import pandas as pd
data = pd.read_csv('assets/gapminder.csv', thousands=',', index_col='Year')
data.head()

from bokeh.io import output_notebook
output_notebook()

Scatter plot of 2010 - income vs life expectancy

data.loc[2010].head()

from bokeh.plotting import figure
#p = figure()
#p.circle(x=data.loc[2010].income, y=data.loc[2010].life)
from bokeh.io import show
#show(p)

01 - plotting

#p = figure(
#    height=400, x_axis_type='log', 
#    x_range=(100, 100000), y_range=(0, 100), 
#    title='2010', x_axis_label='Income', y_axis_label='Life expectancy'
#)
# MAKE A FUNCTION
#p.circle(x=data.loc[2010].income, y=data.loc[2010].life, color='firebrick')
#show(p)

Column Data Source

from bokeh.models import ColumnDataSource
source = ColumnDataSource(
    {
        'column_1': [1, 2, 3],
        'column_2': [3, 4, 5]
    }
)
#source = ColumnDataSource({
#    'income': data.loc[2010].income,
#    'life': data.loc[2010].life,
#    'country': data.loc[2010].Country
#})

02 - column data source

Now we can show regions by color

regions = list(data.region.unique())
regions

from bokeh.palettes import Spectral6
Spectral6

def get_color(r):
    return Spectral6[regions.index(r.region)]
data['region_color'] = data.apply(get_color, axis=1)
data.head()

#p.circle(x='income', y='life', size=20, alpha=0.6, color='color', source=source)
#show(p)

Add a hover

from bokeh.models import HoverTool
#hover = HoverTool(tooltips='@country', show_arrow=False)
#p.circle(x='income', y='life', size=20, alpha=0.6, color='color', source=source)
#p.add_tools(hover)

03 - interactions

Examples Interlude - A1 - Extra Resources

04 - styling

05 - data transformations

Working plot

from bokeh.models import NumeralTickFormatter


source = ColumnDataSource({
    'income': data.loc[2010].income,
    'life': data.loc[2010].life,
    'country': data.loc[2010].Country,
    'color': data.loc[2010].region_color,
    'population': data.loc[2010].population
})

from bokeh.models import LinearInterpolator
size_mapper = LinearInterpolator(
    x=[data.population.min(), data.population.max()],
    y=[5, 50]
)

p = figure(
    height=400, x_axis_type='log', 
    x_range=(100, 100000), y_range=(0, 100), 
    title='2010', x_axis_label='Income', y_axis_label='Life expectancy',
    tools=[HoverTool(tooltips='@country', show_arrow=False)]
)
p.xaxis[0].formatter = NumeralTickFormatter(format="$0,")
p.circle(
    x='income', y='life', 
    size={'field': 'population', 'transform': size_mapper}, 
    color='color',
    alpha=0.6,  
    source=source,
)
show(p)

Interactivity with slider

from bokeh.io import push_notebook
source = ColumnDataSource({
    'income': data.loc[2010].income,
    'life': data.loc[2010].life,
    'country': data.loc[2010].Country,
    'color': data.loc[2010].region_color,
    'population': data.loc[2010].population
})

def update(year):
    new_data = dict(
        income=data.loc[year].income, 
        life=data.loc[year].life, 
        country=data.loc[year].Country,
        population=data.loc[year].population,
        color=data.loc[year].region_color,
    )
    source.data = new_data
    p.title.text = str(year)
    push_notebook()
    
size_mapper = LinearInterpolator(
    x=[data.population.min(), data.population.max()],
    y=[5, 50]
)
p = figure(
    height=400, x_axis_type='log', 
    x_range=(100, 100000), y_range=(0, 100), 
    title='2010', x_axis_label='Income', y_axis_label='Life expectancy',
    tools=[HoverTool(tooltips='@country', show_arrow=False)]
)
p.xaxis[0].formatter = NumeralTickFormatter(format="$0,")
p.circle(
    x='income', y='life', 
    size={'field': 'population', 'transform': size_mapper}, 
    color='color',
    alpha=0.6,  
    source=source,
)
show(p, notebook_handle=True)

from ipywidgets import interact, IntSlider
slider = IntSlider(min=1960, max=2014, value=2010)
interact(update, year=slider)

06 - server

07 - sharing

If we haven't talked about it yet:

• tab completing in notebook
• fuzzy search