x = np.array([[‘a0’, ‘a1’],[‘b0’,‘b1’]]) >>> y = np.array([[‘x0’, ‘x1’],[‘y0’,‘y1’]]) >>> iterable = [np.outer(x[i],y[i]) for i in xrange(x.shape[0])] >>> elbareti = np.asarray(iterable) >>> elbareti array([[[ ‘a0’

‘x0’, ‘a0’‘x1’ ], [ ‘a1’‘x0’, ‘a1’‘x1’ ]],

```
[[ 'b0'*'y0', 'b0'*'y1' ],
[ 'b1'*'y0', 'b1'*'y1' ]]])
```

Since i’m planning on working with large arrays, is there a more numpy-like version of this? I feel like the answer is right under my nose and I’m thinking it has something to do with `reduce`

, but numpy’s version only works with `ufunc`

s, not functions. Even a hint would be greatly appreciated.

Thanks in advance.

Best answer

Is this what you’re looking for?

```
x = np.array([[1,2], [3,4]])
y = np.array([[5,6], [7,8]])
x[:,:,np.newaxis] * y[:,np.newaxis,:]
array([[[ 5, 6],
[10, 12]],
[[21, 24],
[28, 32]]])
```

EDIT:

Btw, it’s alway useful to look the implementation. Helps understanding the “magic”. `np.outer`

looks like this:

```
return a.ravel()[:,newaxis]*b.ravel()[newaxis,:]
```

From here, it’s easy.

Also, in you question, you have:

```
[np.outer(x[i],y[i]) for i in xrange(x.shape[0])]
```

Better written as:

```
[np.outer(xx,yy) for xx,yy in izip(x,y)]
```