[Numpy-discussion] Multidimensional Indexing
Sebastian Berg
sebastian at sipsolutions.net
Tue Apr 7 03:58:59 EDT 2015
On Di, 2015-04-07 at 00:49 +0100, Nicholas Devenish wrote:
> With the indexing example from the documentation:
>
> y = np.arange(35).reshape(5,7)
>
> Why does selecting an item from explicitly every row work as I’d expect:
> >>> y[np.array([0,1,2,3,4]),np.array([0,0,0,0,0])]
> array([ 0, 7, 14, 21, 28])
>
> But doing so from a full slice (which, I would naively expect to mean “Every Row”) has some…other… behaviour:
>
> >>> y[:,np.array([0,0,0,0,0])]
> array([[ 0, 0, 0, 0, 0],
> [ 7, 7, 7, 7, 7],
> [14, 14, 14, 14, 14],
> [21, 21, 21, 21, 21],
> [28, 28, 28, 28, 28]])
>
> What is going on in this example, and how do I get what I expect? By explicitly passing in an extra array with value===index? What is the rationale for this difference in behaviour?
>
The rationale is historic. Indexing with arrays (advanced indexing)
works different from slicing. So two arrays will be iterated together,
while slicing is not (we sometimes call it outer/orthogonal indexing for
that matter, there is just a big discussion about this).
These are different beasts, you can basically get the slicing like
behaviour by adding appropriate axes to your indexing arrays:
y[np.array([[0],[1],[2],[3],[4]]),np.array([0,0,0,0,0])]
The other way around is not possible. Note that if it was the case:
y[:, :]
would give the diagonal (if possible) and not the full array as you
would probably also expect.
One warning: If you index with more then one array (scalars are also
arrays in this sense -- so `[0, :, array]` is an example) in combination
with slices, the result can be transposed in a confusing way (it is not
that difficult, but usually unexpected).
- Sebastian
> Thanks,
>
> Nick
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