Python Bokeh – Plotting Vertical Bar Graphs

30 Nov, 2021
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 vertical bar graphs. Plotting vertical bar graphs can be done using the vbar() method of the plotting module. 
 
Syntax : vbar(parameters)Parameters :  
x : x-coordinates of the center of the vertical bars
width : thickness of the vertical bars
top : y-coordinates of the top edges
bottom : y-coordinates of the bottom edges, default is 0
fill_alpha : fill alpha value of the vertical bars
fill_color : fill color value of the vertical bars
hatch_alpha : hatch alpha value of the vertical bars, default is 1
hatch_color : hatch color value of the vertical bars, default is black
hatch_extra : hatch extra value of the vertical bars
hatch_pattern : hatch pattern value of the vertical bars
hatch_scale : hatch scale value of the vertical bars, default is 12
hatch_weight : hatch weight value of the vertical bars, 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 : soliddasheddotteddotdashdashdot
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 modulesfrom bokeh.plotting import figure, output_file, show  # file to save the modeloutput_file("gfg.html")      # instantiating the figure objectgraph = figure(title = "Bokeh Vertical Bar Graph")  # x-coordinates to be plottedx = [1, 2, 3, 4, 5]  # x-coordinates of the top edgestop = [1, 2, 3, 4, 5]  # width / thickness of the barswidth = 0.5  # plotting the graphgraph.vbar(x,           top = top,           width = width)  # displaying the modelshow(graph)
Output :  
Example 2 :In this example we will be plotting vertical bars with different parameters. 
Python3
# 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 Vertical Bar Graph") # name of the x-axisgraph.xaxis.axis_label = "x-axis"      # name of the y-axisgraph.yaxis.axis_label = "y-axis"  # x-coordinates to be plottedx = [1, 2, 3, 4, 5]  # x-coordinates of the top edgestop = [1, 2, 3, 4, 5]  # width / thickness of the barswidth = [0.5, 0.4, 0.3, 0.2, 0.1] # color values of the barsfill_color = ["yellow", "pink", "blue", "green", "purple"]  # plotting the graphgraph.vbar(x,           top = top,           width = width,           fill_color = fill_color)  # displaying the modelshow(graph)
Output :  
 
kk9826225
Data Visualization
Python Bokeh-plotting-figure-class
Python-Bokeh
Python
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