添加和与另一阵列相乘的numpy的阵列的列 [英] Adding and multiplying columns of a numpy array with another array

问题描述

我有一个 2D numpy的阵列 X 和和 1D numpy的阵列是：

I have a 2D numpy array x and and 1D numpy array y:

import numpy as np
x = np.arange(12).reshape((4, 3))
y = np.array(([1.0,2.0,3.0,4.0])

我要乘/加列向量 y.reshape（（4,1））到 X 。我尝试以下内容：

y1 = y.reshape((4,1))    
y1 * x 

收益

array([[ 0., 1., 2.], 
       [ 6., 8., 10.], 
       [ 18., 21., 24.], 
       [ 36., 40., 44.]])

这就是我想要的。我还发现

which is what I wanted. I also found

array([[ 1., 2., 3.], 
       [ 5., 6., 7.], 
       [ 9., 10., 11.], 
       [ 13., 14., 15.]])

与 Y1 + X 。结果
我想知道，如果有更好（更有效）的方式来达到同样的事情！

with y1 + x.
I would like to know if there is a better (more efficient) way to achieve the same thing!

推荐答案

numpy的通过广播支持这一点。您code使用的广播，它是做事的最有效的方法。我通常写为：

NumPy supports this via broadcasting. Your code used broadcasting and it's the most efficient way to do things. I normally write it as:

>>> x * y[..., np.newaxis]
array([[  0.,   1.,   2.],
       [  6.,   8.,  10.],
       [ 18.,  21.,  24.],
       [ 36.,  40.,  44.]])

要看到它是等价的：

>>> z =  y[..., np.newaxis]
>>> z.shape
(4, 1)

您还可以看到numpy的不复制任何数据，它只是改变了相同的内存迭代内部

You can also see that NumPy doesn't copy any data, it just changes the iteration over the same memory internally

>>> z.base is y
True

在这里阅读更多

• http://docs.scipy.org/doc/numpy/用户/ basics.broadcasting.html


• https://scipy-lectures.github.io/intro/ numpy的/ operations.html＃广播

• http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
• https://scipy-lectures.github.io/intro/numpy/operations.html#broadcasting

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