Adding and multiplying columns of a numpy array with another array

Question

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])

I want to multiply / add the column vector y.reshape((4,1)) to each column of x. I attempted the following:

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

yields

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

which is what I wanted. I also found

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

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

YXD · Accepted Answer · 2015-03-16 22:01:04Z

2

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.]])

To see that it is equivalent:

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

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

Comments

Kasravnd · Accepted Answer · 2015-03-16 22:10:55Z

You can use np.add and np.multiply :

>>> np.add(y1,x)
array([[  1.,   2.,   3.],
       [  5.,   6.,   7.],
       [  9.,  10.,  11.],
       [ 13.,  14.,  15.]])
>>> np.multiply(y1,x)
array([[  0.,   1.,   2.],
       [  6.,   8.,  10.],
       [ 18.,  21.,  24.],
       [ 36.,  40.,  44.]])

OR for in-place changing you can use iadd and imul methods of a numpy ndarray :

>>> x.__iadd__(y1)
array([[ 1,  2,  3],
       [ 5,  6,  7],
       [ 9, 10, 11],
       [13, 14, 15]])
>>> x = np.arange(12).reshape((4, 3))
>>> x.__imul__(y1)
array([[ 0,  1,  2],
       [ 6,  8, 10],
       [18, 21, 24],
       [36, 40, 44]])

But note that :

In place operations will perform the calculation using the precision decided by the data type of the two operands, but will silently downcast the result (if necessary) so it can fit back into the array. Therefore, for mixed precision calculations, A {op}= B can be different than A = A {op} B. For example, suppose a = ones((3,3)). Then, a += 3j is different than a = a + 3j: while they both perform the same computation, a += 3 casts the result to fit back in a, whereas a = a + 3j re-binds the name a to the result.

Read more about arithmetic-and-comparison-operations

Collectives™ on Stack Overflow

Adding and multiplying columns of a numpy array with another array

2 Answers 2

Comments

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related