import pandas as pd
from bokeh.charts import HeatMap, bins, output_file, show
from bokeh.layouts import column, gridplot
from bokeh.palettes import RdYlGn6, RdYlGn9
from bokeh.sampledata.autompg import autompg
from bokeh.sampledata.unemployment1948 import data
# setup data sources
data = data.copy()
data['Year'] = data['Year'].astype(str)
unempl = pd.melt(data, var_name='Month', value_name='Unemployment', id_vars=['Year'])
fruits = {'fruit': ['apples', 'apples', 'apples', 'apples', 'apples',
'pears', 'pears', 'pears', 'pears', 'pears',
'bananas', 'bananas', 'bananas', 'bananas', 'bananas'],
'fruit_count': [4, 5, 8, 12, 4, 6, 5, 4, 8, 7, 1, 2, 4, 8, 12],
'year': [2009, 2010, 2011, 2012, 2013, 2009, 2010, 2011, 2012, 2013, 2009, 2010,
2011, 2012, 2013]}
fruits['year'] = [str(yr) for yr in fruits['year']]
hm1 = HeatMap(autompg, x=bins('mpg'), y=bins('displ'))
hm2 = HeatMap(autompg, x=bins('mpg'), y=bins('displ'), values='cyl', stat='mean')
hm3 = HeatMap(autompg, x=bins('mpg'), y=bins('displ', bins=15),
values='cyl', stat='mean')
hm4 = HeatMap(autompg, x=bins('mpg'), y='cyl', values='displ', stat='mean')
hm5 = HeatMap(autompg, y=bins('displ'), x=bins('mpg'), values='cyl', stat='mean',
spacing_ratio=0.9)
hm6 = HeatMap(autompg, x=bins('mpg'), y=bins('displ'), stat='mean', values='cyl',
palette=RdYlGn6)
hm7 = HeatMap(autompg, x=bins('mpg'), y=bins('displ'), stat='mean', values='cyl',
palette=RdYlGn9)
hm8 = HeatMap(autompg, x=bins('mpg'), y=bins('displ'), values='cyl',
stat='mean', legend='top_right')
hm9 = HeatMap(fruits, y='year', x='fruit', values='fruit_count', stat=None)
hm10 = HeatMap(unempl, x='Year', y='Month', values='Unemployment', stat=None,
sort_dim={'x': False}, width=900, plot_height=500)
output_file("heatmap.html", title="heatmap.py example")
show(column(
gridplot(hm1, hm2, hm3, hm4, hm5, hm6, hm7, hm8, hm9,
ncols=2, plot_width=400, plot_height=400),
hm10)
)
