机器学习精简教程之四——用matplotlib绘制精美的图表

本文转自：http://www.shareditor.com/blogshow/?blogId=55

绘制一元函数图像y=ax+b

import matplotlib.pyplot as pltimport numpy as npplt.figure() # 实例化作图变量plt.title('single variable') # 图像标题plt.xlabel('x') # x轴文本plt.ylabel('y') # y轴文本plt.axis([0, 5, 0, 10]) # x轴范围0-5，y轴范围0-10plt.grid(True) # 是否绘制网格线xx = np.linspace(0, 5, 10) # 在0-5之间生成10个点的向量plt.plot(xx, 2*xx, 'g-') # 绘制y=2x图像，颜色green，形式为线条plt.show() # 展示图像

绘制正弦曲线y=sin(x)

import matplotlib.pyplot as pltimport numpy as npplt.figure() # 实例化作图变量plt.title('single variable') # 图像标题plt.xlabel('x') # x轴文本plt.ylabel('y') # y轴文本plt.axis([-12, 12, -1, 1]) # x轴范围-12到12，y轴范围-1到1plt.grid(True) # 是否绘制网格线xx = np.linspace(-12, 12, 1000) # 在-12到12之间生成1000个点的向量plt.plot(xx, np.sin(xx), 'g-', label="$sin(x)$") # 绘制y=sin(x)图像，颜色green，形式为线条plt.plot(xx, np.cos(xx), 'r--', label="$cos(x)$") # 绘制y=cos(x)图像，颜色red，形式为虚线plt.legend() # 绘制图例plt.show() # 展示图像

绘制多轴图

import matplotlib.pyplot as pltimport numpy as npdef draw(plt):    plt.axis([-12,12,-1,1])#x轴和y轴的范围    plt.grid(True)#是否绘制网格线    xx = np.linspace(-12,12,1000)#在-12到12之间生成1000个点的向量    plt.plot(xx,np.sin(xx),'g-',label="&sin(x)$")#绘制y=sin(x)图像，颜色green，形式为线条    plt.plot(xx,np.cos(xx),'r--',label="&cos(x)$")#绘制y=cos(x)图像，颜色为red，形式为虚线    plt.legend()#绘制图例plt.figure()#实例化作图变量plt1 = plt.subplot(2,2,1)#两行两列中的第1张图draw(plt1)plt2 = plt.subplot(2,2,2)#两行两列中的第2张图draw(plt2)plt3 = plt.subplot(2,2,3)#两行两列中的第3张图draw(plt3)plt4 = plt.subplot(2,2,4)#两行两列中的第4张图draw(plt4)plt.show()#得将画好的图显示出来啊

绘制3D图像

from mpl_toolkits.mplot3d import Axes3Dimport numpy as npimport matplotlib.pyplot as pltfig = plt.figure()ax = fig.add_subplot(1,1,1,projection='3d')theta = np.linspace(-4 * np.pi, 4 * np.pi, 500) # theta旋转角从-4pi到4pi，相当于两圈z = np.linspace(0, 2, 500) # z轴从下到上,从-2到2之间画100个点r = z # 半径设置为z大小x = r * np.sin(theta) # x和y画圆y = r * np.cos(theta) # x和y画圆ax.plot(x, y, z, label='curve')ax.legend()plt.show()

3D散点图

from mpl_toolkits.mplot3d import Axes3Dimport numpy as npimport matplotlib.pyplot as pltfig = plt.figure()ax = fig.add_subplot(1,1,1,projection='3d')xx = np.linspace(0,5,10)yy = np.linspace(0,5,10)zz1 = xxzz2 = 2 *xxzz3 = 3*xxax.scatter(xx,yy,zz1,c='red',marker='o')#o型符号ax.scatter(xx,yy,zz2,c='green',marker='^')#三角型符号ax.scatter(xx,yy,zz3,c='black',marker='*')#星型符号ax.legend()plt.show()

绘制3D表面

from mpl_toolkits.mplot3d import Axes3Dfrom matplotlib import cmfrom matplotlib.ticker import LinearLocator, FormatStrFormatterimport matplotlib.pyplot as pltimport numpy as npfig = plt.figure()ax = fig.gca(projection='3d')X = np.arange(-5, 5, 0.25)Y = np.arange(-5, 5, 0.25)X, Y = np.meshgrid(X, Y)Z = X**2+Y**2ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)plt.show()