# Importing the NumPy library to handle large arrays and matrices
import numpy as np 

# Creating a 3x2 matrix with values [1, 2], [3, 4], and [5, 6] in each row
matrix_a = np.array([[1, 2], [3, 4], [5, 6]]) 

# Printing the resulting matrix
print(matrix_a)

# This code creates a 3x2 matrix using nested lists
matrix = [[1, 2], [3, 4], [5, 6]]

# This code iterates through each row in the matrix
for row in matrix:
    # This code iterates through each element in the row
    for element in row:
        # This code prints each element in the row
        print(element, end=" ")
    # This code creates a new line after each row is printed
    print()

# creating a second 2x1 matrix with values [7] and [8] in each row
matrix_b = np.array([[7], [8]]) # creating a NumPy array with 2 rows and 1 column, containing the values 7 and 8
print(np.matmul(matrix_a, matrix_b)) # multiplying the two matrices using NumPy's matmul function and printing the result

# Note: The original script did not define the matrix_a variable, so it would result in an error. Also, the purpose of this segment is to create a second matrix to multiply with the first one.

# creating a 2x2 matrix with values [1, 2] and [3, 4] in each row
matrix_a = np.array([[1, 2], [3, 4]]) # creating a NumPy array with 2 rows and 2 columns, containing the values 1, 2, 3, and 4

# Note: This segment creates the first matrix that we want to multiply with the second one.

# multiplying the two matrices using NumPy's matmul function and printing the result
print(np.matmul(matrix_a, matrix_b))

# Note: This segment performs the matrix multiplication and prints the result.

# This code will output a 3x1 numpy array with values 58, 139, and 220.

# Import the numpy library
import numpy as np

# Create a 3x1 numpy array with values 58, 139, and 220
arr = np.array([[58], [139], [220]])

# Print the array
print(arr)

# Output:
# [[ 58]
#  [139]
#  [220]]

# Importing the NumPy library
import numpy as np

# Defining two matrices, matrix_a and matrix_b
matrix_a = np.array([[1, 2], [3, 4]]) # creating a 2x2 matrix with values 1, 2, 3, 4
matrix_b = np.array([[5, 6], [7, 8]]) # creating another 2x2 matrix with values 5, 6, 7, 8

# Adding the two matrices together and printing the result
print(np.add(matrix_a, matrix_b)) # using NumPy's add function to perform addition

# Subtracting the second matrix from the first and printing the result
print(np.subtract(matrix_a, matrix_b)) # using NumPy's subtract function to perform subtraction

# Output:
# [[ 6  8]
#  [10 12]]
# [[-4 -4]
#  [-4 -4]]

# The above code imports the NumPy library and defines two matrices, matrix_a and matrix_b, with values 1, 2, 3, 4 and 5, 6, 7, 8 respectively.
# The first print statement uses the NumPy add function to add the two matrices together and prints the result.
# The second print statement uses the NumPy subtract function to subtract the second matrix from the first and prints the result.

# This code will output a 3x2 matrix with values ranging from 3 to 10, and a second 3x2 matrix with negative values ranging from -6 to -19.

# The first matrix is created using the numpy array function, which takes in a list of lists as its argument. 
# The inner lists represent the rows of the matrix, while the outer list represents the entire matrix. 
# The values in the matrix are separated by commas and enclosed in square brackets.

# The second matrix is created using the numpy negative function, which takes in a matrix as its argument and returns a matrix with all its values negated.

import numpy as np # Importing the numpy library to use its functions.

matrix1 = np.array([[8, 10], [3, 6], [5, 4]]) # Creating the first matrix with values ranging from 3 to 10.

matrix2 = np.negative(matrix1) # Creating the second matrix by negating the values in the first matrix.

print(matrix1) # Printing the first matrix.

print(matrix2) # Printing the second matrix.