import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

np.array([1, 2, 3])

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

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

np.arange(5, 10)

np.linspace(5, 10, 10)

np.empty((3, 2))

np.zeros((3, 2))

np.ones((3, 2))

np.eye(3)

np.diag([1, 2, 3])

np.repeat(3, 5)

np.repeat([[1, 2, 3]], 3, axis=0)

a = np.arange(3 * 4).reshape(3, -1)

a.shape

a.size

a.dtype

a.nbytes

np.random.seed(0)

np.random.rand(3, 4)

np.random.randn(3, 4)

plt.hist(np.random.normal(10, 2.0, 1000));

plt.hist(np.random.binomial(1, 0.8, 1000));

v = np.arange(3)
v

v.reshape(-1, 1) # column matrix

v[:, np.newaxis] # column matrix

v.reshape(1, -1) # row vector

v[np.newaxis, :] # row vector

r = v[np.newaxis, :] # returns view -> no copy
r[0, 0] = 10

r

v

A = np.arange(3 * 4).reshape(3, -1)
A

A.flatten()

A.flatten()[0] = 10
A

A.ravel()

A.ravel()[0] = 100
A

A.ravel()

a = np.arange(10)

a[1]

a[[0, 5]]

a[-1]

a[:5]

a[5:]

a[5::2]

b = a[::2]

b[2] = -1

b

a

a[a > 5]

A = np.arange(3 * 4).reshape(3, 4)

A

A[1, :]

b = A.take(1, axis=0)

A.take(1, axis=1)

b

b[0] = 10

b

A

A = np.arange(1, 7).reshape(3,2)
A

A * 2

A + 2

v = np.array([1, 2])
v

A.dot(v)

v.dot(v)

A.sum()

A.sum(axis=0)

A.sum(axis=1)

A.prod(axis=0)

A.mean(axis=0)

A.std(axis=0)

A.min(axis=0)

A

# Scaling rows
r = np.array([[2, 3]])
A * r

# Scaling columns
c = np.array([1, 2, 3]).reshape(3, -1)
A * c
A      (2d array):  5 x 4
B      (1d array):      1
Result (2d array):  5 x 4

A      (2d array):  5 x 4
B      (1d array):      4
Result (2d array):  5 x 4

A      (3d array):  15 x 3 x 5
B      (3d array):  15 x 1 x 5
Result (3d array):  15 x 3 x 5

A      (3d array):  15 x 3 x 5
B      (2d array):       3 x 5
Result (3d array):  15 x 3 x 5

A      (3d array):  15 x 3 x 5
B      (2d array):       3 x 1
Result (3d array):  15 x 3 x 5
A = np.ones((2, 3, 5))
b = np.arange(3).reshape(3, 1)

A

b

A + b

Ac = np.ones((10, 100000), order='C') # C order
Af = np.ones((10, 100000), order='F') # Fortran order

%timeit Ac.sum(axis=0)

%timeit Af.sum(axis=0)

Ac = np.ones((100000, 10), order='C')
Af = np.ones((100000, 10), order='F')

%timeit Ac.sum(axis=1)

%timeit Af.sum(axis=1)