Creating heatmaps

Heat maps visualize data in a matrix using a set of colors. Originally, heat maps were used to represent prices of financial assets, such as stocks. Bokeh is a Python package that can display heatmaps in an IPython notebook or produce a standalone HTML file.

Getting ready

I have Bokeh 0.9.1 via Anaconda. The Bokeh installation instructions are available at http://bokeh.pydata.org/en/latest/docs/installation.html (retrieved July 2015).

How to do it...

1. The imports are as follows:

   from collections import OrderedDict
   from dautil import data
   from dautil import ts
   from dautil import plotting
   import numpy as np
   import bokeh.plotting as bkh_plt
   from bokeh.models import HoverTool

2. The following function loads temperature data and groups it by year and month:

   def load():
       df = data.Weather.load()['TEMP']
       return ts.groupby_year_month(df)

3. Define a function that rearranges data in a special Bokeh structure:

   def create_source():
       colors = plotting.sample_hex_cmap()
   
       month = []
       year = []
       color = []
       avg = []
   
       for year_month, group in load():
           month.append(ts.short_month(year_month[1]))
           year.append(str(year_month[0]))
           monthly_avg = np.nanmean(group.values)
           avg.append(monthly_avg)
           color.append(colors[min(int(abs(monthly_avg)) - 2, 8)])
   
       source = bkh_plt.ColumnDataSource(
           data=dict(month=month, year=year, color=color, avg=avg)
       )
   
       return year, source

4. Define a function that returns labels for the horizontal axis:

   def all_years():
       years = set(year)
       start_year = min(years)
       end_year = max(years)
   
       return [str(y) for y in range(int(start_year), int(end_year), 5)]

5. Define a plotting function for the heat map that also sets up hover tooltips:

   def plot(year, source):
       fig = bkh_plt.figure(title="De Bilt, NL Temperature (1901 - 2014)",
                            x_range=all_years(), 
                            y_range=list(reversed(ts.short_months())),
                            toolbar_location="left", 
                            tools="resize,hover,save,pan,box_zoom,wheel_zoom")
   
       fig.rect("year", "month", 1, 1, source=source,
           color="color", line_color=None)
   
       fig.xaxis.major_label_orientation = np.pi/3
   
       hover = fig.select(dict(type=HoverTool))
       hover.tooltips = OrderedDict([
           ('date', '@month @year'),
           ('avg', '@avg'),
       ])
   
       bkh_plt.output_notebook()
       bkh_plt.show(fig)

6. Call the functions you defined:

   year, source = create_source()
   plot(year, source)

Refer to the following plot for the end result:

The source code is available in the heat_map.ipynb file in this book's code bundle.

See also

• The Bokeh documentation about embedding Bokeh plots at http://bokeh.pydata.org/en/latest/docs/user_guide/embed.html (retrieved July 2015)