Numpy

Question 1

np.array([1, 2, 3, 4], dtype=’float32’)

Answer 1

explicitly set the data type of the resulting array

Question 2

np.array([range(i, i + 3) for i in [2, 4, 6]])

Answer 2

nested lists result in multi-dimensional arrays

Question 3

np.zeros(10, dtype=int)

Answer 3

Create a length-10 integer array filled with zeros

Question 4

np.ones((3, 5), dtype=float)

Answer 4

Create a 3x5 floating-point array filled with ones

Question 5

np.full((3, 5), 3.14)

Answer 5

Create a 3x5 array filled with 3.14

Question 6

np.arange(0, 20, 2)

Answer 6

# Create an array filled with a linear sequence
# Starting at 0, ending at 20, stepping by 2
# (this is similar to the built-in range() function)

Question 7

np.linspace(0, 1, 5)

Answer 7

Create an array of five values evenly spaced between 0 and 1

Question 8

np.random.random((3, 3))

Answer 8

# Create a 3x3 array of uniformly distributed
# random values between 0 and 1

Question 9

np.random.normal(0, 1, (3, 3))

Answer 9

# Create a 3x3 array of normally distributed random values
# with mean 0 and standard deviation 1

Question 10

np.random.randint(0, 10, (3, 3))

Answer 10

Create a 3x3 array of random integers in the interval [0, 10)

Question 11

np.eye(3

Answer 11

Create a 3x3 identity matrix

Question 12

np.empty(3)

Answer 12

# Create an uninitialized array of three integers
# The values will be whatever happens to already exist at that memory location

Question 13

x2[1:2, 0]

Answer 13

In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices

Question 14

x2[0:, 0] = 15.8

x2

Answer 14

Values can also be modified using any of the above index notation

Question 15

x[1::2]

Answer 15

every other element, starting at index 1

Question 16

x[:2:-1]

Answer 16

all elements, reversed

Question 17

x2[:3, ::2]

Answer 17

all rows, every other column

Question 18

x2[0, :]

Answer 18

first row of x2

Question 19

x2[:, 0]

Answer 19

first column of x2

Question 20

x2_sub_copy = x2[:2, :2].copy()

Answer 20

explicitly copy the data within an array or a subarray

Question 21

grid = np.arange(1, 10).reshape((3, 3))

Answer 21

the size of the initial array must match the size of the reshaped array

Question 22

x[np.newaxis, :]

Answer 22

row vector via newaxis

Question 23

np.concatenate([grid, grid], axis=1)

Answer 23

concatenate along the second axis (zero-indexed)

Question 24

np.vstack([x, grid])

Answer 24

vertically stack the arrays

Question 25

np.hstack([grid, y])

Answer 25

horizontally stack the arrays

Question 26

x = [1, 2, 3, 99, 99, 3, 2, 1]

x1, x2, x3 = np.split(x, [3, 6])

Answer 26

opposite of concatenation is splitting

pass a list of indices giving the split points

Question 27

upper, lower = np.vsplit(grid, [2])

left, right = np.hsplit(grid, [1])

Answer 27

N + 1 subarrays. The related functions np.hsplit and np.vsplit are similar