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Python Bokeh – Plotting Multiple Polygons on a Graph

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  • Last Updated : 08 Feb, 2022

Bokeh is a Python interactive data visualization. It renders its plots using HTML and JavaScript. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity.
Bokeh can be used to plot multiple polygons on a graph. Plotting multiple polygons on a graph can be done using the multi_polygons() method of the plotting module.
 

plotting.figure.multi_polygons()

 

Syntax : multi_polygons(parameters)
Parameters : 
 

  • xs : x-coordinates of the polygons
  • ys : y-coordinates of the polygons
  • fill_alpha : fill alpha values of the polygons
  • fill_color : fill color values of the polygons
  • hatch_alpha : hatch alpha values of the polygons, default is 1
  • hatch_color : hatch color values of the polygons, default is black
  • hatch_extra : hatch extra values of the polygons
  • hatch_pattern : hatch pattern values of the polygons
  • hatch_scale : hatch scale values of the polygons, default is 12
  • hatch_weight : hatch weight values of the polygons, default is 1
  • line_alpha : percentage value of line alpha, default is 1
  • line_cap : value of line cap for the line, default is butt
  • line_color : color of the line, default is black
  • line_dash : value of line dash such as : solid, dashed, dotted, dotdash, dashdot [default is solid ]
  • line_dash_offset : value of line dash offset, default is 0
  • line_join : value of line join, default in bevel
  • line_width : value of the width of the line, default is 1
  • name : user-supplied name for the model
  • tags : user-supplied values for the model

Other Parameters : 
 

  • alpha : sets all alpha keyword arguments at once
  • color : sets all color keyword arguments at once
  • legend_field : name of a column in the data source that should be used
  • legend_group : name of a column in the data source that should be used
  • legend_label : labels the legend entry
  • muted : determines whether the glyph should be rendered as muted or not, default is False
  • name : optional user-supplied name to attach to the renderer
  • source : user-supplied data source
  • view : view for filtering the data source
  • visible : determines whether the glyph should be rendered or not, default is True
  • x_range_name : name of an extra range to use for mapping x-coordinates
  • y_range_name : name of an extra range to use for mapping y-coordinates
  • level : specifies the render level order for this glyph

Returns : an object of class GlyphRenderer 
 

Example 1 : In this example we will be using the default values for plotting the graph.
 

Python3




# importing the modules
from bokeh.plotting import figure, output_file, show
 
# file to save the model
output_file("gfg.html")
      
# instantiating the figure object
graph = figure(title = "Bokeh Multiple Polygons Graph")
    
# the points to be plotted
xs = [[[[0, 0, 1, 1]]]]
ys = [[[[3, 2, 2, 3]]]]
     
# plotting the graph
graph.multi_polygons(xs, ys)
      
# displaying the model
show(graph)

Output : 
 

Example 2 : In this example we will be plotting the multiple polygons with various other parameters
 

Python3




# importing the modules
from bokeh.plotting import figure, output_file, show
 
# file to save the model
output_file("gfg.html")
      
# instantiating the figure object
graph = figure(title = "Bokeh Multiple Polygons Graph")
 
# name of the x-axis
graph.xaxis.axis_label = "x-axis"
      
# name of the y-axis
graph.yaxis.axis_label = "y-axis"
 
# the points to be plotted
xs = [[[[0, 0, 1, 1]]],
      [[[2, 2, 4, 4], [2.5, 2.5, 3.5, 3.5]]],
      [[[2, 0, 4]]]]
ys = [[[[2.5, 0.5, 0.5, 2.5]]],
      [[[1, 0, 0, 1], [0.75, 0.25, 0.25, 0.75]]],
      [[[2, 0, 0]]]]
 
# color values of the polygons
color = ["red", "purple", "yellow"]
 
# fill alpha values of the polygons
fill_alpha = 0.5
 
# plotting the graph
graph.multi_polygons(xs, ys,
                     color = color,
                     fill_alpha = fill_alpha)
      
# displaying the model
show(graph)

Output : 
 

 


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