Python交互图表可视化Bokeh:3. 散点图,,散点图① 基本散点图


散点图

① 基本散点图绘制
② 散点图颜色、大小设置方法
③ 不同符号的散点图

1. 基本散点图绘制

import numpy as npimport pandas as pdimport matplotlib.pyplot as plt% matplotlib inlineimport warningswarnings.filterwarnings(‘ignore‘) # 不发出警告from bokeh.io import output_notebookoutput_notebook()# 导入notebook绘图模块from bokeh.plotting import figure,show# 导入图表绘制、图标展示模块

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# 1、基本散点图绘制s = pd.Series(np.random.randn(80))# 创建数据p = figure(plot_width=600, plot_height=400)p.circle(s.index, s.values,                  # x,y值,也可以写成:x=s.index, y = s.values         size=25, color="navy", alpha=0.5,   # 点的大小、颜色、透明度(注意,这里的color是线+填充的颜色,同时线和填充可以分别上色,参数如下)         fill_color = ‘red‘,fill_alpha = 0.6, # 填充的颜色、透明度         line_color = ‘black‘,line_alpha = 0.8,line_dash = ‘dashed‘,line_width = 2,   # 点边线的颜色、透明度、虚线、宽度         # 同时还有line_cap、line_dash_offset、line_join参数             legend = ‘scatter-circle‘,    # 设置图例         #radius = 2   # 设置点的半径,和size只能同时选一个        )# 创建散点图,基本参数# bokeh对line和fill是同样的设置方法p.legend.location = "bottom_right"# 设置图例位置show(p)

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2. 散点图不同 颜色上色/散点大小 的方法

# 2、散点图不同 颜色上色/散点大小 的方法# ① 数据中有一列专门用于设置颜色 / 点大小from bokeh.palettes import brewerrng = np.random.RandomState(1)df = pd.DataFrame(rng.randn(100,2)*100,columns = [‘A‘,‘B‘])# 创建数据,有2列随机值df[‘size‘] = rng.randint(10,30,100)   # 设置点大小字段# colormap1 = {1: ‘red‘, 2: ‘green‘, 3: ‘blue‘}    # df[‘color1‘] = [colormap1[x] for x in rng.randint(1,4,100)]           # 调色盘1;df[‘color1‘] = np.random.choice([‘red‘, ‘green‘, ‘blue‘], 100) #跟上面两行是一样的;  这两种都是在本身的数据中增加size和color1标签,再去绘制图标;print(df.head())p = figure(plot_width=600, plot_height=400)p.circle(df[‘A‘], df[‘B‘],       # 设置散点图x,y值         line_color = ‘white‘,   # 设置点边线为白色         fill_color = df[‘color1‘],fill_alpha = 0.5,   # 设置内部填充颜色,这里用到了颜色字段         size = df[‘size‘]       # 设置点大小,这里用到了点大小字段,按照size的随机数去设置点的大小        )show(p)

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n = 8colormap2 = brewer[‘Blues‘][n]print(colormap2)df[‘color2‘] = [colormap2[x] for x in rng.randint(0,n,100)]           # 通过调色盘2的方式print(df.head())# # 设置颜色字段# # 通过字典/列表,识别颜色str# # 这里设置了两个调色盘,第二个为蓝色渐变p = figure(plot_width=600, plot_height=400)p.circle(df[‘A‘], df[‘B‘],       # 设置散点图x,y值         line_color = ‘white‘,   # 设置点边线为白色         fill_color = df[‘color2‘],fill_alpha = 0.5,   # 设置内部填充颜色,这里用到了颜色字段         size = df[‘size‘]       # 设置点大小,这里用到了点大小字段,按照size的随机数去设置点的大小        )show(p)

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# 2、散点图不同 颜色上色/散点大小 的方法# ② 遍历数据分开做图rng = np.random.RandomState(1)df = pd.DataFrame(rng.randn(100,2)*100,columns = [‘A‘,‘B‘])df[‘type‘] = rng.randint(0,7,100)print(df.head())# 创建数据;type是做不同类别的一个分组colors = ["red", "olive", "darkred", "goldenrod", "skyblue", "orange", "salmon"]# 创建颜色列表p1 = figure(plot_width=600, plot_height=400)p2 = figure(plot_width=600, plot_height=400)p3 = figure(plot_width=600, plot_height=400)plst = [p1, p2, p3]# for t in df[‘type‘].unique():for t,pi in zip(df[‘type‘].unique()[:3], plst):    #p = figure(plot_width=600, plot_height=400,tools = "pan,wheel_zoom,box_select,lasso_select,reset")    pi.circle(df[‘A‘][df[‘type‘] == t], df[‘B‘][df[‘type‘] == t],       # 设置散点图x,y值             size = 20,alpha = 0.5,             color = colors[t],             legend = ‘type%i‘ % t)    show(pi)    # 通过分类设置颜色

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3. 不同符号的散点图

# 3、不同符号的散点图# asterisk(), circle(), circle_cross(), circle_x(), cross(), diamond(), diamond_cross(), inverted_triangle()# square(), square_cross(), square_x(), triangle(), x()p = figure(plot_width=600, plot_height=400,x_range = [0,3], y_range = [0,7])p.circle_cross(1, 1, size = 30, alpha = 0.5, legend = ‘circle_cross‘)p.asterisk(1, 2, size = 30, alpha = 0.5, legend = ‘asterisk‘)p.circle_x(1, 3, size = 30, alpha = 0.5, legend = ‘circle_x‘)p.cross(1, 4, size = 30, alpha = 0.5, legend = ‘cross‘)p.diamond(1, 5, size = 30, alpha = 0.5, legend = ‘diamond‘)p.diamond_cross(1, 6, size = 30, alpha = 0.5, legend = ‘diamond_cross‘)p.inverted_triangle(2, 1, size = 30, alpha = 0.5, legend = ‘inverted_triangle‘)p.square(2, 2, size = 30, alpha = 0.5, legend = ‘square‘)p.square_cross(2, 3, size = 30, alpha = 0.5, legend = ‘square_cross‘)p.square_x(2, 4, size = 30, alpha = 0.5, legend = ‘square_x‘)p.triangle(2, 5, size = 30, alpha = 0.5, legend = ‘triangle‘)p.x(2, 6, size = 30, alpha = 0.5, legend = ‘x‘)p.legend.location = "bottom_right"# 设置图例位置show(p)# 详细参数可参考文档:http://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.circle

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Python交互图表可视化Bokeh:3. 散点图

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