Numpy

Question 1

create a regular array

Answer 1

a=[1,2,3,4]

Question 2

create a ndarray, or a np array

Answer 2

B=np.array([1,2,3,4,5,6,55,5])

Question 3

create a numpy array that is 2x5 matrix

Answer 3

a=[[1,2,3,4,5],[6,7,8,9,10]]
A=np.array(a)# create a np array. A is also a 2 by 5 matrix
A

Question 4

find type

Answer 4

A.dtype

Question 5

returns a np array starting from 0 to 10, but not including 10. The increment is 2 each time.

Answer 5

np.arange(0,10,2)

array([0, 2, 4, 6, 8])

Question 6

show the shape of a array

Answer 6

A.shape
(2, 5)
B=np.array([1, 2, 3, 4, 5])
B.shape
(5,)

Question 7

How do we make B an 5 by 1 matrix?

Answer 7

#reshape function returns to a multi-dim matrix.  Bis is 5 element in 1 dimension. Notice the double brackets!
B=np.array([1, 2, 3, 4, 5])
print(B.reshape(1,5))
[[1 2 3 4 5]]

Question 8

reshape to a 3x3 matrix.

Answer 8

only applys to ndarray
C =np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) 
C.reshape(3,3)
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

Question 9

create 4x3 matrix of zeros

Answer 9

np.zeros((4,3)) 
array([[0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]])
np.zeros(8)
array([0., 0., 0., 0., 0., 0., 0., 0.])

Question 10

create 2x2 1’s

Answer 10

np.ones((2,2))
array([[1., 1.],
[1., 1.]])

Question 11

eye stands for Identity and sysmetric. This function retruns to an edentity matrix with specific dimentions.

Answer 11

np.eye(6)

array([[1., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0.],
       [0., 0., 1., 0., 0., 0.],
       [0., 0., 0., 1., 0., 0.],
       [0., 0., 0., 0., 1., 0.],
       [0., 0., 0., 0., 0., 1.]])

Question 12

generate another (3 x 3) matrix to be multiplied.

Answer 12

D = np.arange(1,10).reshape(3,3) # no stepsize is specified means stepsize is 1. 
D
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

Question 13

Please using numpy random to create one 2by4 random uniform array, named A and one 4by2 random uniform array, named B.

Then get the dot product of A and B.

Get the dot product of B and A.

Answer 13

A=np.random.rand(2,4)
B=np.random.rand(4,2)
print(np.dot(A,B))
print(np.dot(B,A))

[[0.41745136 0.36938202]
[0.869284 0.79135864]]
[[0.12728448 0.44244018 0.39016312 0.70958712]
[0.18397938 0.68209005 0.53437116 1.14459566]
[0.12598516 0.45241722 0.37611223 0.74283167]
[0.00561522 0.01666167 0.01919675 0.02332323]]

Question 14

create float array

Answer 14

A=np.array([[1,2,3,4,5],[6,7,8,9,10]],dtype=np.float64)
A
array([[ 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10.]])

Question 15

create int array

Answer 15

B=np.array([[1,2,3,4,5],[6,7,8,9,10]],dtype=np.int64)
B

array([[ 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10]])

Question 16

cast array to float

Answer 16

A=np.array([[1,2,3,4,5],[6,7,8,9,10]]) # by defining this, type of A will be int64.
B=A.astype(np.float64)
B

Question 17

float to int

Answer 17

2
myFloat = -10.8;
print(int(myFloat));

Question 18

create numpy array 1 thru 9

Answer 18

a=np.arange(10) # create a one-dimentional array.

Question 19

return to a np array from first to third elements, or elements with index 0-2.

