The AutocompleteInput widget is a general-purpose text input widget that usesĪ list of possible inputs to provide autocomplete while typing. YouĬan see this output in your browser’s JavaScript console log. Many of theĮxamples produce print output using the JavaScript console.log function. The sections below are examples for all widgets available in Bokeh. See UI elements supporting tooltips for more information about adding Hover over the question mark icon next to “Choose values” to see the tooltip. Information about the widget’s purpose or use, for example. This can be helpful to provide additional This allows you to use callbacksĪdditionally, since the visualization itself is displayed in a browser, you If you want to use widgets in connection with a Bokeh server, the serverĬan handle some interactivity. You can write your own Javascript code, or use Bokeh’s pre-defined Pythonįunction or a SetValue object which generate the HTML document, the browser needs to handle all interactivity. If you want to use widgets to interact with Bokeh objects in a standalone Which one to use depends on whether you are usingīokeh server or are generating standalone HTML output: Interactively manipulate data and properties of objects in your visualization.īokeh uses callbacks to handle these interactions. While some widgets are only meant to display data, others can be used to You can create your ownĬustom widgets, or wrap different third party widget libraries by creatingĬustom extensions as described in Custom extensions. Widgets canīe added directly to the document root or be nested inside a layout.Ĭan use to drive new computations, update plots, and connect to otherīokeh provides a simple default set of widgets. Widgets are interactive control and display elements that can be added to Bokehĭocuments to provide a front end user interface to a visualization. circle ( x = "index", y = "hwy", fill_color = "#CE603D", size = 8, alpha = 0.5, source = source ) tooltips = cty_hover_tool = HoverTool ( renderers =, tooltips = ) hwy_hover_tool = HoverTool ( renderers =, tooltips = ) p. unique ()) columns = data_table = DataTable ( source = source, columns = columns, editable = True, width = 800, index_position =- 1, index_header = "row index", index_width = 60 ) p = figure ( width = 800, height = 300, tools = "pan,wheel_zoom,xbox_select,reset", active_drag = "xbox_select" ) cty = p. The following code shows an example of linked brushing between circle glyphs onįrom bokeh.layouts import column from bokeh.models import ( ColumnDataSource, DataTable, HoverTool, IntEditor, NumberEditor, NumberFormatter, SelectEditor, StringEditor, StringFormatter, TableColumn ) from otting import figure, show from 2 import autompg2 as mpg source = ColumnDataSource ( mpg ) manufacturers = sorted ( mpg. Source, see Linked selection with filtered data. Selection extends to glyph renderers that plot only a subset of data from a data Glyph must pass to all other glyphs that share that same source. This is all Bokeh needs to understand that selections acted on one Linked brushing in Bokeh is expressed by sharing data sources between glyph The SPLOM section of the Statistical plots Now you have learned how to link panning between multiple plots with theĪ more sophisicated example of a linked scatterplot matric can be found in square ( x, y2, size = 10, color = "olive", alpha = 0.5 ) p = gridplot (], toolbar_location = None ) show ( p ) triangle ( x, y1, size = 10, color = "firebrick", alpha = 0.5 ) # create a new plot and share only one range s3 = figure ( width = 250, height = 250, x_range = s1. circle ( x, y0, size = 10, color = "navy", alpha = 0.5 ) # create a new plot and share both ranges s2 = figure ( width = 250, height = 250, x_range = s1. From bokeh.layouts import gridplot from otting import figure, show x = list ( range ( 21 )) y0 = x y1 = y2 = # create a new plot s1 = figure ( width = 250, height = 250, title = None ) s1.
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