Python Bokeh – Plotting Vertical Bar Graphs

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.

plotting.figure.vbar()

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 :
    • 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.

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# 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 Vertical Bar Graph")
   
# x-coordinates to be plotted
x = [1, 2, 3, 4, 5]
   
# x-coordinates of the top edges
top = [1, 2, 3, 4, 5]
   
# width / thickness of the bars 
width = 0.5
   
# plotting the graph
graph.vbar(x,
           top = top,
           width = width)
   
# displaying the model
show(graph)

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Output :

Example 2 :In this example we will be plotting verticle bars with different parameters.

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# 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 Vertical Bar Graph")
  
# name of the x-axis
graph.xaxis.axis_label = "x-axis"
       
# name of the y-axis
graph.yaxis.axis_label = "y-axis"
   
# x-coordinates to be plotted
x = [1, 2, 3, 4, 5]
   
# x-coordinates of the top edges
top = [1, 2, 3, 4, 5]
   
# width / thickness of the bars 
width = [0.5, 0.4, 0.3, 0.2, 0.1]
  
# color values of the bars
fill_color = ["yellow", "pink", "blue", "green", "purple"]
   
# plotting the graph
graph.vbar(x,
           top = top,
           width = width,
           fill_color = fill_color)
   
# displaying the model
show(graph)

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Output :




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