numpy.newaxis用法

>> type(np.newaxis)
NoneType
>> np.newaxis == None
True

In [1]: np.linspace(1, 10, 10)
Out[1]: array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])

In [2]: np.linspace(1, 10, 10)[np.newaxis,:]
Out[2]: array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]])

In [3]: np.linspace(1, 10, 10)[:,np.newaxis]
Out[3]:
array([[ 1.],
[ 2.],
[ 3.],
[ 4.],
[ 5.],
[ 6.],
[ 7.],
[ 8.],
[ 9.],
[ 10.]])

In [4]: np.linspace(1, 10, 10).shape
Out[4]: (10,)

In [5]: np.linspace(1, 10, 10)[np.newaxis,:].shape
Out[5]: (1, 10)

In [6]: np.linspace(1, 10, 10)[:,np.newaxis].shape
Out[6]: (10, 1)

>> x = np.arange(3)
>> x
array([0, 1, 2])
>> x.shape
(3,)

>> x[:, np.newaxis]
array([[0],
       [1],
       [2]])

>> x[:, None]
array([[0],
       [1],
       [2]])

>> x[:, np.newaxis].shape
 (3, 1)

>>> X = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
>>> X[:, 1]
array([2, 6, 10])       % 这里是一个行
>>> X[:, 1].shape       % X[:, 1] 的用法完全等同于一个行，而不是一个列，
(3, )

>>>X[:, 1][:, np.newaxis]
array([[2],
      [6],
      [10]])

>>>X_sub = np.hstack([X[:, 1][:, np.newaxis], X[:, 3][:, np.newaxis]])           
                   % hstack：horizontal stack，水平方向上的层叠
>>>X_sub
array([[2, 4]
      [6, 8]
      [10, 12]])

>> X[:, [1, 3]]
array([[ 2,  4],
       [ 6,  8],
       [10, 12]])

>> X = np.random.randint(0, 9, (2, 3))
>> mean_X = np.mean(X, axis=0)
>> X - mean_X                      # 这样做是没有问题的

>> mean_X = np.mean(X, axis=1)
>> X - mean_X
ValueError: operands could not be broadcast together with shapes (2,3) (2,)