pip install matplotlib numpy

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

📌 重要提示

在Jupyter Notebook中使用3D绘图时，请确保添加%matplotlib notebook魔术命令以获得交互式图形。

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 设置随机种子以保证结果可复现
np.random.seed(42)

# 创建数据
n = 200
x = np.random.rand(n) * 10
y = np.random.rand(n) * 8
z = np.random.rand(n) * 6
colors = np.random.rand(n)
sizes = 100 * np.random.rand(n)

# 创建3D图形
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# 绘制散点图
scatter = ax.scatter(x, y, z, c=colors, s=sizes, cmap='viridis', alpha=0.8, 
                     edgecolor='k', linewidth=0.5)

# 添加颜色条
cbar = fig.colorbar(scatter, ax=ax, pad=0.1)
cbar.set_label('颜色值', rotation=270, labelpad=15)

# 设置坐标轴标签
ax.set_xlabel('X轴', labelpad=15, fontsize=12)
ax.set_ylabel('Y轴', labelpad=15, fontsize=12)
ax.set_zlabel('Z轴', labelpad=15, fontsize=12)

# 设置标题
ax.set_title('3D散点图示例', fontsize=16, pad=20)

# 设置视角
ax.view_init(elev=25, azim=45)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

# 创建数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2)) * np.cos(0.5*X) * np.exp(-0.1*(X**2 + Y**2))

# 创建3D图形
fig = plt.figure(figsize=(12, 9))
ax = fig.add_subplot(111, projection='3d')

# 绘制曲面图
surf = ax.plot_surface(X, Y, Z, cmap='plasma', 
                      edgecolor='none', alpha=0.9, 
                      rstride=2, cstride=2,
                      antialiased=True)

# 添加颜色条
fig.colorbar(surf, ax=ax, shrink=0.5, aspect=10, label='Z值')

# 设置标题和标签
ax.set_title('3D曲面图: $z = sin(r) * cos(0.5x) * e^{-0.1r}$', fontsize=14, pad=20)
ax.set_xlabel('X轴', labelpad=15)
ax.set_ylabel('Y轴', labelpad=15)
ax.set_zlabel('Z轴', labelpad=15)

# 设置视角
ax.view_init(elev=35, azim=-45)

# 添加网格
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.grid(True, linestyle='--', alpha=0.4)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

# 创建数据
t = np.linspace(0, 2 * np.pi, 100)
x = np.sin(t)
y = np.cos(t)
z = t / (2*np.pi)

# 曲面数据
x_surf = np.linspace(-1.5, 1.5, 50)
y_surf = np.linspace(-1.5, 1.5, 50)
X_surf, Y_surf = np.meshgrid(x_surf, y_surf)
Z_surf = 0.5 * np.sin(2*X_surf) * np.cos(2*Y_surf) + 1

# 创建图形
fig = plt.figure(figsize=(12, 10))
ax = fig.add_subplot(111, projection='3d')

# 绘制螺旋线
ax.plot(x, y, z, 'r-', linewidth=3, label='螺旋线')

# 绘制曲面
ax.plot_surface(X_surf, Y_surf, Z_surf, cmap='coolwarm', 
               alpha=0.4, antialiased=True)

# 绘制散点
ax.scatter([0], [0], [0], s=200, c='gold', edgecolor='k', 
          label='原点', zorder=4)

# 添加图例和标签
ax.legend(loc='upper left', fontsize=12)
ax.set_xlabel('X轴', labelpad=15)
ax.set_ylabel('Y轴', labelpad=15)
ax.set_zlabel('Z轴', labelpad=15)
ax.set_title('组合3D图形: 曲面、曲线和散点', fontsize=16, pad=20)

# 设置视角
ax.view_init(elev=25, azim=-30)

# 设置坐标轴范围
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_zlim(0, 1.5)

plt.tight_layout()
plt.show()

Matplotlib的3D绘图功能为复杂数据提供了强大的可视化手段，通过合理配置可以创建出版级质量的图形。