In [1]:

    

from bokeh.io import output_notebook, show
output_notebook()


    

    






    

        
        Loading BokehJS ...
    






    

In [2]:

    

import bokeh
bokeh.__version__


    

    
Out[2]:





'0.12.3'

In [3]:

    

import pandas as pd

from bokeh.plotting import figure
from bokeh.models import LinearInterpolator
from bokeh.models import ColumnDataSource, HoverTool


    

In [4]:

    

data = pd.read_csv('gpm2.csv', index_col='year')
print(data.dtypes)
data.head()


    

    




country        object
continent      object
lifeExp       float64
population      int64
gdpPercap     float64
dtype: object






    
Out[4]:






  

    

      

      
country
      
continent
      
lifeExp
      
population
      
gdpPercap
    
    

      
year
      

      

      

      

      

    
  
  

    

      
1952
      
Afghanistan
      
Asia
      
28.801
      
8425333
      
779.445314
    
    

      
1957
      
Afghanistan
      
Asia
      
30.332
      
9240934
      
820.853030
    
    

      
1962
      
Afghanistan
      
Asia
      
31.997
      
10267083
      
853.100710
    
    

      
1967
      
Afghanistan
      
Asia
      
34.020
      
11537966
      
836.197138
    
    

      
1972
      
Afghanistan
      
Asia
      
36.088
      
13079460
      
739.981106
    
  

In [5]:

    

source = ColumnDataSource(dict(
    x=data.loc[2007].gdpPercap,
    y=data.loc[2007].lifeExp,
    country=data.loc[2007].country,
    population=data.loc[2007].population,
    continent=data.loc[2007].continent
))


    

In [6]:

    

def make_plot():
    return figure(
        tools=[
            HoverTool(tooltips=[('country','@country'),('population','@population{int}')])],
        height=400,
        x_axis_type='log',
        x_range=(100, 100000),
        y_range=(0, 100)
    )


    

In [7]:

    

p = make_plot()
size_mapper=LinearInterpolator(
    x=[data.population.min(), data.population.max()],
    y=[5,50]
)
p.circle(
    x='x',y='y', 
    size={'field':'population', 'transform': size_mapper},
    alpha=0.6,
    source=source
)
show(p)


    

In [8]:

    

p = make_plot()
size_mapper=LinearInterpolator(
    x=[data.population.min(), data.population.max()+1],
    y=[5,50]
)
p.circle(
    x='x',y='y', 
    size={'field':'population', 'transform': size_mapper},
    alpha=0.6,
    source=source
)
show(p)


    

In [ ]: