How to plot multiple variables with Pandas and Bokeh I'm new to Pandas and Bokeh; I'd to create a bar plot that shows two different variables next to each other for comparison. 

For instance, with the following Pandas data frame, I'd like to see how the amount of Recalled compares to the amount of Recovered for each year.

    year    Recalled    Recovered
0   1994    11.472  10.207
1   1995    11.810  10.326
2   1996    10.632  10.094
3   1997    13.857  12.944
4   1998    13.861  12.588
5   1999    13.375  11.951
6   2000    11.278  nan
7   2001    12.827  nan
8   2002    12.687  nan
9   2003    10.859  nan
10  2004    nan nan
11  2005    11.782  11.047
12  2006    12.089  10.194
13  2007    14.351  13.401
14  2008    14.921  13.886
15  2009    11.759  10.815
16  2010    12.987  11.482
17  2011    13.262  10.730
18  2012    9.980   9.520
19  2013    10.626  9.591
20  2014    12.199  10.270
This changed in the latest version of Bokeh (I guess 0.12.7). This is the new way of doing it.
from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, FactorRange
from bokeh.plotting import figure

output_file(&quot;bars.html&quot;)

fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
years = ['2015', '2016', '2017']

data = {'fruits' : fruits,
        '2015'   : [2, 1, 4, 3, 2, 4],
        '2016'   : [5, 3, 3, 2, 4, 6],
        '2017'   : [3, 2, 4, 4, 5, 3]}

# this creates [ (&quot;Apples&quot;, &quot;2015&quot;), (&quot;Apples&quot;, &quot;2016&quot;), (&quot;Apples&quot;, &quot;2017&quot;), (&quot;Pears&quot;, &quot;2015), ... ]
x = [ (fruit, year) for fruit in fruits for year in years ]
counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) # like an hstack

source = ColumnDataSource(data=dict(x=x, counts=counts))

p = figure(x_range=FactorRange(*x), plot_height=250, title=&quot;Fruit Counts by Year&quot;,
           toolbar_location=None, tools=&quot;&quot;)

p.vbar(x='x', top='counts', width=0.9, source=source)

p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None

show(p)