Numpy

Question 1

What is NumPy?

Answer 1

NumPy is a fundamental package for scientific computing in Python, providing support for arrays, matrices, and a collection of mathematical functions.

Question 2

True or False: NumPy arrays can hold elements of different data types.

Answer 2

False

Question 3

What is the main purpose of the NumPy library?

Answer 3

The main purpose of NumPy is to provide efficient operations on large arrays and matrices along with a collection of mathematical functions to operate on these arrays.

Question 4

Fill in the blank: The primary data structure in NumPy is the __________.

Answer 4

ndarray

Question 5

Which function is used to create a NumPy array from a Python list?

Answer 5

np.array()

Question 6

What does the function np.arange() do?

Answer 6

np.arange() creates an array with evenly spaced values within a given interval.

Question 7

True or False: NumPy supports multidimensional arrays.

Answer 7

True

Question 8

What method would you use to find the shape of a NumPy array?

Answer 8

Use the .shape attribute.

Question 9

Multiple Choice: Which of the following is NOT a valid way to create a NumPy array? (a) np.array([1, 2, 3]) (b) np.zeros(3) (c) np.create_array([1, 2, 3])

Answer 9

c

Question 10

What does the function np.zeros() return?

Answer 10

np.zeros() returns a new array of given shape and type, filled with zeros.

Question 11

Fill in the blank: The function np.linspace(start, stop, num) generates __________ evenly spaced samples.

Answer 11

num

Question 12

What is the purpose of the np.reshape() function?

Answer 12

np.reshape() changes the shape of an existing array without changing its data.

Question 13

True or False: NumPy provides built-in functions for performing linear algebra operations.

Answer 13

True

Question 14

What is the difference between a list and a NumPy array?

Answer 14

NumPy arrays are more efficient for numerical operations and support element-wise operations, while lists can hold different data types.

Question 15

Which function is used to compute the mean of a NumPy array?

Answer 15

np.mean()

Question 16

Multiple Choice: Which of the following functions can be used to concatenate two NumPy arrays? (a) np.concat() (b) np.append() (c) np.concatenate()

Answer 16

c

Question 17

What does the function np.transpose() do?

Answer 17

np.transpose() permutes the dimensions of the array, effectively flipping it over its diagonal.

Question 18

Fill in the blank: To sort a NumPy array in place, you can use the method __________.

Answer 18

sort()

Question 19

What is broadcasting in NumPy?

Answer 19

Broadcasting is a mechanism that allows NumPy to perform arithmetic operations on arrays of different shapes.

Question 20

True or False: NumPy can handle missing data in arrays.

Answer 20

True

Question 21

What does the function np.unique() do?

Answer 21

np.unique() returns the sorted unique elements of an array.

Question 22

What is the purpose of the np.random module?

Answer 22

The np.random module is used to generate random numbers and perform random sampling.

Question 23

Multiple Choice: Which of the following is a common use case for NumPy? (a) Web development (b) Data analysis (c) Text processing

Answer 23

b

Question 24

What is a masked array in NumPy?

Answer 24

A masked array is an array that may have missing or invalid entries, with a mask that indicates which entries are valid.

Question 25

Fill in the blank: The function __________ returns the dot product of two arrays.

Answer 25

np.dot()

Question 26

What is NumPy primarily used for?

Answer 26

NumPy is primarily used for numerical computing in Python.

Question 27

True or False: NumPy arrays can only hold elements of the same data type.

Answer 27

True

Question 28

Fill in the blank: The function to create an array in NumPy is ______.

Answer 28

numpy.array

Question 29

What function would you use to generate an array of evenly spaced values?

Answer 29

numpy.linspace

Question 30

What is the output of numpy.zeros((2, 3))?

Answer 30

A 2x3 array filled with zeros.

Question 31

Multiple Choice: Which of the following functions is used to concatenate two arrays? A) numpy.add B) numpy.concatenate C) numpy.join

Answer 31

B) numpy.concatenate

Question 32

What does the method numpy.shape return?

Answer 32

It returns the dimensions of the array.

Question 33

True or False: NumPy supports element-wise operations.

Answer 33

True

Question 34

What is the purpose of numpy.reshape?

Answer 34

It changes the shape of an array without changing its data.

Question 35

Fill in the blank: The default data type of a NumPy array is ______.

Answer 35

float

Question 36

What does the numpy.mean function compute?

Answer 36

It computes the average of the array elements.

Question 37

Multiple Choice: Which of the following is NOT a NumPy array method? A) numpy.sum B) numpy.mean C) numpy.sortby

Answer 37

C) numpy.sortby

Question 38

What is the result of numpy.array([1, 2, 3]) + numpy.array([4, 5, 6])?

