Python Mastery: 99 Tips & Techniques for Efficient Matrix Computing

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A matrix is a two-dimensional array of numbers, symbols, or expressions arranged in rows and columns. The individual elements of a matrix are often referred to as entries or components. Matrices are widely used in various fields, including mathematics, physics, computer science, engineering, and more.

1. Matrix Creation:

# Creates a 3x3 matrix using NumPy.
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

[[1, 2, 3],
 [4, 5, 6],
 [7, 8, 9]]

2. Matrix Transposition:

The transpose of a matrix is obtained by switching its rows and columns.

# Creates a 3x3 matrix using NumPy.
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Transposes the matrix.
transposed_matrix = np.transpose(matrix)

# Original matrix:
[[1, 2, 3],
 [4, 5, 6],
 [7, 8, 9]]

# Transposed matrix:
[[1, 4, 7],
 [2, 5, 8],
 [3, 6, 9]]

3. Matrix Addition:

Adds two matrices element-wise.

# Creates a 3x3 matrix using NumPy.
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Creates another 3x3 matrix.
matrix2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])

# Adds the two matrices element-wise…