NumPy provides a powerful set of linear algebra functions that can be used for a variety of data analysis tasks.

#### Here are some basic linear algebra operations that can be performed with NumPy:

- Dot product: The dot product of two vectors or matrices can be calculated using the
`dot()`

function.

import numpy as np # Create two vectors a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) # Calculate the dot product of the two vectors c = np.dot(a, b) print(c) # Create two matrices d = np.array([[1, 2], [3, 4]]) e = np.array([[5, 6], [7, 8]]) # Calculate the dot product of the two matrices f = np.dot(d, e) print(f)

2. Matrix inverse: The inverse of a matrix can be calculated using the `inv()`

function.

import numpy as np # Create a matrix a = np.array([[1, 2], [3, 4]]) # Calculate the inverse of the matrix b = np.linalg.inv(a) print(b)

3. Eigenvalues and eigenvectors: The eigenvalues and eigenvectors of a matrix can be calculated using the `eig()`

function.

import numpy as np # Create a matrix a = np.array([[1, 2], [3, 4]]) # Calculate the eigenvalues and eigenvectors of the matrix eigenvalues, eigenvectors = np.linalg.eig(a) print("Eigenvalues:", eigenvalues) print("Eigenvectors:", eigenvectors)

4. Singular value decomposition: The singular value decomposition (SVD) of a matrix can be calculated using the `svd()`

function.

import numpy as np # Create a matrix a = np.array([[1, 2], [3, 4]]) # Calculate the SVD of the matrix u, s, vh = np.linalg.svd(a) print("U:", u) print("S:", s) print("Vh:", vh)

These are just a few basic linear algebra operations that can be performed with NumPy. NumPy’s linear algebra functions are powerful and flexible, and can be used in a variety of data analysis tasks, such as machine learning, image processing, and scientific computing.