value ) = True ] # Generate the new plot x = selected y = selected source. value selected = df if ( category_value != "All" ): selected = selected. value def update_data ( attrname, old, new ): category_value = category.
Learn bokeh python update#
#Set up update functions and callbacks def update_title ( attrname, old, new ): plot. The preview should now show the current (non-interactive) scatter plot. For now, we’ll include an empty widgetbox that we’ll populate in a moment when we add the interactivity. The last two lines define the layout of the webapp and adds it to the current “document”. add_root ( row ( inputs, plot, width = 800 ))Ĭreates a plot object with the desired height and width properties ĭefines the title of the plot using the X- and Y-Axis column names Ĭonfigures a set of built-in Bokeh plot tools Ĭomputes the minimum and maximum values of customer age and total, and uses those to define the axis limits Īnd defines the visualization as a scatter plot that plots data from the source defined above. scatter ( 'x', 'y', source = source ) # Set up layouts and add to document inputs = widgetbox () curdoc (). # Set up plot plot = figure ( plot_height = 400, plot_width = 400, title = y_column + " by " + x_column, tools = "crosshair,pan,reset,save,wheel_zoom", x_range =, y_range = ) plot. Compute and Resource Quotas on Dataiku Cloud.Preferred Connections and Format for Dataset Storage.Deploying Dataiku Instances to Cloud Stacks.Examples of Plugin Component Development.
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