Python使用Bokeh进行交互式数据可视化

from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.iris import flowers
# assign custom colors to represent each
# class of data in a dictionary format
colormap = {'setosa': 'red', 'versicolor': 'green',
			'virginica': 'blue'}
colors = [colormap[x] for x in flowers['species']]
# title for the graph
p = figure(title="Iris Morphology")
# label on x-axis
p.xaxis.axis_label = 'Petal Length'
# label on y-axis
p.yaxis.axis_label = 'Petal Width'
# plot each datapoint as a circle
# with custom attributes.
p.circle(flowers["petal_length"],
		flowers["petal_width"],
		color=colors,
		fill_alpha=0.3,
		size=15)
# you can save the output as an
# interactive html file
output_file("iris1.html", title="iris.py example")
# display the generated plot of graph
show(p)

from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral10
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
output_file("fruits_bar_chart.html") #output save file name
# creating custom data
fruits = ['Apples', 'Pears', 'Nectarines',
		'Plums', 'Grapes', 'Strawberries',
		'bananas','berries','pineapples','litchi']
counts = [51, 34, 4, 28, 119, 79, 15, 68, 26, 88]
# mapping counts with classes as a dictionary
source = ColumnDataSource(data=dict(fruits=fruits,
									counts=counts))
# initializing the figure
p = figure(x_range=fruits,
		plot_width=800,
		plot_height=350,
		toolbar_location=None,
		title="Fruit Counts")
# assigning various attributes to plot
p.vbar(x='fruits', top='counts',
	width=1, source=source,
	legend_field="fruits",
	line_color='white',
	fill_color=factor_cmap('fruits',
							palette=Spectral10,
							factors=fruits))
p.xgrid.grid_line_color = None
p.y_range.start = 0
p.y_range.end = 150
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
# display output
show(p)