b[0, 1, 1] means 1st row 2 second level 2 second number

np.zeros((2,3)) means 2 rows and 3 columns with zeros

np.full((2,2), number you want)

np.full_like(a, 4) means any number and also 4

np.random.rand(4,2)

np.random.random_sample()

np.random.randint(7, size=())

np.identity()

r1 = np.repeat(array, times)

with two dimensional array do axis= 0

output = np.ones((5,5))

z = np.zeros((3.3))
z[1,1] = 9

output[1:4, 1:4] = z

when copying be careful even though you assign in new variable it changes

a = np.array()
b = a.copy()

you can do a * 2 a / 2 a + 2 a - 2

a += 2

a ** 2

np.sin(a)

np.cos(a)

a = np.ones((2,3))

b = np.full((3,2), 2)

np.matmul(a, b)

np.sum(array)

after = before.reshape((8,1))

np.vstack([array1, array2])