Bokeh 2.3.3

from bokeh.plotting import figure, show from bokeh.models import ColumnDataSource, HoverTool # Sample structured data data = 'x_values': [1, 2, 3, 4, 5], 'y_values': [10, 15, 13, 17, 20], 'labels': ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon'] source = ColumnDataSource(data=data) p = figure(title="CDS Example with Hover Tool", tools="pan,wheel_zoom,reset") # Add circle glyphs mapped to the ColumnDataSource p.circle(x='x_values', y='y_values', size=15, source=source, color="green") # Configure the HoverTool to read from our source columns hover = HoverTool() hover.tooltips = [ ("Name", "@labels"), ("Coordinates", "(@x_values, @y_values)") ] p.add_tools(hover) show(p) Use code with caution. 2. Organizing Layouts

Bokeh is a popular Python library used for creating interactive and web-based visualizations. The latest version, Bokeh 2.3.3, offers a wide range of tools and features that make it easy to create stunning plots and dashboards. In this write-up, we'll explore the key features and improvements in Bokeh 2.3.3. bokeh 2.3.3

By following this guide, you'll be well on your way to creating stunning visualizations with Bokeh 2.3.3. Happy visualizing! from bokeh