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

1. How can you flatten a NumPy array?

arr.flatten()

np.flat(arr)

arr.flat()

np.reshape(arr, -1)

2. How can you perform element-wise exponentiation in NumPy?

Using the `np.power` function

NumPy does not support element-wise exponentiation

By applying the `np.exponential` method

Using the `**` operator

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

Calculates the median of an array

Finds the mean of an array

Counts the number of elements in an array

Calculates the standard deviation of an array

4. How can you create a NumPy array from a Python list?

Using `np.array(my_list)`

Converting the list to a tuple

NumPy arrays cannot be created from Python lists

By using the `array` method in Python

5. What is the role of the `np.mean` function in NumPy?

Calculates the mean (average) of an array

Finds the median of an array

Counts the number of elements in an array

Calculates the standard deviation of an array

6. Examine the purpose of the `np.savez` function in NumPy.

Saves multiple NumPy arrays into a single compressed file

Saves a NumPy array to a text file

Counts the number of elements in a NumPy array

Creates a zip file containing multiple NumPy arrays

7. 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)

8. How can you perform element-wise subtraction of two NumPy arrays?

np.subtract(arr1, arr2)

arr1 - arr2

np.sub(arr1, arr2)

arr1.subtract(arr2)

9. What does the NumPy function 'np.percentile()' calculate?

Mean value of the array

Median value of the array

Standard deviation of the array

Percentiles of the array data

10. How can you perform element-wise comparison between two NumPy arrays `arr1` and `arr2`?

Using the comparison operators (`==`, `!=`, `<`, `>`, etc.)

By applying the `np.compare` function

NumPy does not support element-wise comparison

Using the `arr1.compare(arr2)` method

11. What is NumPy used for in Python?

Numerical computing and array manipulation

Web development

Natural language processing

Data visualization

12. 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

13. 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

14. What does the function `np.arange(5)` do?

Creates an array with elements 0 to 4

Generates an array with elements 1 to 5

Creates an array with elements 0 to 5

Generates an array with elements 0 to 4

15. 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)

16. 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

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. 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

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

Combines two or more arrays along a specified axis

Counts the number of elements in an array

Calculates the mean of an array

Finds the maximum value in an array

20. Examine the purpose of the `np.interp` function in NumPy.

Performs linear interpolation on a one-dimensional array

Calculates the intersection points of two arrays

Creates an array with regularly spaced values

Finds the inverse of a matrix

21. What is the purpose of the `np.save` and `np.load` functions in NumPy?

To save and load NumPy arrays to and from disk

To check the memory usage of NumPy arrays

Saving and loading arrays is not possible in NumPy

To compress NumPy arrays for storage

22. What does the NumPy function 'np.unique()' return?

The unique elements of an array

The maximum value of an array

The mean of unique elements

The indices of repeated elements

23. Explain the concept of NumPy broadcasting and provide an example.

NumPy broadcasting allows for element-wise operations on arrays of different shapes by implicitly expanding the smaller array. Example: Adding a scalar to a one-dimensional array.

Broadcasting is not a feature in NumPy

NumPy broadcasting is limited to addition and subtraction operations

Broadcasting is applicable only to two-dimensional arrays

24. What is the purpose of the NumPy function 'np.linalg.inv()'?

Computing the dot product of two arrays

Calculating the inverse of a matrix

Finding the eigenvalues of a matrix

Performing element-wise matrix multiplication

25. 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

26. Examine the role of universal functions (ufuncs) in NumPy and provide an example.

Universal functions are functions that operate element-wise on arrays, promoting vectorized operations. Example: `np.square` for element-wise squaring

Universal functions are only applicable to one-dimensional arrays

NumPy does not have universal functions

Universal functions can only be applied to integer arrays

27. 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

28. What does the NumPy function 'np.interp()' do?

Interpolates values in an array

Performs element-wise interpolation

Returns the intersection of two arrays

Calculates the inverse of an array

29. In NumPy, what does the 'np.where()' function do?

Filters elements based on a condition

Performs element-wise addition

Returns the indices of array elements that meet a condition

Concatenates two arrays along a specified axis

30. How can you calculate the standard deviation of a NumPy array?

arr.std()

np.std(arr)

np.compute_std(arr)

arr.standard_deviation()

NumPy MCQ Test 3