Excellent Numpy Test - 78+ MCQs: NumPy test: Evaluate Your Proficiency with NumPy Questions and Answers

1. How can you create a diagonal matrix from a NumPy array?

np.diagonal_matrix(arr)

np.diag(arr)

arr.create_diagonal()

np.matrix_diag(arr)

2. How can you perform matrix multiplication in NumPy?

np.matmul(arr1, arr2)

arr1 @ arr2

np.mult_matrix(arr1, arr2)

arr1.matrix_mult(arr2)

3. What is the purpose of the `np.ma.masked_where` function in NumPy?

Creates a masked array based on a specified condition

Finds the elements with the maximum value in an array

Calculates the mean of an array

Masks all elements in an array

4. How do you calculate the element-wise square root of a NumPy array?

Using `np.sqrt(my_array)`

By applying the `my_array.square_root` method

Both A and B

NumPy does not support calculating the square root of array elements

5. How can you create a diagonal matrix from a one-dimensional NumPy array?

Using the `np.diag` function

Creating diagonal matrices is not possible in NumPy

By applying the `np.create_diagonal` method

Using the `np.diagonal` function

6. In NumPy, what does the 'np.concatenate()' function do?

Joins two arrays along an existing axis

Adds a new axis to an array

Rearranges array elements randomly

Performs element-wise division

7. What does the `np.eye` function do in NumPy?

Creates an identity matrix

Generates an array with random values

Creates an array with ones on the diagonal

Generates an array filled with zeros

8. How do you access the element at the 3rd index of a NumPy array named `my_array`?

Using `my_array[3]`

By using `my_array(3)`

It is not possible to access elements in NumPy arrays

By using `my_array.element(3)`

9. Explain the difference between a Python list and a NumPy array.

NumPy arrays are homogeneous and support vectorized operations, while Python lists can contain mixed data types and lack vectorization capabilities

Python lists are more memory-efficient than NumPy arrays

NumPy arrays are limited to one dimension, while Python lists support multidimensional structures

There is no significant difference between Python lists and NumPy arrays

10. What is the difference between `np.concatenate` and `np.stack` in NumPy?

`np.concatenate` combines arrays along an existing axis, while `np.stack` creates a new axis to stack the arrays

`np.concatenate` and `np.stack` serve the same purpose

`np.concatenate` is used for one-dimensional arrays, and `np.stack` for multi-dimensional arrays

`np.stack` combines arrays along an existing axis, while `np.concatenate` creates a new axis to stack the arrays

NumPy MCQ Test 5