Answer 19

a[0:3]

array([0, 1, 2])

Question 20

insert value 10 in front of 50 on a list.

then print sum

Answer 20

List = [True, 50, 10]
List.insert(2, 10)
print(List, “Sum is: “, sum(List))

Question 21

how do you tell if it’s a float, int, string

Answer 21

type(myFloat)

Question 22

pull value from list 7th position

Answer 22

a[6]

Question 23

retrieve first 9 values in array

Answer 23

array1[0:9] . it would be position 0 thru 8

Question 24

insert value 10 for first 5 positions in array

Answer 24

arrayB[0:4]= 10

Question 25

make a new array with slice of another array

Answer 25

arrayc=arrayb[0:5]

Question 26

make 3x3 matrix with values 1-9

Answer 26

arrayd=np.array([[1,2,3],[4,5,6],[7,8,9]])

dont forget the double braces

Question 27

what is output of A[:2] assuming 1-9 3x3

Answer 27

rows 0 and 1, all colums
array([[1, 2],
[4, 5],
[7, 8]])

Question 28

A[:2,1:]

Answer 28

row 0 and 1, columes 1 to end.
array([[2, 3],
[5, 6]])

Question 29

create random numpy array 2 rows 4 columns

Answer 29

arr=np.random.randn(2,4)
array([[-1.00636069, 0.72807051, 0.49342933, 1.15461629],
[ 1.49388338, 0.36216412, 0.66747094, -1.51994783]])

Question 30

create a np array with three axes, axis 0 size is 2, axis 1 size is 3, axis 2 size is 4.

Answer 30

arr=np.arange(24).reshape(2,3,4)
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],

       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]],
arr=np.arange(36).reshape(3,3,4)
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],

   [[12, 13, 14, 15],
    [16, 17, 18, 19],
    [20, 21, 22, 23]],

   [[24, 25, 26, 27],
    [28, 29, 30, 31],
    [32, 33, 34, 35]]])

Question 31

swap axis

Answer 31

arr.swapaxes(1,2)

array([[[ 0, 4, 8],
[ 1, 5, 9],
[ 2, 6, 10],
[ 3, 7, 11]],

   [[12, 16, 20],
    [13, 17, 21],
    [14, 18, 22],
    [15, 19, 23]]])

Question 32

create array using a range of numbers

then convert to square root of the values

Answer 32

arr=np.arange(10)
np.sqrt(arr)
arr([1,2,3,4,5,6,7,8])
array([0. , 1. , 1.41421356, 1.73205081, 2. ,
2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ])

Question 33

generate array of 8 with random generated numbers.

Answer 33

a=np.random.randn(8)

a

Question 34

add 2 arrays together

Answer 34

np.add(x,y)
or g=np.add(x,y)

x=np.array([[1,2,3],[4,5,6],[7,8,9]])
#x
y=np.array([[1,2,3],[4,5,6],[7,8,9]])
array([[ 2,  4,  6],
       [ 8, 10, 12],
       [14, 16, 18]])

Question 35

show maxium of each element comparing 2 arrays

Answer 35

np.maximum(x,y)
array([[ 2, 4, 6],
[ 8, 10, 12],
[14, 16, 18]])

Question 36

2 4x4 random arrays normal distribution

Answer 36

x=np.random.randn(4,4)

y=np.random.randn(4,4)

Question 37

compare 2 arrays where and show highest in new array

Answer 37

newarray=np.where(x>y,x,y)

Question 38

create random 5x3 and replace neg numbers with 1

Answer 38

a=np.random.randn(5,3)

b=np.where(x>0,x,1)

Question 39

compute the mean over columns. its the same as a.mean(axis=1)

Answer 39

a.mean(1)
column 0
row 1
axis = 0 means along the column and axis = 1 means working along the row.

Question 40

add all the array elements

Answer 40

b=np.array([[1,3],[2,4]])
b
b.sum()

Question 41

average of all the elements of array

Answer 41

a.mean()

Question 42

Exercise

Please generate a random numpy array of size 200 from standard normal distribution. Then please find the 5% quantile

Answer 42

n=200
a=np.random.randn(n)
a.sort()
a[int(n*0.05)-1]

Question 43

create regular array and create numpy array

Answer 43

a=[1,2,3,4]

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