Python Bokeh – Plotting Dots on a Graph

03 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 dots on a graph. Plotting dots on a graph can be done using the dot() method of the plotting module.
Syntax : dot(parameters)
Parameters :
x : x-coordinates of the center of the dot markers
y : y-coordinates of the center of the dot markers
size : diameter of the dot markers, default is 4
angle : angle of rotation of the dot markers, default is 0
angle_units : unit of the angle, default is rad
fill_alpha : fill alpha value of the dot markers
fill_color : fill color value of the dot 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 :soliddasheddotteddotdashdashdotdefault is solid
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. We have provided the size attribute to make the glyph visible.
# importing the modulesfrom bokeh.plotting import figure, output_file, show      # file to save the modeloutput_file("gfg.html")      # instantiating the figure objectgraph = figure(title = "Bokeh Dot Graph")    # the points to be plottedx = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]y = [i ** 2 for i in x]   # plotting the graphgraph.dot(x, y, size = 25)    # displaying the modelshow(graph)
Output :
Example 2 :In this example we will generate the points using the random() function and pass some parameters.
# importing the modulesfrom bokeh.plotting import figure, output_file, showimport random     # file to save the modeloutput_file("gfg.html")     # instantiating the figure objectgraph = figure(title = "Bokeh Dot Graph")   # name of the x-axisgraph.xaxis.axis_label = "x-axis"    # name of the y-axisgraph.yaxis.axis_label = "y-axis"   # generating the points to be plottedx = []y = []for i in range(100):    x.append(i)for i in range(100):    y.append(random.random())   # size of the pointssize = [i * 40 for i in y]   # fill color valuefill_color = "yellow"  # name of the legendlegend_label = "Sample Dots"    # plotting the graphgraph.dot(x, y,      size = size,      fill_color = fill_color,      legend_label = legend_label)     # displaying the modelshow(graph)
Output :
Data Visualization
Python Bokeh-plotting-figure-class
Python-Bokeh
Python
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