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In this chapter, we shall discuss various types of bokeh axes.

Bokeh – Categorical Axes

In the examples so far, the Bokeh plots show numerical data along both x and y axes. In order to use categorical data along with either of the axes, we need to specify a FactorRange to specify categorical dimensions for one of them. For example, to use strings in the given list for x-axis −

langs = ['C', 'C++', 'Java', 'Python', 'PHP']
fig = figure(x_range = langs, plot_width = 300, plot_height = 300)

Example

With the following example, students enrolled in various courses offered.

from bokeh.plotting import figure, output_file, show
langs = ['C', 'C++', 'Java', 'Python', 'PHP']
students = [23,17,35,29,12]
fig = figure(x_range = langs, plot_width = 300, plot_height = 300)
fig.vbar(x = langs, top = students, width = 0.5)
show(fig)

Output

To show each bar in a different color, set the color property of the v-bar() function to a list of color values.

cols = ['red','green','orange','navy', 'cyan']
fig.vbar(x = langs, top = students, color = cols,width=0.5)

Output

To render a vertical (or horizontal) stacked bar using vbar_stack() or hbar_stack() function, set stackers property to list of fields to stack successively and source property to a direct object containing values corresponding to each field.

In the following example, a sale is a dictionary showing sales figures for three products in three months.

from bokeh.plotting import figure, output_file, show
products = ['computer','mobile','printer']
months = ['Jan','Feb','Mar']
sales = {'products':products,
   'Jan':[10,40,5],
   'Feb':[8,45,10],
   'Mar':[25,60,22]}
cols = ['red','green','blue']#,'navy', 'cyan']
fig = figure(x_range = products, plot_width = 300, plot_height = 300)
fig.vbar_stack(months, x = 'products', source = sales, color = cols,width = 0.5)
show(fig)

Output

The dodge() function introduces a relative offset for each bar plot thereby achieving a visual impression of the group.

from bokeh.plotting import figure, output_file, show
from bokeh.transform import dodge
products = ['computer','mobile','printer']
months = ['Jan','Feb','Mar']
sales = {'products':products,
   'Jan':[10,40,5],
   'Feb':[8,45,10],
   'Mar':[25,60,22]}
fig = figure(x_range = products, plot_width = 300, plot_height = 300)
fig.vbar(x = dodge('products', -0.25, range = fig.x_range), top = 'Jan',
   width = 0.2,source = sales, color = "red")
fig.vbar(x = dodge('products', 0.0, range = fig.x_range), top = 'Feb',
   width = 0.2, source = sales,color = "green")
fig.vbar(x = dodge('products', 0.25, range = fig.x_range), top = 'Mar',
   width = 0.2,source = sales,color = "blue")
show(fig)

Output

Log Scale Axes

When values on one of the axes of a plot grow exponentially with linearly increasing values of another, For example, if there exists a power-law relationship between x and y data series, it is desirable to use log scales on both axes.

Bokeh. plotting API’s figure() function accepts x_axis_type and y_axis_type as arguments which may be specified as log axis bypassing “log” for the value of either of these parameters.

First figure shows plot between x and 10x on a linear scale.

from bokeh.plotting import figure, output_file, show x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0] y = [10**i for i in x] fig = figure(title = ‘Linear scale example’,plot_width = 400, plot_height = 400) fig.line(x, y, line_width = 2) show(fig)

Output

Now change figure() function to configure y_axis_type=’log’

fig = figure(title = 'Linear scale example',plot_width = 400, plot_height = 400, y_axis_type = "log")

Output

Twin Axes

In certain situations, it may be needed to show multiple axes representing varying ranges on a single plot figure. The figure object can be so configured by defining extra_x_range and extra_y_range properties. While adding a new glyph to the figure, these named ranges are used.

We try to display a sine curve and a straight line in the same plot. Both glyphs have y-axes with different ranges. The x and y data series for the sine curve and the line is obtained by the following −

from numpy import pi, arange, sin, linspace
x = arange(-2*pi, 2*pi, 0.1)
y = sin(x)
y2 = linspace(0, 100, len(y))

Here, a plot between x and y represents sine relation, and the plot between x and y2 is a straight line. Follows as the Figure −

fig = figure(title = 'Twin Axis Example', y_range = (-1.1, 1.1))
fig.line(x, y, color = "red")

We need an extra y range. It is defined as −

fig.extra_y_ranges = {"y2": Range1d(start = 0, end = 100)}

To add an additional y-axis on the right side, use the add_layout() method. Add a new line glyph representing x and y2 to the figure.

fig.add_layout(LinearAxis(y_range_name = "y2"), 'right')
fig.line(x, y2, color = "blue", y_range_name = "y2")

This will result in a plot with twin y-axes. Complete code and the output is as follows −

from numpy import pi, arange, sin, linspace
x = arange(-2*pi, 2*pi, 0.1)
y = sin(x)
y2 = linspace(0, 100, len(y))
from bokeh.plotting import output_file, figure, show
from bokeh.models import LinearAxis, Range1d
fig = figure(title='Twin Axis Example', y_range = (-1.1, 1.1))
fig.line(x, y, color = "red")
fig.extra_y_ranges = {"y2": Range1d(start = 0, end = 100)}
fig.add_layout(LinearAxis(y_range_name = "y2"), 'right')
fig.line(x, y2, color = "blue", y_range_name = "y2")
show(fig)

Output

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