[雪峰磁针石博客]Bokeh数据可视化工具1快速入门
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) 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 本文最新版本地址 讨论 钉钉免费群21745728 qq群144081101 567351477 本文涉及的python测试开发库 谢谢点赞！ 本文相关海量书籍下载 代码仓库
