Exploring Multidimensional Lists in Python

What is a multidimensional list in Python?

• A multidimensional list is a list of lists in Python, enabling the representation of data in multiple dimensions.

• A multidimensional list is a single list containing unique elements.

• A multidimensional list is a flat list with no nested structures.

• A multidimensional list is a dictionary that maps keys to values.

Given the code `matrix = [[1, 2], [3, 4]]`, what is `matrix[1][0]`?

• 3

• 4

• 2

• 1

How do you access the last element of a 2D list?

• list[-1][0]

• list[0][-1]

• list[-1][-1]

• list[0][0]

What will be the output of `print(len(matrix))` for the above matrix?

• 2

• 4

• 5

• 3

How can you append a new row to a multidimensional list?

• Use matrix.append([new_row_values]) to append a new row.

• matrix.append_row(new_row_values) to add a new row.

• Use matrix.add_row([new_row_values]) to append a new row.

• matrix.insert(new_row_values) to append a new row.

What does the expression `matrix[0] + matrix[1]` return?

• An error due to incompatible types

• A new matrix with the first row as matrix[0] and the second row as matrix[1]

• A single list containing only the first element of matrix[0]

• A new list containing the elements of matrix[0] followed by the elements of matrix[1]

How do you iterate through each element in a 2D list?

• Flatten the list first: flat_list = [element for row in list for element in row]

• Access elements directly by index: list[0][0]

• Use a single loop: for element in list: process(element)

• Use nested loops: for row in list: for element in row: process(element)

What will `matrix[0][1]` return in the given matrix?

• The value at the first row and second column of the matrix.

• The value at the second row and first column of the matrix.

• The value at the second row and second column of the matrix.

• The value at the first row and first column of the matrix.

How can you create a 3x3 matrix filled with zeros?

• Create a list of lists with zeros: [[0, 0, 0], [0, 0, 0], [0, 0, 0]].

• Use np.empty((3, 3)) in Python with NumPy.

• Use np.ones((3, 3)) and then subtract one in Python with NumPy.

• Use np.zeros((3, 3)) in Python with NumPy.

What is the output of `print(matrix[1])` for the matrix defined above?

• [value1, value2]

• [value3, value4, value5]

• [value1, value3, value4]

• [value1, value2, value3]