python交互式绘图库_一个交互式可视化Python库——Bokeh

本篇为《Python数据可视化实战》第十篇文章，我们一起学习一个交互式可视化Python库——Bokeh。

Bokeh基础

Bokeh是一个专门针对Web浏览器的呈现功能的交互式可视化Python库。这是Bokeh与其它可视化库最核心的区别。

Bokeh绘图步骤

①获取数据

②构建画布figure()

③添加图层，绘图line，circle，square，scatter，multi_line等；参数co

lor，legend

④自定义视觉属性

⑤选择性展示折线数据，建立复选框激活显示，复选框(checkbox)

导入库和数据

import numpy as np

import bokeh

from bokeh.layouts import gridplot

from bokeh.plotting import figure, output_file, show

图表实例

1.散点图

import numpy as np

import bokeh

from bokeh.layouts import gridplot

from bokeh.plotting import figure, output_file, show

# output_file("patch.html") #输出网页形式

p = figure(plot_width=100, plot_height=100)

#数据

N=9

x=np.linspace(-2,2,N)

y=x**2

sizes=np.linspace(10,20,N)

xpts=np.array([-0.09,-0.12,0.0,0.12,0.09])

ypts=np.array([-0.1,0.02,0.1,0.02,-0.1])

p=figure(title="annular_wedge")

p.annular_wedge(x,y,10,20,0.3,4.1,color="#8888ee",inner_radius_units="screen",outer_radius_units="screen")

# Set to output the plot in the notebook

output_notebook()

show(p)

2.多分类的散点图

from bokeh.sampledata.iris import flowers

from bokeh.plotting import figure

from bokeh.io import show, output_notebook

#配色

colormap={'setosa':'red','versicolor':'green','virginica':'blue'}

colors=[colormap[x] for x in flowers['species']]

#画布

p=figure(title='Tris Morphology')

#绘图

#flowers['petal_length']为x,flowers['petal_width']为y，fill_alpha=0.3为填充透明度

p.circle(flowers['petal_length'],flowers['petal_width'],color=colors,fill_alpha=0.3,size=10)

#显示

output_notebook()

show(p)

3.数值大小以散点图大小来表示

import numpy as np

from bokeh.sampledata.iris import flowers

from bokeh.plotting import figure

from bokeh.io import show, output_notebook

x=[1,2,3,4]

y=[5,7,9,12]

sizes=np.array(y)+10 #气泡大小

p=figure(title='bubble chart')

p=figure(plot_width=300,plot_height=300)

p.scatter(x,y,marker="circle",size=sizes,color="navy")

output_notebook()

show(p)

4.折线图line

from bokeh.layouts import column, gridplot

from bokeh.models import BoxSelectTool, Div

from bokeh.plotting import figure

from bokeh.io import show, output_notebook

# 数据

x = [1, 2, 3, 4, 5, 6, 7]

y = [6, 7, 2, 4, 5, 10, 4]

# 画布：坐标轴标签，画布大小

p = figure(title="line example", x_axis_label='x', y_axis_label='y', width=400, height=400)

# 画图：数据、图例、线宽

p.line(x, y, legend="Temp.", line_width=2) # 折线图

# 显示

output_notebook()

show(p)

5.同时展示不同函数，以散点和折线方式

# 数据，同时展示不同函数，以散点和折线方式

x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]

y0 = [i**2 for i in x]

y1 = [10**i for i in x]

y2 = [10**(i**2) for i in x]

# 创建画布

p = figure(

tools="pan,box_zoom,reset,save",

y_axis_type="log", title="log axis example",

x_axis_label='sections', y_axis_label='particles',

width=700, height=350) # y轴类型：log指数或linear线性

# 增加图层，绘图

p.line(x, x, legend="y=x")

p.circle(x, x, legend="y=x", fill_color="white", size=8)

p.line(x, y0, legend="y=x^2", line_width=3)

p.line(x, y1, legend="y=10^x", line_color="red")

p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)

p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")

# 显示

output_notebook()

show(p)

6.不同颜色不同形状表示不同类别的事物

# 数据，同时展示不同函数，以散点和折线方式

x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]

y0 = [i**2 for i in x]

y1 = [10**i for i in x]

y2 = [10**(i**2) for i in x]

# 创建画布

p = figure(

tools="pan,box_zoom,reset,save",

y_axis_type="log", title="log axis example",

x_axis_label='sections', y_axis_label='particles',

width=700, height=350) # y轴类型：log指数或linear线性

# 增加图层，绘图

p.line(x, x, legend="y=x")

p.circle(x, x, legend="y=x", fill_color="white", size=8)

p.line(x, y0, legend="y=x^2", line_width=3)

p.line(x, y1, legend="y=10^x", line_color="red")

p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)

p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")

# 显示

output_notebook()

show(p)

7.不同函数设置创建复选框库选择性显示

x = np.linspace(0, 4 * np.pi, 100)

# 画布

p = figure()

# 折线属性

props = dict(line_width=4, line_alpha=0.7)

# 绘图3条函数序列

l0 = p.line(x, np.sin(x), color=Viridis3[0], legend="Line 0", **props)

l1 = p.line(x, 4 * np.cos(x), color=Viridis3[1], legend="Line 1", **props)

l2 = p.line(x, np.tan(x), color=Viridis3[2], legend="Line 2", **props)

# 复选框激活显示，复选框(checkbox)，三个函数序列可选择性展示出来

checkbox = CheckboxGroup(labels=["Line 0", "Line 1", "Line 2"],

active=[0, 1, 2], width=100)

#

checkbox.callback = CustomJS(args=dict(l0=l0, l1=l1, l2=l2, checkbox=checkbox), code="""

l0.visible = 0 in checkbox.active;

l1.visible = 1 in checkbox.active;

l2.visible = 2 in checkbox.active;

""")

# 添加图层

layout = row(checkbox, p)

output_notebook()

# 显示

show(layout)

8.收盘价的时序图走势和散点图

import numpy as np

from bokeh.plotting import figure

from bokeh.io import show, output_notebook

from bokeh.layouts import row #row()的作用是将多个图像以行的方式放到同一张图中

from bokeh.palettes import Viridis3

from bokeh.models import CheckboxGroup, CustomJS #CheckboxGroup 创建复选框库

# 数据

aapl = np.array(AAPL['adj_close'])

aapl_dates = np.array(AAPL['date'], dtype=np.datetime64)

window_size = 30

window = np.ones(window_size)/float(window_size)

aapl_avg = np.convolve(aapl, window, 'same')

# 画布

p = figure(width=800, height=350, x_axis_type="datetime")

# 图层

p.circle(aapl_dates, aapl, size=4, color='darkgrey', alpha=0.2, legend='close') #散点图

p.line(aapl_dates, aapl_avg, color='red', legend='avg') #折线时序图

# 自定义视觉属性

p.title.text = "AAPL One-Month Average"

p.legend.location = "top_left"

p.grid.grid_line_alpha=0

p.xaxis.axis_label = 'Date'

p.yaxis.axis_label = 'Price'

p.ygrid.band_fill_color="gray"

p.ygrid.band_fill_alpha = 0.1

p.legend.click_policy="hide" # 点击图例显示隐藏数据

# 显示结果

output_notebook()

show(p)