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

1. 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

2. 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

3. How do you create a NumPy array with regularly spaced values between two numbers?

Using `np.linspace(start, stop, num)`

By using `np.range(start, stop, step)`

Both A and B

NumPy does not support creating arrays with regularly spaced values

4. 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

5. How can you perform matrix multiplication in NumPy?

np.matmul(arr1, arr2)

arr1 @ arr2

np.mult_matrix(arr1, arr2)

arr1.matrix_mult(arr2)

6. How do you perform matrix multiplication in NumPy?

Using the `np.dot` function or the `@` operator

Matrix multiplication is not supported in NumPy

Using the `np.multiply` function

By applying the `np.cross` function

7. 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

8. 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)

9. What is the purpose of the `np.max` function in NumPy?

Finds the maximum value in an array

Calculates the mean of an array

Counts the number of non-zero elements in an array

Finds the minimum value in an array

10. What is broadcasting in NumPy, and how does it work?

Broadcasting is a mechanism that allows NumPy to perform operations on arrays of different shapes, by implicitly expanding the smaller array

Broadcasting is only applicable to one-dimensional arrays

NumPy does not support broadcasting

Broadcasting is limited to addition and subtraction operations

11. 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

12. Explain the concept of a NumPy structured array.

A structured array allows the definition of multiple fields with different data types, similar to a table with named columns.

Structured arrays are not supported in NumPy

A structured array is an array with a fixed structure and cannot have different data types

Structured arrays are only applicable to one-dimensional arrays

13. 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

14. 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

15. 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

16. What does the `np.ravel` function do in NumPy?

Flattens a multi-dimensional array into a one-dimensional array

Generates a random array

Counts the number of non-zero elements in an array

Reshapes an array into a specified shape

17. 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]

18. How do you calculate the inverse of a matrix in NumPy?

Using the `np.linalg.inv` function

Inverting matrices is not possible in NumPy

By applying the `np.matrix_inverse` method

Using the `np.inverse` function

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

Generates an array with regularly spaced values

Creates an array filled with ones

Calculates the mean of an array

Finds the maximum value in an array

20. What does the `np.clip` function do in NumPy?

Clips (limits) the values in an array to a specified range

Counts the occurrences of each unique element in an array

Concatenates two arrays along a specified axis

Calculates the cumulative sum of an array

21. What is the purpose of the NumPy function 'np.fft.fft()'?

Calculating the fast Fourier transform

Finding the first occurrence of a value

Flattening a multidimensional array

Performing a fuzzy string match

22. In NumPy, what does the 'np.random.seed()' function do?

Sets the random seed for reproducibility

Generates a random seed array

Initializes the random number generator

Resets the random seed

23. What is the purpose of the `np.zeros` function in NumPy?

To create an array filled with zeros

To generate an array with random numbers

To create an empty array

To create an array filled with ones

24. 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

25. How can you transpose a NumPy array?

Using `my_array.T`

By applying the `np.transpose` function

Both A and B

NumPy arrays cannot be transposed

26. How can you calculate the mean of a NumPy array along a specific axis?

np.mean(arr, axis=1)

np.average(arr, axis=0)

arr.mean(axis=2)

np.compute_mean(arr, axis=1)

27. 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

28. 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

29. 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)`

30. What is the purpose of the `np.ma.masked_array` function in NumPy?

Creates a masked array, allowing elements to be masked based on a specified condition

Finds the elements with the minimum value in an array

Calculates the mean of an array

Masks all elements in an array

31. 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

32. How can you perform element-wise multiplication of two NumPy arrays?

np.multiply(arr1, arr2)

arr1 * arr2

np.mult(arr1, arr2)

np.dot(arr1, arr2)

33. 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

34. How do you check the data type of a NumPy array named `my_array`?

Using `my_array.dtype`

By applying the `np.type` function

Both A and B

NumPy arrays do not have a data type

35. How do you perform element-wise multiplication of two NumPy arrays `arr1` and `arr2`?

By using the expression `arr1 * arr2`

Using the `np.multiply` function

Both A and B

NumPy does not support element-wise multiplication

36. 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

37. 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

38. Examine the purpose of the `np.histogram` function in NumPy.

Generates a histogram from an array of data

Counts the number of unique elements in an array

Calculates the mean of an array

Creates a scatter plot for two arrays

39. 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

40. What is the purpose of the NumPy function 'np.ma.masked_where()'?

Creates a masked array where values meet a condition

Calculates the mean of a masked array

Fills masked elements with a specified value

Returns the indices of masked elements

41. What does the `np.std` function calculate in NumPy?

The standard deviation of an array

The sum of all elements in an array

The variance of an array

The mean of an array

42. How do you apply a function element-wise to a NumPy array?

Using the `np.vectorize` function

Applying functions element-wise is not supported in NumPy

By using a for loop

Using the `np.apply_along_axis` function

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

arr.std()

np.std(arr)

np.compute_std(arr)

arr.standard_deviation()

44. 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

45. How do you calculate the cumulative sum of elements along a specified axis in a NumPy array?

Using the `np.cumsum` function

Calculating cumulative sum along an axis is not supported in NumPy

By applying the `np.cumulative_sum` method

Using the `np.sum_cumulative` function

46. 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

47. What does the NumPy function 'np.unravel_index()' do?

Reshapes a flattened array to its original shape

Converts an array of indices to Cartesian coordinates

Returns the indices of the minimum values along an axis

Splits an array into multiple sub-arrays

48. 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

49. What is the purpose of the NumPy function 'np.argsort()'?

Sorts array elements in ascending order

Returns the indices that would sort an array

Performs element-wise sorting

Finds the index of the minimum value in an array

50. 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

NumPy MCQ Test 1