import numpy as np

z = [1,2,3,4,5,6,7,8,9.3,10.6] # This is a list
z_array = np.array(z)
z_array

z_array.sum()

z_array.min()

z_array.max()

np.max(z_array)

np.array(['a','b','c'])

np.array([1,2,3,6,8,29])

y = [1,3,'a']
np.array(y)

np.array([1,3,'a'], dtype='object')

np.arange(10)

np.linspace(start=0, stop=1, num=11) # or np.linspace(0, 1, 11)

np.linspace(start = 0, stop = 2*np.pi, num = 10)

np.array([[1,3,5,6],[4,3,9,7]])

np.zeros(10)

np.zeros((10,10))

np.ones((3,4))

np.eye(4)

a = [1,2,3,4,5,6,7,8]
b = ['a','b','c','d','3']

np.array(a)

np.array(b)

rng = np.random.RandomState(35) # set seed
rng.randint(0, 10, (3,4))

rng.random_sample((5,2))

A = rng.normal(0,1,(4,5))

A.shape

A.size

np.zeros_like(A)

B = rng.normal(0, 1, (4,))
B

A

A[:,0]

A[3,:]

A[:2,:2]

A = rng.randn(3,5)
A

A + 10

B = rng.randint(0,10, (3,5))
B

A + B

A * B

A @ np.transpose(B)

np.transpose(A) @ B

D = rng.randint(0,5, (4,4))
D

D.reshape(8,2)

D.reshape(1,16)

e = np.arange(20)
E = e.reshape(5,4)
E

A = rng.normal(0, 1, (4,2))
A

A.sum()

A.sum(axis=0)

A.sum(axis=1)

A.min(axis = 0)

A.max(axis = 0)

A.argmin(axis=0)

A.argmax(axis=0)

a = rng.randint(0,10, 8)
a

np.sort(a)

np.argsort(a)

a[np.argsort(a)]

ind_2nd_smallest = np.argsort(a)[1]
a[ind_2nd_smallest]

ind_3rd_largest = np.argsort(a)[-3]
a[ind_3rd_largest]

m = np.array(['Aram','Raymond','Elizabeth','Donald','Harold'])
np.sort(m)

m.sort()
m

A = rng.randint(0,5, (3,5))
B = rng.randint(0,5, (3,5))
print('A = ', A)
print('B = ', B)

np.hstack((A,B))

np.vstack((A,B))

A < 0

A[A<0]

np.sum(A<0)

A[0,:]

A1 = A
A1[0,0] = 4
A[0,0]

A1 = A.copy()
A1[0,0] = 6
A[0,0]

A[:2,:2] = np.eye(2)
A

A = rng.randint(0,5, (5,1))
A

A.flatten()

A.ravel()

A = rng.normal(0,1,(4,5))
B = rng.normal(0,1,5)

A.shape

B.shape

A - B

B[np.newaxis,:].shape

B[:,np.newaxis].shape

d = rng.random_sample((10,2))
d

d.shape

d[np.newaxis,:,:]

d[np.newaxis,:,:].shape

d[:, np.newaxis,:]

d[:,np.newaxis,:].shape

dist_sq = np.sum((d[:,np.newaxis,:] - d[np.newaxis,:,:]) ** 2)

dist_sq.shape

dist_sq

(d[:,np.newaxis,:] - d[np.newaxis,:,:]).shape

dist_sq = np.sum((d[:,np.newaxis,:] - d[np.newaxis,:,:]) ** 2, axis=2)

dist_sq

dist_sq.shape

dist_sq.diagonal()