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

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.

---

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.