Bokeh – Axes
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In this chapter, we shall discuss about various types of axes.
Sr.No
Axes
Description
1
Categorical Axes
The bokeh plots show numerical data along both x and y axes. In order to use categorical data along either of axes, we need to specify a FactorRange to specify categorical dimensions for one of them.
2
Log Scale Axes
If there exists a power law relationship between x and y data series, it is desirable to use log scales on both axes.
3
Twin Axes
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
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 either of 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 following example, a simple bar plot is displayed showing number of students enrolled for 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 different colour, set color property of vbar() function to 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 dict object containing values corresponding to each field.
In following example, sales is a dictionary showing sales figures of 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
A grouped bar plot is obtained by specifying a visual displacement for the bars with the help of dodge() function in bokeh.transform module.
The dodge() function introduces a relative offset for each bar plot thereby achieving a visual impression of group. In following example, vbar() glyph is separated by an offset of 0.25 for each group of bars for a particular month.
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, it is often necessary to have the data on former axis be displayed on a log scale. 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 by passing “log” for the value of either of these parameters.
First figure shows plot between x and 10x on a linear scale. In second figure y_axis_type is set to ”log”
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 new glyph to the figure, these named ranges are used.
We try to display a sine curve and a straight line in same plot. Both glyphs have y axes with different ranges. The x and y data series for sine curve and line are 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, plot between x and y represents sine relation and plot between x and y2 is a straight line. The Figure object is defined with explicit y_range and a line glyph representing sine curve is added as follows −
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 additional y axis on right side, use 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”,