One chart with two different y axis ranges in Bokeh?

Question

I would like a Bar chart with Quantity information on the left y-axis, and then overlay a Scatter/Line plot with Yield % on the right. I can create each of these charts separately, but do not know how to combine them into a single plot.

In matplotlib, we would create a second figure using twinx(), and then use yaxis.tick_left() and yaxis.tick_right() on the respective figures.

Is there a method for doing something similar with Bokeh?

Answer 1

Yes, now it is possible to have two y axes in Bokeh plots. The code below shows script parts significant in setting up the second y axis to the usual figure plotting script.

# Modules needed from Bokeh. from bokeh.io import output_file, show from bokeh.plotting import figure from bokeh.models import LinearAxis, Range1d  # Seting the params for the first figure. s1 = figure(x_axis_type="datetime", tools=TOOLS, plot_width=1000,            plot_height=600)  # Setting the second y axis range name and range s1.extra_y_ranges = {"foo": Range1d(start=-100, end=200)}  # Adding the second axis to the plot.   s1.add_layout(LinearAxis(y_range_name="foo"), 'right')  # Setting the rect glyph params for the first graph.  # Using the default y range and y axis here.            s1.rect(df_j.timestamp, mids, w, spans, fill_color="#D5E1DD", line_color="black")  # Setting the rect glyph params for the second graph.  # Using the aditional y range named "foo" and "right" y axis here.  s1.rect(df_j.timestamp, ad_bar_coord, w, bar_span,          fill_color="#D5E1DD", color="green", y_range_name="foo")  # Show the combined graphs with twin y axes. show(s1) 

And the plot we get looks like this:

If you want to add a label to the second axis, this can be accomplished by editing the call to LinearAxis as follows:

s1.add_layout(LinearAxis(y_range_name="foo", axis_label='foo label'), 'right') 

Answer 2

This post helped me to accomplish the effect you are looking for.

Here is the content of that post:

from bokeh.plotting import figure, output_file, show from bokeh.models.ranges import Range1d import numpy   output_file("line_bar.html")  p = figure(plot_width=400, plot_height=400)  # add a line renderer p.line([1, 2, 3, 4, 5], [6, 7, 6, 4, 5], line_width=2)  # setting bar values h = numpy.array([2, 8, 5, 10, 7])  # Correcting the bottom position of the bars to be on the 0 line. adj_h = h/2  # add bar renderer p.rect(x=[1, 2, 3, 4, 5], y=adj_h, width=0.4, height=h, color="#CAB2D6")  # Setting the y  axis range    p.y_range = Range1d(0, 12)  p.title = "Line and Bar"  show(p) 

If you want to add the second axis to the plot do so with p.extra_y_ranges as described in the post above. Anything else, you should be able to figure out.

For example, in my project I have code like this:

s1 = figure(plot_width=800, plot_height=400, tools=[TOOLS, HoverTool(tooltips=[('Zip', "@zip"),('((Rides/day)/station)/capita', "@height")])],            title="((Rides/day)/station)/capita in a Zipcode (Apr 2015-Mar 2016)")  y = new_df['rides_per_day_per_station_per_capita'] adjy = new_df['rides_per_day_per_station_per_capita']/2  s1.rect(list(range(len(new_df['zip']))), adjy, width=.9, height=y, color='#f45666') s1.y_range = Range1d(0, .05) s1.extra_y_ranges = {"NumStations": Range1d(start=0, end=35)} s1.add_layout(LinearAxis(y_range_name="NumStations"), 'right') s1.circle(list(range(len(new_df['zip']))),new_df['station count'], y_range_name='NumStations', color='blue') show(s1) 

And the result is: