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

1. 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)`

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. 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

4. 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

5. What is NumPy and what is its primary purpose in Python?

NumPy is a numerical computing library for Python, designed for efficient array manipulation and mathematical operations

NumPy is a Python web framework for building web applications

NumPy is used for natural language processing in Python

NumPy is a Python library exclusively for data visualization

6. 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

7. 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)

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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)

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. How can you calculate the dot product of two NumPy arrays?

np.dot_product(arr1, arr2)

np.dot(arr1, arr2)

arr1 * arr2

np.multiply(arr1, arr2)

21. 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

22. 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

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. 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

25. 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

26. 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

27. 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

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

arr.std()

np.std(arr)

np.compute_std(arr)

arr.standard_deviation()

29. 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

30. How do you find the unique elements in a NumPy array?

Using `np.unique(my_array)`

By applying the `my_array.distinct` method

Both A and B

NumPy does not support finding unique elements in arrays

31. 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

32. 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

33. 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

34. 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

35. How can you flatten a NumPy array?

arr.flatten()

np.flat(arr)

arr.flat()

np.reshape(arr, -1)

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. What is NumPy used for in Python?

Numerical computing and array manipulation

Web development

Natural language processing

Data visualization

45. 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

46. 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

47. 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

48. How can you perform matrix multiplication in NumPy?

np.matmul(arr1, arr2)

arr1 @ arr2

np.mult_matrix(arr1, arr2)

arr1.matrix_mult(arr2)

49. 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

50. 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

NumPy MCQ Test 1