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

1. 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

2. 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

3. 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

4. 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

5. What does the `np.shape` attribute of a NumPy array represent?

The dimensions (shape) of the array

The size of the array

The data type of the array

The maximum value in the array

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. How can you flatten a NumPy array?

arr.flatten()

np.flat(arr)

arr.flat()

np.reshape(arr, -1)

20. 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

21. 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

22. 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

23. 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

24. 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

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

arr.std()

np.std(arr)

np.compute_std(arr)

arr.standard_deviation()

26. 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

27. 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

28. 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]

29. What does the `np.linspace(0, 1, 5)` function do?

Creates an array with five evenly spaced values between 0 and 1

Generates an array with values [0, 1, 5]

Creates an array with values [0, 0.2, 0.4, 0.6, 0.8, 1]

Generates an array with five random values between 0 and 1

30. 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

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

Data Visualization

Machine Learning

Numerical Computing

Web Development

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

np.subtract(arr1, arr2)

arr1 - arr2

np.sub(arr1, arr2)

arr1.subtract(arr2)

33. 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

34. 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

35. What does the `np.where` function do in NumPy?

Returns the indices of elements that satisfy a given condition

Specifies the location of the NumPy library on disk

Identifies the dimensions of an array

Finds the maximum value in an array

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. What is NumPy used for in Python?

Numerical computing and array manipulation

Web development

Natural language processing

Data visualization

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

NumPy MCQ Test 1