Skip to content
Related Articles

Related Articles

Improve Article

Python Bokeh – Plotting Hexagon Tiles 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 hexagon tiles on a graph. Plotting hexagon tiles on a graph can be done using the hex_tile() method of the plotting module.

plotting.figure.hex_tile()

Syntax : hex_tile(parameters)

Parameters :

  • q : column axial coordinates of the center of the hexagon tiles
  • r : row axial coordinates of the center of the hexagon tiles
  • aspect_scale : aspect scale value, default is 1
  • fill_alpha : fill alpha value of the hexagon tile markers
  • fill_color : fill color value of the hexagon tile markers
  • 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
  • orientation : orientation value, default is pointytop
  • scale : scale factor of individual tiles, default is 1
  • size : radius of the hexagonal tiles, default is 1
  • 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. We have provided the size and fill_color attributes to make the glyph visible.




# 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 Hexagon Tiles Graph",
               match_aspect = True
     
# the points to be plotted 
r = [0, 0, 1
q = [1, 2, 2
    
# plotting the graph 
graph.hex_tile(r, q) 
     
# displaying the model 
show(graph) 

Output :

Example 2 : In this example we will be plotting the hexagon tiles with various 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 Hexagon Tiles Graph",
               match_aspect = True
  
# 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 
r = [0, -101, -1, 0, 1]
q = [00, -1, -11, 1, 0
  
# fill color values
fill_color = ["yellow", "blue", "pink", "green", "orange", "red", "purple"]
  
# line color values
line_color = ["yellow", "blue", "pink", "green", "orange", "red", "purple"]
  
# plotting the graph 
graph.hex_tile(r, q,
               fill_color = fill_color,
               line_color = line_color) 
     
# 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. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course




My Personal Notes arrow_drop_up
Recommended Articles
Page :