[Numpy-discussion] use index array of len n to select columns of n x m array
Bruce Southey
bsouthey@gmail....
Thu Aug 5 15:47:46 CDT 2010
On 08/05/2010 03:07 PM, Martin Spacek wrote:
> josef.pkt wrote:
>>>> a = np.array([[0, 1],
> [2, 3],
> [4, 5],
> [6, 7],
> [8, 9]])
>>>> i = np.array([0, 1, 1, 0, 1])
>>>> a[range(a.shape[0]), i]
> array([0, 3, 5, 6, 9])
>>>> a[np.arange(a.shape[0]), i]
> array([0, 3, 5, 6, 9])
>
>
> Thanks for all the tips. I guess I was hoping for something that could avoid
> having to generate np.arange(a.shape[0]), but
>
> >>> a[np.arange(a.shape[0]), i]
>
> sure is easy to understand. Is there maybe a more CPU and/or memory efficient
> way? I kind of like John Salvatier's idea:
>
> >>> np.choose(i, (a[:,0], a[:,1])
>
> but that would need to be generalized to "a" of arbitrary columns. This could be
> done using split or vsplit:
>
> >>> np.choose(i, np.vsplit(a.T, a.shape[1]))[0]
> array([0, 3, 5, 6, 9])
>
> That avoids having to generate an np.arange(), but looks kind of wordy. Is there
> a more compact way? Maybe this is better:
>
> >>> b, = i.choose(np.vsplit(a.T, a.shape[1]))
> >>> b
> array([0, 3, 5, 6, 9])
>
> Ah, but I've just discovered a strange limitation of choose():
>
> >>> a = np.arange(9*32)
> >>> a.shape = 9, 32
> >>> i = np.random.randint(0, a.shape[1], size=a.shape[0])
> >>> i
> array([ 1, 21, 23, 2, 30, 23, 20, 30, 17])
> >>> b, = i.choose(np.vsplit(a.T, a.shape[1]))
> Traceback (most recent call last):
> File "<input>", line 1, in<module>
> ValueError: Need between 2 and (32) array objects (inclusive).
>
> Compare with:
>
> >>> a = np.arange(9*31)
> >>> a.shape = 9, 31
> >>> i = np.random.randint(0, a.shape[1], size=a.shape[0])
> >>> i
> array([14, 22, 18, 6, 1, 12, 8, 8, 30])
> >>> b, = i.choose(np.vsplit(a.T, a.shape[1]))
> >>> b
> array([ 14, 53, 80, 99, 125, 167, 194, 225, 278])
>
> So, the ValueError should really read "Need between 2 and 31 array object
> (inclusive)", should it not? Also, I can't seem to find this limitation in the
> docs for choose(). I guess I'll stick to using the np.arange(a.shape[0]) method.
>
> Martin
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
I think you might want numpy's where function:
>>> np.where(i,a[:,1],a[:,0])
array([0, 3, 5, 6, 9])
Bruce
More information about the NumPy-Discussion
mailing list