Plotting in Bokeh

A Bokeh Line plot can be created simply by following these steps:

• Create a blank figure with necessary arguments: p = figure(...)
• Call a glyph method such as p.line on the figure
• Show the figure

#line plot Example with BoxAnnotation usage

#Importing necessary modules
from bokeh.models import BoxAnnotation #for highlighting

#Preparing the plot data
#Taking gulcose data from bokeh sample data
from bokeh.sampledata.glucose import data 
week=data.loc['2010-10-01':'2010-10-08'] #taking one week data
x=week.index
y=week.glucose

#Create a new plot 
p = figure(x_axis_type="datetime", title="Glocose Variation", plot_height=350, plot_width=800) #note to specify x_axis type as datetime to make x axis as datatime
p.xgrid.grid_line_color=None
p.ygrid.grid_line_alpha=0.5
p.xaxis.axis_label = 'Time'
p.yaxis.axis_label = 'GulcoseValue(gm/cc)'
box_left = pd.to_datetime('2010-10-4')
box_right = pd.to_datetime('2010-10-6')

#Configure BoxAnnotation
box = BoxAnnotation(left=box_left, right=box_right,
                    line_width=1, line_color='black', line_dash='dashed',
                    fill_alpha=0.2, fill_color='orange')

#Add box layout to figure
p.add_layout(box)

#Add a line renderer
p.line(x,y,line_width=2)

#Show the results
show(p) 

OUTPUT:

Scatter Plot:

Scatter plot can be created easily with the following steps:

• Create a blank figure with necessary arguments: p = figure(...)
• Call a glyph method such as p.circle on the figure
• Show the figure

#Scatter plot Example

#Preparing plot data
from bokeh.sampledata.autompg import autompg 

#See relationships between horse power and acceleration 
x=autompg.hp
y=autompg.accel

# create a new plot with default tools, using figure
p = figure(plot_width=600, plot_height=400,title="Horse Power & Accleration")
p.xaxis.axis_label = 'Horse power'
p.yaxis.axis_label = 'Acceleration'

# add a circle renderer with x and y coordinates, size, color, and alpha
p.circle(x,y, size=15, line_color="navy", fill_color="orange", fill_alpha=0.5) 

# show the results
show(p) 

OUTPUT:

Bar Chart:

A bar chart can be created easily with the following steps:

• Create a blank figure with necessary arguments: p = figure(...)
• Call a glyph method such as p.vbar on the figure
• Show the figure

#Bar chart Example

#Importing necessary modules
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6

#Preparing data as pandas dataframe
gapminder_regions=pd.read_csv('https://github.com/PHI-Toolkit/docker-jupyterhub/raw/master/.bokeh/data/gapminder_regions.csv')
print(gapminder_regions.head(3))

#Data processing to find how many countries are there in each group
df = gapminder_regions.groupby('Group')['ID'].nunique() 
regions=gapminder_regions['Group'].unique()
counts=[]
print(regions)
for x in regions:
    counts.append(df[x])
print(counts)


#Set the x_range to the list of categories above
p = figure(x_range=regions, plot_height=250,plot_width=800, title="No.of Countries in Regions")

#Categorical values can also be used as coordinates
p.vbar(x=regions, top=counts, color=Spectral6,width=0.9)

#Set some properties to make the plot look better
p.xgrid.grid_line_color = None
p.y_range.start = 0

show(p)

OUTPUT:

    Country               Group  ID
0    Angola  Sub-Saharan Africa  AO
1     Benin  Sub-Saharan Africa  BJ
2  Botswana  Sub-Saharan Africa  BW
['Sub-Saharan Africa' 'South Asia' 'Middle East & North Africa' 'America'
 'Europe & Central Asia' 'East Asia & Pacific']
[50, 8, 21, 52, 66, 46]

#Example of Stacked bar chart
from bokeh.palettes import GnBu3, OrRd3
years = ['2017', '2018', '2019']

fashion_items=['Jeans','Bags','Shoe','Shirt','Belt']

exports = {'fashion_items' : fashion_items,
           '2017'   : [10, 13, 22, 33, 22],
           '2018'   : [53, 36, 45, 25, 45],
           '2019'   : [34, 28, 45, 45, 54]}

imports = {'fashion_items' : fashion_items,
           '2017'   : [-20,-15 , -28, -38, -29],
           '2018'   : [-60, -39, -50, -30, -45],
           '2019'   : [-38, -29, -49, -49, -59]}

p = figure(y_range=fashion_items, plot_height=250,plot_width=800, x_range=(-500, 500), title="Fashion items import/export, by year")

p.hbar_stack(years, y='fashion_items', height=0.9, color=GnBu3, source=ColumnDataSource(exports),
             legend_label=["%s exports" % x for x in years])

p.hbar_stack(years, y='fashion_items', height=0.9, color=OrRd3, source=ColumnDataSource(imports),
             legend_label=["%s imports" % x for x in years])

p.y_range.range_padding = 0.1
p.ygrid.grid_line_color = None
p.legend.location = "center_left"

show(p)

OUTPUT:

#Mixed bar chart Example

from bokeh.models import FactorRange

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 [ ("Apples", "2015"), ("Apples", "2016"), ("Apples", "2017"), ("Pears", "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="Fruit Counts by Year")

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)

OUTPUT:

Pie Chart:

Pie chart can be created easily with following steps:

• Create a blank figure with necessary arguments: p = figure(...)
• Call a glyph method such as p.wedge on the figure
• Show the figure

#Importing necessary modules
from math import pi
from bokeh.io import output_file, show
from bokeh.palettes import Spectral6
from bokeh.plotting import figure
from bokeh.transform import cumsum

#Data preparation for plot
gapminder_regions=pd.read_csv('https://github.com/PHI-Toolkit/docker-jupyterhub/raw/master/.bokeh/data/gapminder_regions.csv')
df = gapminder_regions.groupby('Group')['ID'].nunique()
data = pd.Series(df).reset_index(name='value').rename(columns={'Group':'Regions'})
print(data)
data['angle'] = data['value']/data['value'].sum() * 2*pi
data['color'] = Spectral6

#Create necessary figure
p = figure(plot_height=350, title="World Regions by Countries", toolbar_location=None,
           tools="hover", tooltips="@Regions: @value", x_range=(-0.5, 1.0))

#Create wedge for defining pie chart
p.wedge(x=0, y=1, radius=0.4,
        start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
        line_color="white", fill_color='color', legend_field='Regions', source=data)

p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None

show(p)

OUTPUT:

                      Regions  value
0                     America     52
1         East Asia & Pacific     46
2       Europe & Central Asia     66
3  Middle East & North Africa     21
4                  South Asia      8
5          Sub-Saharan Africa     50

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