Answer 38

An array: [5, 7, 9]

Question 39

True or False: You can use NumPy for linear algebra operations.

Answer 39

True

Question 40

What function would you use to calculate the standard deviation of an array?

Answer 40

numpy.std

Question 41

Fill in the blank: To create an identity matrix, you would use ______.

Answer 41

numpy.eye

Question 42

What does the function numpy.unique do?

Answer 42

It finds the unique elements of an array.

Question 43

Multiple Choice: Which function is used to stack arrays in sequence vertically? A) numpy.vstack B) numpy.hstack C) numpy.concat

Answer 43

A) numpy.vstack

Question 44

What is the purpose of numpy.transpose?

Answer 44

It transposes the dimensions of an array.

Question 45

True or False: NumPy arrays can be multidimensional.

Answer 45

True

Question 46

What does the method numpy.max return?

Answer 46

It returns the maximum value in the array.

Question 47

Fill in the blank: The function used to create a random array is ______.

Answer 47

numpy.random.rand

Question 48

What is the output of numpy.arange(5)?

Answer 48

An array: [0, 1, 2, 3, 4]

Question 49

Multiple Choice: Which of the following is used to perform matrix multiplication? A) numpy.multiply B) numpy.dot C) numpy.cross

Answer 49

B) numpy.dot

Question 50

What does the numpy.where function do?

Answer 50

It returns the indices of elements that satisfy a condition.

Question 51

True or False: NumPy can handle missing values in arrays.

Answer 51

True

Question 52

What function would you use to compute the cumulative sum of an array?

Answer 52

numpy.cumsum

Question 53

Basic Array Creation

Answer 53

1. 1D Array: np.array([1, 2, 3]) 2. 2D Array: np.array([[1, 2], [3, 4]]) 3. Zeros Array: np.zeros((3, 4)) 4. Ones Array: np.ones((2, 2)) 5. Identity Matrix: np.eye(3) 6. Range of Numbers: np.arange(0, 10, 2) 7. Evenly Spaced Numbers (Linspace): np.linspace(0, 1, 5)

Question 54

Array properties

Answer 54

1. Shape: a.shape 2. Size (Number of Elements): a.size 3. Data Type: a.dtype 4. Number of Dimensions: a.ndim

Question 55

mathematical operations in Arrays

Answer 55

1. Addition: a + b 2. Subtraction: a - b 3. Multiplication (Element-wise): a * b 4. Division (Element-wise): a / b 5. Matrix Multiplication: np.dot(a, b) or a @ b 6. Square Root: np.sqrt(a) 7. Power: np.power(a, 2)

Question 56

Statistical functions in Arrays

Answer 56

1. Sum of All Elements: np.sum(a) 2. Mean: np.mean(a) 3. Median: np.median(a) 4. Standard Deviation: np.std(a) 5. Variance: np.var(a) 6. Minimum and Maximum: np.min(a), np.max(a) 7. Index of Min/Max Element: np.argmin(a), np.argmax(a)

Question 57

Logical Operations in Arrays

Answer 57

1. Element-wise Comparison: a > 5 2. Check Equality: np.array_equal(a, b) 3. Any True: np.any(a > 5) 4. All True: np.all(a > 5)

Question 58

Logical Operations in Arrays

Answer 58

1. Element-wise Comparison: a > 5 2. Check Equality: np.array_equal(a, b) 3. Any True: np.any(a > 5) 4. All True: np.all(a > 5)

Question 59

Indexing and Slicing

Answer 59

1. Access Single Element: a[1] (1D), a[1, 2] (2D) 2. Slice Array: a[1:4] 3. Conditional Selection: a[a > 5]

Question 60

Reshaping and Flattening

Answer 60

1. Reshape: a.reshape((2, 3)) 2. Flatten: a.flatten()

Question 61

Random Number Generation using Arrays

Answer 61

1. Random Floats (0-1): np.random.rand(3, 2) 2. Random Integers: np.random.randint(1, 10, (2, 3)) 3. Normal Distribution: np.random.randn(3, 3)

Question 62

Miscellaneous Operations in Arrays

Answer 62

1. Transpose of Matrix: a.T 2. Concatenate Arrays: np.concatenate((a, b), axis=0) 3. Stack Arrays (Vertically): np.vstack((a, b)) 4. Stack Arrays (Horizontally): np.hstack((a, b)) 5. Unique Elements: np.unique(a) 6. Sorting: np.sort(a)