CODEWITHSUNDEEP
pythonnumpymultidimensional-array
Alexandre Kempf
Alexandre Kempf

Reputation: 989

transpose manipulation with indexing over multiple dimensions

I have a trouble with numpy ndarray when I'm indexing multiple dimensions at the same time : 

> a = np.random.random((25,50,30))
> b = a[0,:,np.arange(30)]
> print(b.shape)

Here I expected the result to be (50,30), but actually the real result is (30,50) !

Can someone explain it to me please I don't get it and this feature introduces tons of bugs in my code. Thank you :)

Additional information :

Indexing in one dimension works perfectly :

> b = a[0,:,:]
> print(b.shape)
(50,30)

And when I have the transposition :

> a[0,:,0] == b[0,:]
True

Upvotes: 0

Views: 70

Answers (2)

Arthur
Arthur

Reputation: 573

When you use a list or array of integers to index a numpy array, you're using something that is known as Fancy Indexing. The rules for Fancy Indexing are not so straightforward as one might think. This is the reason that you're array has the wrong dimension. To avoid surprises, I'd recommend you to stick with slicing. So, you should change your code to:

a = np.random.random((25,50,30))
b = a[0,:,:]
print(b.shape)

Upvotes: 1

Paul Panzer
Paul Panzer

Reputation: 53029

From numpy docs

The easiest way to understand the situation may be to think in terms of the result shape. There are two parts to the indexing operation, the subspace defined by the basic indexing (excluding integers) and the subspace from the advanced indexing part. Two cases of index combination need to be distinguished:

The advanced indexes are separated by a slice, ellipsis or newaxis. For example x[arr1, :, arr2].

The advanced indexes are all next to each other. For example x[..., arr1, arr2, :] but not x[arr1, :, 1] since 1 is an advanced index in this regard.

In the first case, the dimensions resulting from the advanced indexing operation come first in the result array, and the subspace dimensions after that. In the second case, the dimensions from the advanced indexing operations are inserted into the result array at the same spot as they were in the initial array (the latter logic is what makes simple advanced indexing behave just like slicing).

(my emphasis) the highlighted bit applies to your

b = a[0,:,np.arange(30)]

Upvotes: 1

Related Questions