python - Advanced slicing when passed list instead of tuple in numpy

Question

In the docs, it says (emphasis mine):

Advanced indexing is triggered when the selection object, obj, is a non-tuple sequence object, an ndarray (of data type integer or bool), or a tuple with at least one sequence object or ndarray (of data type integer or bool). There are two types of advanced indexing: integer and Boolean.

<snip>

Also recognize that x[[1,2,3]] will trigger advanced indexing, whereas x[[1,2,slice(None)]] will trigger basic slicing.

I know why x[(1, 2, slice(None))] triggers basic slicing. But why does x[[1,2,slice(None)]] trigger basic slicing, when [1,2,slice(None)] meets the condition of being a non-tuple sequence?

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On a related note, why does the following occur?

>>> a = np.eye(4)
>>> a[(1, 2)]  # basic indexing, as expected
0.0
>>> a[(1, np.array(2))] # basic indexing, as expected
0.0

>>> a[[1, 2]]  # advanced indexing, as expected
array([[ 0.,  1.,  0.,  0.],
   [ 0.,  0.,  1.,  0.]])
>>> a[[1, np.array(2)]]  # basic indexing!!??
0.0

Answer 1

There's an exception to that rule. The Advanced Indexing documentation section doesn't mention it, but up above, near the start of the Basic Slicing and Indexing section, you'll see the following text:

In order to remain backward compatible with a common usage in Numeric, basic slicing is also initiated if the selection object is any non-ndarray sequence (such as a list) containing slice objects, the Ellipsis object, or the newaxis object, but not for integer arrays or other embedded sequences.

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a[[1, np.array(2)]] doesn't quite trigger basic indexing. It triggers an undocumented part of the backward compatibility logic, as described in a comment in the source code:

    /*
     * Sequences < NPY_MAXDIMS with any slice objects
     * or newaxis, Ellipsis or other arrays or sequences
     * embedded, are considered equivalent to an indexing
     * tuple. (`a[[[1,2], [3,4]]] == a[[1,2], [3,4]]`)
     */

The np.array(2) inside the list causes the list to be treated as if it were a tuple, but the result, a[(1, np.array(2))], is still an advanced indexing operation. It ends up applying the 1 and the 2 to separate axes, unlike a[[1, 2]], and the result ends up looking identical to a[1, 2], but if you try it with a 3D a, it produces a copy instead of a view.