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bokeh.plotting.figure.circle_x() function in Python

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  • Last Updated : 17 Jun, 2020

Bokeh is a data visualization library in Python that provides high-performance interactive charts and plots and the output can be obtained in various mediums like notebook, html and server. The Figure Class create a new Figure for plotting. It is a subclass of Plot that simplifies plot creation with default axes, grids, tools, etc.

bokeh.plotting.figure.circle_x() Function

The circle_x() function in plotting module of bokeh library is used to Configure and add circle_x glyphs to this Figure.

Syntax: circle_x(x, y, size=4, angle=0.0, *, angle_units=’rad’, fill_alpha=1.0, fill_color=’gray’, line_alpha=1.0, line_cap=’butt’, line_color=’black’, line_dash=[], line_dash_offset=0, line_join=’bevel’, line_width=1, name=None, tags=[], **kwargs)

Parameters: This method accept the following parameters that are described below:

  • x: This parameter is the x-coordinates for the center of the markers.
  • y: This parameter is the y-coordinates for the center of the markers.
  • size: This parameter is the size (diameter) values for the markers in screen space units.
  • angle: This parameter is the angles to rotate the markers.
  • fill_alpha: This parameter is the fill alpha values for the markers.
  • fill_color: This parameter is the fill color values for the markers.
  • line_alpha: This parameter is the line alpha values for the markers with default value of 1.0 .
  • line_cap: This parameter is the line cap values for the markers with default value of butt.
  • line_color: This parameter is the line color values for the markers with default value of black.
  • line_dash: This parameter is the line dash values for the markers with default value of [].
  • line_dash_offset: This parameter is the line dash offset values for the markers with default value of 0.
  • line_join: This parameter is the line join values for the markers with default value of bevel.
  • line_width: This parameter is the line width values for the markers with default value of 1.
  • mode: This parameter can be one of three values : [“before”, “after”, “center”].
  • name: This parameter is the user-supplied name for this model.
  • tags: This parameter is the user-supplied values for this model.

Other Parameters: These parameters are **kwargs that are described below:

  • alpha: This parameter is used to set all alpha keyword arguments at once.
  • color: This parameter is used to to set all color keyword arguments at once.
  • legend_field: This parameter is the name of a column in the data source that should be used or the grouping.
  • legend_group: This parameter is the name of a column in the data source that should be used or the grouping.
  • legend_label: This parameter is the legend entry is labeled with exactly the text supplied here.
  • muted: This parameter contains the bool value.
  • name: This parameter is the optional user-supplied name to attach to the renderer.
  • source: This parameter is the user-supplied data source.
  • view: This parameter is the view for filtering the data source.
  • visible: This parameter contains the bool value.
  • x_range_name: This parameter is the name of an extra range to use for mapping x-coordinates.
  • y_range_name: This parameter is the name of an extra range to use for mapping y-coordinates.
  • level: This parameter specify the render level order for this glyph.

Return: This method return the GlyphRenderer value.

Below examples illustrate the bokeh.plotting.figure.circle_x() function in bokeh.plotting:
Example 1:




# Implementation of bokeh function
    
import numpy as np 
from bokeh.plotting import figure, output_file, show
    
plot = figure(plot_width = 300, plot_height = 300)
plot.circle_x(x = [1, 2, 3], y = [3, 7, 5], size = 20,
         color ="green", alpha = 0.6)
    
show(plot)

Output:

Example 2:




# Implementation of bokeh function
    
import numpy as np 
from bokeh.plotting import figure, output_file, show
    
x = [1, 2, 3, 4, 5]
y = [6, 7, 8, 7, 3]
   
output_file("geeksforgeeks.html")
   
p = figure(plot_width = 300, plot_height = 300)
   
# add both a line and circles on the same plot
p.line(x, y, line_width = 2)
p.circle_x(x, y, fill_color ="red",
           line_color ="green", size = 8)
   
show(p)

Output:


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