How can I use NumPy for linear algebra in Python?

How can I use NumPy for linear algebra in Python?

You can use NumPy to perform various linear algebra operations like matrix multiplication, finding determinants, and eigenvalues. Would you like to see some examples?

Yes, please show me how to multiply two matrices.

Sure! You can use the numpy.dot() function or the @ operator. For example:

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = np.dot(A, B)
# or
result = A @ B
print(result)

What about finding the inverse of a matrix?

You can use the numpy.linalg.inv() function to find the inverse of a matrix. Here's how:

import numpy as np
A = np.array([[1, 2], [3, 4]])
inverse_A = np.linalg.inv(A)
print(inverse_A)

Can NumPy help with solving linear equations?

Absolutely! You can use numpy.linalg.solve() to solve linear equations. For example:

import numpy as np
A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
solution = np.linalg.solve(A, b)
print(solution)