Broadcasting a 1d numpy array with a 2d numpy array
This is probably a really simple question, but I am not figuring this out.
I have a 2D numpy array, which is of shape (3,2) and a 1D array of shape (3,):
A = [[2,4],[6,8][10,12]]
B = [1,2,4]
I would like to divide array A by array B, resulting in:
[[2,4],[3,4][2.5,3]]
But numpy will not let me do this, I think because the shape is not right. I get the familiar 'operands could not be broadcast together with shapes (10,2) (10,)' error.
I tried things with reshape and swapaxis, but it is not working. I would much prefer to be able to do this without a for loop (because I need to do this many many times with large arrays) and without having to swap the axis of array A (because other arrays are if this shape).
Can you guys help me?
Extend B to 2D and then divide -
A/B[:,None].astype(float)
Sample run -
In [9]: A
Out[9]:
array([[ 2, 4],
[ 6, 8],
[10, 12]])
In [10]: B
Out[10]: array([1, 2, 4])
In [11]: A/B[:,None].astype(float)
Out[11]:
array([[ 2. , 4. ],
[ 3. , 4. ],
[ 2.5, 3. ]])
Or use from __future__ import division that takes care of division to result in a floating pt array -
In [14]: from __future__ import division
In [15]: A/B[:,None]
Out[15]:
array([[ 2. , 4. ],
[ 3. , 4. ],
[ 2.5, 3. ]])
Performance boost with multiplication by reciprocal -
In [32]: A = np.random.rand(300,200)
In [33]: B = np.random.rand(300)
In [34]: from __future__ import division
In [35]: %timeit A/B[:,None]
1000 loops, best of 3: 336 µs per loop
In [36]: %timeit A*(1.0/B[:,None])
10000 loops, best of 3: 101 µs per loop
More info on this could be found here . Also, one needs to be careful using this method though, if the values of B are extremely close to 0 .
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