NumPy的线性代数模块提供了在任何numpy阵列上应用线性代数的各种方法。

人们可以找到：

• 数组的秩，行列式，跟踪等。
• 矩阵的本征值
• 矩阵和矢量积（点，内，外等产品），矩阵求幂
• 求解线性或张量方程等等！

# Importing numpy as np
import numpy as np

A = np.array([[6, 1, 1],
[4, -2, 5],
[2, 8, 7]])

# Rank of a matrix
print("Rank of A:", np.linalg.matrix_rank(A))

# Trace of matrix A
print("\nTrace of A:", np.trace(A))

# Determinant of a matrix
print("\nDeterminant of A:", np.linalg.det(A))

# Inverse of matrix A
print("\nInverse of A:\n", np.linalg.inv(A))

print("\nMatrix A raised to power 3:\n",
np.linalg.matrix_power(A, 3))

输出:

Rank of A: 3

Trace of A: 11

Determinant of A: -306.0

Inverse of A:
 [[ 0.17647059 -0.00326797 -0.02287582]
 [ 0.05882353 -0.13071895  0.08496732]
 [-0.11764706  0.1503268   0.05228758]]

Matrix A raised to power 3:
 [[336 162 228]
 [406 162 469]
 [698 702 905]]