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

1. How can you find the sum of all elements in a NumPy array?

Using `np.sum(my_array)`

By applying the `my_array.total` attribute

Both A and B

NumPy does not support calculating the sum of array elements

2. What is the purpose of the NumPy function 'np.meshgrid()'?

Generates a mesh plot of a 3D surface

Creates a grid from element-wise operations

Expands the dimensions of an array

Generates coordinate matrices from coordinate vectors

3. What is the primary purpose of NumPy in Python?

Data Visualization

Machine Learning

Numerical Computing

Web Development

4. What is the purpose of the `np.fft.fft` function in NumPy?

Computes the one-dimensional discrete Fourier Transform

Finds the factorial of each element in an array

Calculates the eigenvalues of a matrix

Finds the maximum value in an array

5. Explain the concept of masking in NumPy and provide an example.

Masking involves using boolean arrays to select elements from another array based on certain conditions. Example: `arr[arr > 5]` selects elements greater than 5 from the array `arr`.

Masking is not a feature in NumPy

Masking is only applicable to one-dimensional arrays

NumPy arrays do not support conditional selection

6. What is the purpose of the `np.unique` function in NumPy?

Finds the unique elements in an array

Calculates the sum of all elements in an array

Counts the number of elements in an array

Generates an array with ones on the diagonal

7. What is NumPy used for in Python?

Numerical computing and array manipulation

Web development

Natural language processing

Data visualization

8. In NumPy, what does the 'np.pad()' function do?

Computes the padding required for a convolutional operation

Returns the padded array

Fills array elements with a specified value

Calculates the partial derivative of an array

9. How can you reshape a NumPy array?

Using the `reshape` method or the `np.reshape` function

Reshaping is not possible in NumPy

By converting the array to a Python list and then reshaping

Using the `resize` method

10. What is the purpose of the `np.eye` function 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

11. 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

12. 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

13. How can you find the index of the maximum value in a NumPy array?

Using `np.argmax`

By using the `max_index` attribute

Both A and B

It is not possible to find the index of the maximum value in NumPy

14. How can you perform element-wise addition of two NumPy arrays `arr1` and `arr2`?

By using the expression `arr1 + arr2`

Using the `np.add` function

Both A and B

NumPy does not support element-wise addition

15. 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

16. How can you concatenate two NumPy arrays vertically?

np.append(arr1, arr2, axis=0)

np.concat(arr1, arr2, axis=0)

np.vstack((arr1, arr2))

np.merge_vertical(arr1, arr2)

17. What is the purpose of the NumPy function 'np.histogram()'?

Calculating the mean of an array

Creating a histogram from an array

Sorting array elements in ascending order

Counting occurrences of unique values in an array

18. How do you concatenate two NumPy arrays vertically?

Using `np.vstack((arr1, arr2))`

By using `np.concatenate(arr1, arr2, axis=0)`

Both A and B

NumPy does not support array concatenation

19. What is the purpose of the `np.random.seed` function in NumPy?

Sets the random seed for reproducibility in random number generation

Generates a random seed based on the system time

Seeding is not applicable in NumPy

Defines the random number generator for parallel processing

20. What is the result of the expression `np.arange(2, 10, 2)`?

An array with values [2, 4, 6, 8]

An array with values [2, 4, 8]

An array with values [2, 6, 10]

An array with values [2, 8]

NumPy MCQ Test 4