Python NumPy Quiz

What is the purpose of a Module in Python?

• To create loops

• To define functions and classes

• To manipulate arrays

• To sort arrays

What does the np.array() function do?

• Creates a new array filled with zero values.

• Creates a new array from an existing data structure like a list or tuple.

• Generates a range of numbers spaced evenly between specified start, stop, and step values.

• Randomly initializes an array of given shape and data type.

What does the np.arange(3, 15, 3) function call return?

• An array with elements [3, 6, 9, 12, 15]

• An array with elements [3, 6, 9, 12]

• An array with elements [3, 5, 7, 9, 11, 13]

• An array with elements [2, 5, 8, 11, 14]

Which of the following will create a 2D array using np.array()?

• np.array(5, 5)

• np.array([1, 2], [3,4])

• np.array([[1, 2], [3, 4]])

• np.array() with no arguments

What does the following operation do? numpy.array([1, 2, 3]) + 4

• It raises an error because of a mismatch in dimensions

• It creates a new array with each element incremented by 4, resulting in [5, 6, 7]

• IIt appends 4 to the array, resulting in [1, 2, 3, 4]

• None of the above

How do you change a flat array with 12 elements into a 3 rows and 4 columns matrix?

• array.reshape(3, 4)

• array.reshape([3, 4])

• array.reshape(3, 4, 1)

• array.reshape(4, 3)

You want to reshape a 4x4 matrix into a new array with 8 columns. What should pass to .reshape() to let NumPy automatically calculate the appropriate number of rows?

• .reshape(:, 8)

• .reshape(-1, 8)

• .reshape(0, 8)

• .reshape(, 8)

What does the slice arr[:, 1] represent?

• All rows of the first column of the array arr

• All columns of the first row of the array arr

• All rows of the second column of the array arr

• The second element of every row in the array arr

Given x = np.arange(12). Which of the following will NOT split the array?

• np.split(x, 3)

• np.split(x, [4, 8])

• np.split(x, 10)

• np.split(x, 6)

Which parameter should you use with numpy.sort() to sort an array in descending order?

• numpy.sort(array, order='descending')

• numpy.sort(array, reverse=True)

• numpy.sort(array)[::-1]

• numpy.sort(array, axis=-1)