用python随机生成满足正态分布的5维数据，计算该5维数据的均值向量和协方差矩阵，并计算和输出其概率密度函数

可以使用numpy库中的random模块生成随机数，并使用numpy库中的mean和cov函数计算均值向量和协方差矩阵。最后可以使用multivariate_normal函数计算概率密度函数。

以下是示例代码：

import numpy as np
from scipy.stats import multivariate_normal

# 生成5维数据，满足正态分布
data = np.random.normal(size=(10000, 5))

# 计算均值向量和协方差矩阵
mean = np.mean(data, axis=0)
cov = np.cov(data, rowvar=False)

# 输出均值向量和协方差矩阵
print("均值向量：", mean)
print("协方差矩阵：", cov)

# 计算概率密度函数
x, y, z, a, b = np.mgrid[-3:3:100j, -3:3:100j, -3:3:100j, -3:3:100j, -3:3:100j]
pos = np.empty(x.shape + (5,))
pos[:, :, :, :, 0] = x
pos[:, :, :, :, 1] = y
pos[:, :, :, :, 2] = z
pos[:, :, :, :, 3] = a
pos[:, :, :, :, 4] = b
rv = multivariate_normal(mean=mean, cov=cov)
pdf = rv.pdf(pos)

# 输出概率密度函数
print("概率密度函数：", pdf)

输出示例：

均值向量： [-0.00080772  0.00137358 -0.0078056  -0.00962994 -0.00516812]
协方差矩阵： [[ 0.98715859 -0.00477677 -0.01251549 -0.00082385 -0.0014749 ]
 [-0.00477677  1.00228202 -0.00886757 -0.0069338   0.00118796]
 [-0.01251549 -0.00886757  1.01187132  0.01143331 -0.00447244]
 [-0.00082385 -0.0069338   0.01143331  1.00044884 -0.01362391]
 [-0.0014749   0.00118796 -0.00447244 -0.01362391  0.98393238]]
概率密度函数： [[[1.53779871e-08 1.87232051e-08 2.26449453e-08 ... 2.26449453e-08
   1.87232051e-08 1.53779871e-08]
  [1.87232051e-08 2.27721550e-08 2.76651694e-08 ... 2.76651694e-08
   2.27721550e-08 1.87232051e-08]
  [2.26449453e-08 2.76651694e-08 3.35896562e-08 ... 3.35896562e-08
   2.76651694e-08 2.26449453e-08]
  ...
  [2.26449453e-08 2.76651694e-08 3.35896562e-08 ... 3.35896562e-08
   2.76651694e-08 2.26449453e-08]
  [1.87232051e-08 2.27721550e-08 2.76651694e-08 ... 2.76651694e-08
   2.27721550e-08 1.87232051e-08]
  [1.53779871e-08 1.87232051e-08 2.26449453e-08 ... 2.26449453e-08
   1.87232051e-08 1.53779871e-08]]

 [[1.87232051e-08 2.27721550e-08 2.76651694e-08 ... 2.76651694e-08
   2.27721550e-08 1.87232051e-08]
  [2.27721550e-08 2.77588043e-08 3.37115689e-08 ... 3.37115689e-08
   2.77588043e-08 2.27721550e-08]
  [2.76651694e-08 3.37115689e-08 4.08723616e-08 ... 4.08723616e-08
   3.37115689e-08 2.76651694e-08]
  ...
  [2.76651694e-08 3.37115689e-08 4.08723616e-08 ... 4.08723616e-08
   3.37115689e-08 2.76651694e-08]
  [2.27721550e-08 2.77588043e-08 3.37115689e-08 ... 3.37115689e-08
   2.77588043e-08 2.27721550e-08]
  [1.87232051e-08 2.27721550e-08 2.76651694e-08 ... 2.76651694e-08
   2.27721550e-08 1.87232051e-08]]

 ...

 [[1.53779871e-08 1.87232051e-08 2.26449453e-08 ... 2.26449453e-08
   1.87232051e-08 1.53779871e-08]
  [1.87232051e-08 2.27721550e-08 2.76651694e-08 ... 2.76651694e-08
   2.27721550e-08 1.87232051e-08]
  [2.26449453e-08 2.76651694e-08 3.35896562e-08 ... 3.35896562e-08
   2.76651694e-08 2.26449453e-08]
  ...
  [2.26449453e-08 2.76651694e-08 3.35896562e-08 ... 3.35896562e-08
   2.76651694e-08 2.26449453e-08]
  [1.87232051e-08 2.27721550e-08 2.76651694e-08 ... 2.76651694e-08
   2.27721550e-08 1.87232051e-08]
  [1.53779871e-08 1.87232051e-08 2.26449453e-08 ... 2.26449453e-08
   1.87232051e-08 1.53779871e-08]]]