Numpy3

Question 1

a = np.array([0, 1, 2])
b = np.array([5, 5, 5])
a + b

Answer 1

array([5, 6, 7])

for arrays of the same size, binary operations are performed on an element-by-element basis

Question 2

Rule 1: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is padded with ones on its leading (left) side.
Rule 2: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other shape.
Rule 3: If in any dimension the sizes disagree and neither is equal to 1, an error is raised.

Answer 2

Rules of Broadcasting

Question 3

np.newaxis

Answer 3

is used to increase the dimension of the existing array by one more dimension, when used once. Thus,

1D array will become 2D array

2D array will become 3D array

3D array will become 4D array

Question 4

x = np.array([1, 2, 3, 4, 5])

x < 3

Answer 4

array([ True, True, False, False, False], dtype=bool)

Question 5

(2 * x) == (x ** 2)

Answer 5

element-wise comparison of two arrays, and to include compound expressions
array([False, True, False, False, False], dtype=bool)

Question 6

== np.equal != np.not_equal
< np.less <= np.less_equal
> np.greater >= np.greater_equal

Answer 6

comparison operators are implemented as ufuncs in NumPy

Question 7

# how many values less than 6?
#np.count_nonzero(x < 6)
np.sum(inches >.1)

Answer 7

To count the number of True entries in a Boolean array, np.count_nonzero is useful:

Question 8

np.sum(x < 6, axis=1)

Answer 8

how many values less than 6 in each row?

Question 9

# are there any values greater than 8?
np.any(x > 8)

Answer 9

quickly checking whether any or all the values are true

Question 10

np.all(x < 10)

# are all values equal to 6?
np.all(x == 6)

Answer 10

quickly checking whether any or all the values are true

Question 11

np.sum((inches > 0.5) & (inches < 1))

Answer 11

bitwise logic operators, &, |, ^, and ~

Question 12

& np.bitwise_and | np.bitwise_or

^ np.bitwise_xor ~ np.bitwise_not

Answer 12

bitwise Boolean operators and their equivalent ufuncs

Question 13

“Number days without rain: “, np.sum(inches == 0))
“Number days with rain: “, np.sum(inches != 0))
“Days with more than 0.5 inches:”, np.sum(inches > 0.5))
“Rainy days with < 0.2 inches :”, np.sum((inches > 0) &
(inches < 0.2)))

Answer 13

bitwise Examples

Question 14

x[x < 5]

Answer 14

to select these values from the array, we can simply index on this Boolean array; this is known as a masking operation

Question 15

np.median(inches[rainy & ~summer]

Answer 15

Example operation