

In [1]:

    
import pandas as pd
import numpy as np

from IPython.core.display import HTML, display

from bokeh.embed import file_html
from bokeh.models import ColumnDataSource, Patches, HoverTool, TapTool, Plot, Range1d, Slider
from bokeh.palettes import Blues9
from bokeh.plotting import vplot
from bokeh.resources import Resources

from constants import PLOT_FORMATS

Get the data assembled and colored

The following code to get the data assembled is explained in more detail in the notebook "Example pretty static map with data & hover"



In [2]:

    
map_data = pd.read_hdf('data/province_map_data.hdf', 'df')
map_data.sort('alpha', inplace=True)
data = pd.read_csv('data/sample_data_by_year.csv')
all_data = map_data.merge(data)

def color_data(data, columns_to_colorify, data_min=None, data_max=None, palette=Blues9):
    # data - the data frame which you are adding colored values to
    # columns_to_colorify - a list of strings which select the columns
    
    if data_min is None:
        num_only = data[columns_to_colorify]
        global_min = num_only.min().min()
        data_min = np.floor(global_min)

    if data_max is None:
        num_only = data[columns_to_colorify]
        global_max = num_only.max().max()
        data_max = np.ceil(global_max)
    
    data_range = data_max - data_min
    bin_factor = data_range / len(palette)
    
    def _get_color(value, palette):
        index = int(value / bin_factor)
        return palette[index - 1]

    for column_name in columns_to_colorify:
        color_name = '%s_color' % column_name
        data[color_name] = data['%s' % column_name].apply(_get_color, args=([palette]))
    return data

colored_data = color_data(all_data, [str(x) for x in range(1990, 2015)])
source = ColumnDataSource(colored_data)

def setup_china_map_plot(start_time, plot_width=600, x_range=[70, 140], y_range=[10, 60], title=""):
    aspect_ratio = (x_range[1] - x_range[0]) / (y_range[1] - y_range[0])
    plot_height = int(plot_width / aspect_ratio)
    x_range = Range1d(x_range[0], x_range[1])
    y_range = Range1d(y_range[0], y_range[1])
    plot = Plot(
        x_range=x_range, 
        y_range=y_range, 
        title=title, 
        plot_width=plot_width, 
        plot_height=plot_height, 
        **PLOT_FORMATS)
    
    countries = Patches(
        xs='xs', 
        ys='ys',
        fill_color='%s_color' % start_time,
        line_color=Blues9[0]
    )    

    plot.add_glyph(source, countries)
    return plot


PLOT_WIDTH = 400
TITLE = 'Random data'
START_TIME = '1990'

map_box = setup_china_map_plot(START_TIME, plot_width=PLOT_WIDTH, title=TITLE)

# Note rendered_html is a notebook thing

css = """
.bk-plot-wrapper table tr td, 
table, td, tr {
  border: none !important;
  padding: 0 !important;
  margin: 0 !important;
}
"""
HTML('<style>{}</style>'.format(css))









    Out[2]:



In [3]:

    
resources = Resources(mode='server', root_url='/tree/')
html = file_html(map_box, resources, "")
display(HTML(html))









    







    
        
        Bokeh Plot



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]: