本文实例为大家分享了python matlibplot绘制3D图形的具体代码，供大家参考，具体内容如下

1、散点图使用scatter

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

from matplotlib import pyplot as plt

# 生成3D示例数据

mu_vec1 = np.array([0,0,0]) # 均值向量

cov_mat1 = np.array([[1,0,0],[0,1,0],[0,0,1]]) # 协方差矩阵

class1_sample = np.random.multivariate_normal(mu_vec1, cov_mat1, 20)

class2_sample = np.random.multivariate_normal(mu_vec1 + 1, cov_mat1, 20)

class3_sample = np.random.multivariate_normal(mu_vec1 + 2, cov_mat1, 20)

# class1_sample.shape -> (20, 3), 20 rows, 3 columns

fig = plt.figure(figsize=(8,8))

ax = fig.add_subplot(111, projection='3d')

ax.scatter(class1_sample[:,0], class1_sample[:,1], class1_sample[:,2],

marker='x', color='blue', s=40, label='class 1')

ax.scatter(class2_sample[:,0], class2_sample[:,1], class2_sample[:,2],

marker='o', color='green', s=40, label='class 2')

ax.scatter(class3_sample[:,0], class3_sample[:,1], class3_sample[:,2],

marker='^', color='red', s=40, label='class 3')

ax.set_xlabel('variable X')

ax.set_ylabel('variable Y')

ax.set_zlabel('variable Z')

plt.title('3D Scatter Plot')

plt.show()

2、直线使用plot3D

from mpl_toolkits.mplot3d import Axes3D

import matplotlib.pyplot as plt

import numpy as np

from itertools import product, combinations

fig = plt.figure(figsize=(7,7))

ax = fig.gca(projection='3d')

ax.set_aspect("equal")

# 画点

# 立方体里的点

X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])

X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],

[0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],

[0.7,-0.6,-0.8]])

for row in X_inside:

ax.scatter(row[0], row[1], row[2], color="r", s=50, marker='^')

for row in X_outside:

ax.scatter(row[0], row[1], row[2], color="k", s=50)

# 画立方体

h = [-0.5, 0.5]

for s, e in combinations(np.array(list(product(h,h,h))), 2):

if np.sum(np.abs(s-e)) == h[1]-h[0]:

ax.plot3D(*zip(s,e), color="g")

ax.set_xlim(-1.5, 1.5)

ax.set_ylim(-1.5, 1.5)

ax.set_zlim(-1.5, 1.5)

plt.show()

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本文标题: python matlibplot绘制3D图形

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