[Numpy-discussion] use index array of len n to select columns of n x m array

Martin Spacek numpy@mspacek.mm...
Thu Aug 5 15:07:50 CDT 2010


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


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