ax.plot(x, y, z, label='parametric curve') ax.legend() plt.show() ➤02绘制Scatter 利用和上面的相同的绘制命令,将原来的plot3D修改成为 scatter即可。 frommpl_toolkits.mplot3dimportaxes3d ax = plt.axes(projection='3d') angle = linspace(0,2*pi*5,40) x...
plot(x, y, z, label='parametric curve') ax.legend() plt.show() ➤02 绘制Scatter 利用和上面的相同的绘制命令,将原来的plot3D修改成为 scatter即可。 from mpl_toolkits.mplot3d import axes3d ax = plt.axes(projection='3d') angle = linspace(0, 2*pi*5, 40) x = cos(angle) y = sin(...
plot_trisurf(data.x, data.y, data.z, cmap=plt.cm.Spectral, linewidth=0.2) ax.view_init(30, 80) plt.title('3D图') plt.show() 13 总结 以上通过seaborn的scatterplot和matplotlib的plot可以快速绘制散点图,并通过修改参数或者辅以其他绘图知识自定义各种各样的散点图来适应相关使用场景。 共勉～...
最基本的三维图是由(x, y, z)三维坐标点构成的线图与散点图,可以用ax.plot3D和ax.scatter3D函数来创建,默认情况下,散点会自动改变透明度,以在平面上呈现出立体感 三维的线图和散点图 #绘制三角螺旋线 from mpl_toolkitsimport mplot3d %matplotlib inline import matplotlib.pyplot as plt import numpy as n...
利用和上面的相同的绘制命令,将原来的plot3D修改成为 scatter即可。 代码语言:javascript 复制 from mpl_toolkits.mplot3dimportaxes3d ax=plt.axes(projection='3d')angle=linspace(0,2*pi*5,40)x=cos(angle)y=sin(angle)z=linspace(0,5,40)ax.scatter(x,y,z,color='b')ax.set_xlabel('X Axes')ax...
ax = fig.gca(projection='3d') # Plot a sin curve using the x and y axes. x = np.linspace(0, 1, 100) y = np.sin(x * 2 * np.pi) / 2 + 0.5 ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)') # Plot scatterplot data (20 2D points per colour) on the x...
将原来的plot3D修改成为 scatter即可。 frommpl_toolkits.mplot3dimportaxes3d ax=plt.axes(projection='3d')angle=linspace(0,2*pi*5,40)x=cos(angle)y=sin(angle)z=linspace(0,5,40)ax.scatter(x,y,z,color='b')ax.set_xlabel('X Axes')ax.set_ylabel('Y Axes')ax.set_zlabel('Z Axes')plt...
*args等为扩展变量,如maker = 'o',则scatter结果为’o‘的形状 示例: from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np def randrange(n, vmin, vmax): ''' Helper function to make an array of random numbers having shape (n, ) ...
# Creating the 3D plot sctt = ax.scatter3D(x, y, z,alpha = 0.8,c = (x + y + z),cmap = my_cmap,marker ='^') plt.title("3D scatter plot in Python") ax.set_xlabel('X-axis', fontweight ='bold') ax.set_ylabel('Y-axis', fontweight ='bold') ...
ax = fig.gca(projection='3d') # Plot a sin curve using the x and y axes. x = np.linspace(0, 1, 100) y = np.sin(x * 2 * np.pi) / 2 + 0.5 ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)') # Plot scatterplot data (20 2D points per colour) on the x...