![]() ![]() ![]() HSpacer (), sizing_mode = 'stretch_width' ). GitHub - bokeh/bokeh: Interactive Data Visualization in the browser, from Python GitHub - altair-viz/altair: Declarative statistical visualization library for. bind ( plot, x, y, n_clusters ),), ), ( 'CODE', code ), ( 'DESCRIPTION', description ), width = 800 ) pn. WidgetBox ( x, y, n_clusters, explanation, width = 175, margin = 10 ), pn. interactive_plot = pn.bind(plot, x, y, n_clusters) pn.Row( pn.WidgetBox(x, y, n_clusters, explanation), interactive_plot ) ``` """, width = 800 ) app = pn. Starting with the next release, Bokeh 2.0, Python 3.6 or later will be required. Markdown ( """ ```python import panel as pn pn.extension() x = pn.widgets.Select(name='x', options=cols) y = pn.widgets.Select(name='y', options=cols, value='bill_depth_mm') n_clusters = pn.widgets.IntSlider(name='n_clusters', start=2, end=5, value=3) explanation = pn.pane.Markdown(.) def plot(x, y, n_clusters). Bokeh Version 1.4.0 (October 2019) is a significant release that marks the end of support for Python 2.7 and Python 3.5 and earlier. Click on an image below to see its code and interact with a live plot. ![]()
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