简介
python数据可视化库最突出的为Matplotlib、Seaborn和Bokeh。前两个，Matplotlib和Seaborn，绘制静态图。Bokeh可以绘制交互式图。
安装
conda install bokeh
pip2 install bokeh
pip3 install bokeh
检验安装
from bokeh.plotting import figure, output_file, show
#HTML file to output your plot into
output_file("bokeh.html")
#Constructing a basic line plot
x = [1,2,3]
y = [4,5,6]
p = figure()
p.line(x,y)
show(p)
image.png
问题讨论：
https://groups.google.com/a/anaconda.com/forum/#!forum/bokeh
bug跟踪：https://github.com/bokeh/bokeh/issues
应用程序：Bokeh应用程序是在浏览器中运行的Bokeh渲染文档
Glyph：Glyph是Bokeh的基石，它们是线条，圆形，矩形等。
服务器：Bokeh服务器用于共享和发布交互式图表
小部件Widgets:：Bokeh中的小部件是滑块，下拉菜单等
输出方法有：output_file('plot.html')和output_notebook()
构建图片的方式：
#Code to construct a figure
from bokeh.plotting import figure
# create a Figure object
p = figure(plot_width=500, plot_height=400, tools="pan,hover")
绘图基础
线状图
#Creating a line plot
#Importing the required packages
from bokeh.io import output_file, show
from bokeh.plotting import figure
#Creating our data arrays used for plotting the line plot
x = [5,6,7,8,9,10]
y = [1,2,3,4,5,6]
#Calling the figure() function to create the figure of the plot
plot = figure()
#Creating a line plot using the line() function
plot.line(x,y)
#Creating markers on our line plot at the location of the intersection between x and y
plot.cross(x,y, size = 15)
#Output the plot
output_file('line_plot.html')
show(plot)
image.png
柱形图
#Creating bar plots
#Importing the required packages
from bokeh.plotting import figure, show, output_file
#Points on the x axis
x = [8,9,10]
#Points on the y axis
y = [1,2,3]
#Creating the figure of the plot
plot = figure()
#Code to create the barplot
plot.vbar(x,top = y, color = "blue", width= 0.5)
#Output the plot
output_file('barplot.html')
show(plot)
image.png
补丁图
#Creating patch plots
#Importing the required packages
from bokeh.io import output_file, show
from bokeh.plotting import figure
#Creating the regions to map
x_region = [[1,1,2,], [2,3,4], [2,3,5,4]]
y_region = [[2,5,6], [3,6,7], [2,4,7,8]]
#Creating the figure
plot = figure()
#Building the patch plot
plot.patches(x_region, y_region, fill_color = ['yellow', 'black', 'green'], line_color = 'white')
#Output the plot
output_file('patch_plot.html')
show(plot)
image.png
散列图
#Creating scatter plots
#Importing the required packages
from bokeh.io import output_file, show
from bokeh.plotting import figure
#Creating the figure
plot = figure()
#Creating the x and y points
x = [1,2,3,4,5]
y = [5,7,2,2,4]
#Plotting the points with a cirle marker
plot.circle(x,y, size = 30)
#Output the plot
output_file('scatter.html')
show(plot)
image.png
更多资源
#- cross()
#- x()
#- diamond()
#- diamond_cross()
#- circle_x()
#- circle_cross()
#- triangle()
#- inverted_triangle()
#- square()
#- square_x()
#- square_cross()
#- asterisk()
#Adding labels to the plot
plot.figure(x_axis_label = "Label name of x axis", y_axis_label = "Label name of y axis")
#Customizing transperancy of the plot
plot.circle(x, y, alpha = 0.5)
plot.circle(x, y, alpha = 0.5)
参考资料
本文涉及的python中文资源 请在github上点赞，谢谢！
