Skip to content
Related Articles

Related Articles

Python Bokeh – Plotting Multiple Polygons on a Graph
  • Last Updated : 10 Jul, 2020

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.




# 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




# 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 poloygons
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 :

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up
Recommended Articles
Page :