unemployment.py¶
A categorical heatmap using unemployment data. This example demonstrates
adding a ColorBar
to a plot.
Details
- Bokeh APIs
- More info
- Keywords
categorical, colorbar, heatmap, hover, tooltip
from math import pi
import pandas as pd
from bokeh.models import BasicTicker, ColorBar, LinearColorMapper, PrintfTickFormatter
from bokeh.plotting import figure, show
from bokeh.sampledata.unemployment1948 import data
data['Year'] = data['Year'].astype(str)
data = data.set_index('Year')
data.drop('Annual', axis=1, inplace=True)
data.columns.name = 'Month'
years = list(data.index)
months = list(data.columns)
# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()
# this is the colormap from the original NYTimes plot
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
mapper = LinearColorMapper(palette=colors, low=df.rate.min(), high=df.rate.max())
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
p = figure(title="US Unemployment ({0} - {1})".format(years[0], years[-1]),
x_range=years, y_range=list(reversed(months)),
x_axis_location="above", width=900, height=400,
tools=TOOLS, toolbar_location='below',
tooltips=[('date', '@Month @Year'), ('rate', '@rate%')])
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "7px"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
p.rect(x="Year", y="Month", width=1, height=1,
source=df,
fill_color={'field': 'rate', 'transform': mapper},
line_color=None)
color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size="7px",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6, border_line_color=None)
p.add_layout(color_bar, 'right')
show(p)