python图表绘制及可视化

用于数据分析及可视化(更多内容可以阅读官方文档)
1.Matplotlib
子库：pyplot,与MATLAB相似
函数式绘图

import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(0,10,1000)
y=np.sin(x)
z=np.cos(x**2)
plt.figure(figsize=(8,4))
plt.plot(x,y,label="$sin(x)$",color="red",linewidth=2)
plt.plot(x,z,"b--",label="$cos(X^2)$")
plt.xlabel("Time(s)")
plt.ylabel("Volt")
plt.title("PyPlot First Example")
plt.ylim(-1.2,1,2)
plt.legend()
plt.show()

子图声明方法

import numpy as np
import matplotlib.pyplot as plt
plt.subplot(2,1,1)  #参数依次为行，列，第几项
plt.subplot(2,2,3)
plt.subplot(2,2,4)
plt.show()

柱状图

import numpy as np
import matplotlib.pylab as plt
plt.subplot(2,1,1)
n=12
X=np.arange(n)
Y1=(1-X/float(n))*np.random.uniform(0.5,1.0,n)
Y2=(1-X/float(n))*np.random.uniform(0.5,1.0,n)
plt.bar(X,+Y1,facecolor='#9999ff',edgecolor='white')
plt.bar(X,-Y2,facecolor='#9999ff',edgecolor='white')
for x,y in zip(X,Y1):
       plt.text(x+0.4,y+0.05,'%.2f' % y ,ha='center',va='bottom')
       plt.ylim(-1.25,+1.25)
plt.subplot(2,2,3)
plt.subplot(2,2,4)
plt.show()

饼状图和曲线图

import numpy as np
import matplotlib.pylab as plt
plt.subplot(2,2,3)
n=20
Z=np.random.uniform(0,1,n)
plt.pie(Z)
plt.subplot(2,2,4)
X=np.linspace(-np.pi,np.pi,256,endpoint=True)
Y_C,Y_S=np.cos(X),np.sin(X)
plt.plot(X,Y_C,color="blue",linewidth=2.5,linestyle="-")
plt.plot(X,Y_S,color="red",linewidth=2.5,linestyle="-")
plt.xlim(X.min()*1.1,X.max()*1.1)
plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],[r'$-\pi$',r'$-\pi/2$',r'$0$',r'$+\pi/2$',r'$+\pi$'])
plt.ylim(Y_C.min()*1.1,Y_C.max()*1.1)
plt.yticks([-1,0,+1],[r'$-1$',r'$0$',r'$+1$'])
plt.show()

更多图表类型可以去Matplotlib画廊(Gallery)

2.Bokeh
针对浏览器，d3.js
交互式绘图
简单折线图

from bokeh.plotting import figure,output_file,show
x=[1,2,3,4,5]
y=[6,7,2,4,5]
#输出为静态文件
output_file("lines.html",title="line plot example")
p=figure(title="simple line example",x_axis_label='x',y_axis_label='y')
p.line(x,y,legend="Line A.",line_width=2)
show(p)