[雪峰磁针石博客]Bokeh数据可视化工具1快速入门

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[雪峰磁针石博客]Bokeh数据可视化工具1快速入门

python人工智能命理 2018-08-18 06:53:34 浏览907
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简介

数据可视化python库参考

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)

635
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)

631
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)

615
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)

700
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)

625
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

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