How to generate commonly used np.arrays ?

arange+reshape

n = np.arange(0, 30, 2)# start at 0 count up by 2, stop before 30
n = n.reshape(3, 5) # reshape array to be 3x5

linspace+resize

o = np.linspace(0, 4, 9)
o.resize(3, 3)

About Resize and Reshape

reshape is not inplace operation and will return an value

resize is inplace operation without feedback

>> X = np.random.randn(2, 3)
>> X
array([[ 1.23077478, -0.70550605, -0.37017735],
       [-0.61543319,  1.1188644 , -1.05797142]])

>> X.reshape((3, 2))
array([[ 1.23077478, -0.70550605],
       [-0.37017735, -0.61543319],
       [ 1.1188644 , -1.05797142]])

>> X
array([[ 1.23077478, -0.70550605, -0.37017735],
       [-0.61543319,  1.1188644 , -1.05797142]])

>> X.resize((3, 2))
>> X
array([[ 1.23077478, -0.70550605],
       [-0.37017735, -0.61543319],
       [ 1.1188644 , -1.05797142]])

ones, zeros eye diag random.randint

In

np.ones((3, 2))
np.zeros((2, 3))
np.eye(3)#3维单位矩阵
y = np.array([4, 5, 6])
np.diag(y)#以y为主对角线创建矩阵
np.random.randint(0, 10, (4,3))

Out

ones:
[[ 1. 1.]
[ 1. 1.]
[ 1. 1.]]
zeros:
[[ 0. 0. 0.]
[ 0. 0. 0.]]
eye:
[[ 1. 0. 0.]
[ 0. 1. 0.]
[ 0. 0. 1.]]
diag:
[[4 0 0]
[0 5 0]
[0 0 6]]
randint
[[1 3 5]
[4 4 3]
[9 3 0]
[7 0 0]]

Compound Matrix

In

p = np.ones([2, 3], int)
np.hstack([p, 2*p])#水平拼接
np.vstack([p, 2*p])#竖直拼接

Out

hstack：
[[1 1 1 2 2 2]
[1 1 1 2 2 2]]
vstack:
[[1 1 1]
[1 1 1]
[2 2 2]
[2 2 2]]