Python使用plotly绘制数据图表的方法

转载：http://www.jb51.net/article/118936.htm

本篇文章主要介绍了Python使用plotly绘制数据图表的方法，实例分析了plotly绘制的技巧。

导语：使用 python-plotly 模块来进行压测数据的绘制，并且生成静态 html 页面结果展示。

不少小伙伴在开发过程中都有对模块进行压测的经历，压测结束后大家往往喜欢使用Excel处理压测数据并绘制数据可视化视图，但这样不能很方便的使用web页面进行数据展示。本文将介绍使用python-plotly模块来进行压测数据的绘制，并且生成静态html页面方便结果展示。

Plotly绘图实例：

1、line-plots

绘图效果：

生成的html页面在右上角提供了丰富的交互工具。

代码：

 import plotly.plotly
 import plotly.graph_objs as pg
 
 def line_plots(output_path):
     """
     绘制普通线图
     """
     # 数据，x为横坐标，y,z为纵坐标的两项指标，三个array长度相同
     dataset = {'x': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
                'y': [5, 4, 1,3, 11, 2, 6, 7, 19, 20],
                'z': [12, 9, 0, 0, 3, 25, 8, 17, 22, 5]}
 
     data_g = []
     # 分别插入 y, z
     tr_x = pg.Scatter(
         x=dataset['x'],
         y=dataset['y'],
         name='y'
     )
     data_g.append(tr_x)
     tr_z = pg.Scatter(
         x=dataset['x'],
         y=dataset['z'],
         name='z'
     )
     data_g.append(tr_z)
 
     # 设置layout,指定图表title,x轴和y轴名称
     layout = pg.Layout(title="line plots", xaxis={'title': 'x'}, yaxis={'title': 'value'})
     # 将layout设置到图表
     fig = pg.Figure(data=data_g, layout=layout)
     # 绘图,输出路径为output_path参数指定
     plotly.offline.plot(fig, filename=output_path)
 
 if __name__ == '__main__':
     line_plots(output_path)

2、scatter-plots

绘图效果：

 import plotly.plotly
 import plotly.graph_objs as pg
 
 def scatter_plots(output_path):
   '''
   绘制散点图
   '''
   dataset = {'x':[0,1,2,3,4,5,6,7,8,9],
         'y':[5,4,1,3,11,2,6,7,19,20],
         'text':['5_txt','4_txt','1_txt','3_txt','11_txt','2_txt','6_txt','7_txt','19_txt','20_txt']}
 
   data_g = []
 
   tr_x = pg.Scatter(
     x = dataset['x'],
     y = dataset['y'],
     text = dataset['text'],
     textposition='top center',
     mode='markers+text',
     name = 'y'
   )
   data_g.append(tr_x)
 
   layout = pg.Layout(title="scatter plots", xaxis={'title':'x'}, yaxis={'title':'value'})
   fig = pg.Figure(data=data_g, layout=layout)
   plotly.offline.plot(fig, filename=output_path)
 
 if __name__ == '__main__':
     scatter_plots("C:/Users/fuqia/Desktop/scatter.html")

3、bar-charts

绘图效果：

代码：

 import plotly.plotly
 import plotly.graph_objs as pg
 
 def bar_charts(name):
     '''
     绘制柱状图
     '''
     dataset = {'x':['Windows', 'Linux', 'Unix', 'MacOS'],
         'y1':[45, 26, 37, 13],
         'y2':[19, 27, 33, 21]}
     data_g = []
     tr_y1 = pg.Bar(
         x = dataset['x'],
         y = dataset['y1'],
         name = 'v1'
     )
     data_g.append(tr_y1)
 
     tr_y2 = pg.Bar(
         x = dataset['x'],
         y = dataset['y2'],
         name = 'v2'
     )
     data_g.append(tr_y2)
     layout = pg.Layout(title="bar charts", xaxis={'title':'x'}, yaxis={'title':'value'})
     fig = pg.Figure(data=data_g, layout=layout)
     plotly.offline.plot(fig, filename=name)
 
 if __name__ == '__main__':
     bar_charts("C:/Users/fuqia/Desktop/bar.html")

