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

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

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 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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)`

21. 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

22. 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

23. 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

24. 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)

25. 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

26. 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

27. What is NumPy used for in Python?

Numerical computing and array manipulation

Web development

Natural language processing

Data visualization

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. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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)

39. 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

40. 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

NumPy MCQ Test 2