1、网格图
import matplotlib.pyplot as pltimport numpy as npa=np.arange(0.0,5.0,0.02)plt.plot(a,np.cos(2*np.pi*a),'b--')plt.xlabel('横轴：时间',fontproperties='SimHei',fontsize=15,color='green')plt.ylabel('纵轴：振幅',fontproperties='SimHei',fontsize=15,color='red')plt.title(r'正弦波实例 $y=cos(2\pi x)$',fontproperties='SimHei',fontsize=15,color='yellow') #坐标图名称plt.text(3,1,'(3,1)') #某一点的文本值plt.annotate('(2,1)',xy=(2,1),xytext=(3,1.5),arrowprops=dict(facecolor='pink',shrink=0.1,width=2)) #shrink表示文本到箭头末端的距离plt.axis([-1,6,-2,2]) #横、纵坐标范围plt.grid(True) #网格线plt.show()

2、饼图
import matplotlib.pyplot as pltlabels='sleeping','studying','eating','exercising' #每一块的标签sizes=[15,30,45,10] #所占百分比explode=(0,0.1,0,0) #所对应一块的分离情况plt.pie(sizes,explode,labels=labels,autopct='%1.1f%%',shadow=True,startangle=90) plt.axis('equal') #以正圆展示plt.show()

3、直方图
import matplotlib.pyplot as pltimport numpy as npnp.random.seed(0) #随机种子mu,sigma=100,20 #正态分布的两个参数a=np.random.normal(mu,sigma,size=100) #生产服从正太分布的随机数组元素plt.hist(a,30,normed=1,histtype='stepfilled',facecolor='g',alpha=0.75) #30代表直方图中的方块数plt.title('Histogrm')plt.show()

4、极坐标图
import matplotlib.pyplot as pltimport numpy as npN=10theta=np.linspace(0.0,2*np.pi,N,endpoint=False)radii=10*np.random.rand(N)width=np.pi/4*np.random.rand(N)ax=plt.subplot(111,projection='polar')bars=ax.bar(theta,radii,width=width,bottom=0.0)for r,bar in zip(radii,bars):bar.set_facecolor(plt.cm.viridis(r/10.))bar.set_alpha(0.5)plt.show()
5、散点图
import matplotlib.pyplot as pltimport numpy as npfig,ax=plt.subplots() #面向对象绘图ax.plot(10*np.random.rand(100),10*np.random.randn(100),'o') #直接利用函数也可以 plt.plot(10*np.random.rand(100),10*np.random.randn(100),'go')ax.set_title('Simple Scatter')plt.show()