4、pie-charts

绘图效果：

代码：

 import plotly.plotly
 import plotly.graph_objs as pg
 
 def pie_charts(name):
     '''
     绘制饼图
     '''
     dataset = {'labels': ['Windows', 'Linux', 'Unix', 'MacOS', 'Android', 'iOS'],
                'values': [280, 25, 10, 100, 250, 270]}
     data_g = []
     tr_p = pg.Pie(
         labels = dataset['labels'],
         values = dataset['values']
     )
     data_g.append(tr_p)
     layout = pg.Layout(title="pie charts")
     fig = pg.Figure(data=data_g, layout=layout)
     plotly.offline.plot(fig, filename=name)
 
 if __name__ == '__main__':
     pie_charts("C:/Users/fuqia/Desktop/bar.html")

5、filled-area-plots

本例是绘制具有填充效果的堆叠线图，适合分析具有堆叠百分比属性的数据

绘图效果：

代码：

 import plotly.plotly
 import plotly.graph_objs as pg
 
 def filled_area_plots(name):
     '''
     绘制堆叠填充的线图
     '''
     dataset = {'x':[0,1,2,3,4,5,6,7,8,9],
           'y1':[5,4,1,3,11,2,6,7,19,20],
           'y2':[12,9,0,0,3,25,8,17,22,5],
           'y3':[13,22,46,1,15,4,18,11,17,20]}
 
     #计算y1,y2,y3的堆叠占比
     dataset['y1_stack'] = dataset['y1']
     dataset['y2_stack'] = [y1+y2 for y1, y2 in zip(dataset['y1'], dataset['y2'])]
     dataset['y3_stack'] = [y1+y2+y3 for y1, y2, y3 in zip(dataset['y1'], dataset['y2'], dataset['y3'])]
 
     dataset['y1_text'] = ['%s(%s%%)'%(y1, y1*100/y3_s) for y1, y3_s in zip(dataset['y1'], dataset['y3_stack'])]
     dataset['y2_text'] = ['%s(%s%%)'%(y2, y2*100/y3_s) for y2, y3_s in zip(dataset['y2'], dataset['y3_stack'])]
     dataset['y3_text'] = ['%s(%s%%)'%(y3, y3*100/y3_s) for y3, y3_s in zip(dataset['y3'], dataset['y3_stack'])]
 
     data_g = []
     tr_1 = pg.Scatter(
       x = dataset['x'],
       y = dataset['y1_stack'],
       text = dataset['y1_text'],
       hoverinfo = 'x+text',
       mode = 'lines',
       name = 'y1',
       fill = 'tozeroy' #填充方式: 到x轴
     )
     data_g.append(tr_1)
 
     tr_2 = pg.Scatter(
       x = dataset['x'],
       y = dataset['y2_stack'],
       text = dataset['y2_text'],
       hoverinfo = 'x+text',
       mode = 'lines',
       name = 'y2',
       fill = 'tonexty' #填充方式:到下方的另一条线
     )
     data_g.append(tr_2)
 
     tr_3 = pg.Scatter(
       x = dataset['x'],
       y = dataset['y3_stack'],
       text = dataset['y3_text'],
       hoverinfo = 'x+text',
       mode = 'lines',
       name = 'y3',
       fill = 'tonexty'
     )
     data_g.append(tr_3)
 
     layout = pg.Layout(title="field area plots", xaxis={'title':'x'}, yaxis={'title':'value'})
     fig = pg.Figure(data=data_g, layout=layout)
     plotly.offline.plot(fig, filename=name)
 
 if __name__ == '__main__':
     filled_area_plots("C:/Users/fuqia/Desktop/bar.html")

小结

本文介绍了利用python-plotly绘制数据图的方法，实例中 线图(line plots)、散点图(scatter plots)、柱状图(bar charts)、饼图(pie charts)以及填充堆叠线图(filled area plots)这五种典型的图表基本上涵盖了大部分类型的测试数据，各位小伙伴可以加以变形绘制出更多的漂亮图标。

文中所示代码：test_plotly_jb51.rar

参考资料

1. https://plot.ly/python/basic-charts/

2. https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf