I just discovered — by chance — that an array in numpy may be indexed by an empty tuple:
In [62]: a = arange(5)
In [63]: a[()]
Out[63]: array([0, 1, 2, 3, 4])
I found some documentation on the numpy wiki ZeroRankArray:
(Sasha) First, whatever choice is made for x[...] and x[()] they should be the same because ... is just syntactic sugar for "as many : as necessary", which in the case of zero rank leads to ... = (:,)*0 = (). Second, rank zero arrays and numpy scalar types are interchangeable within numpy, but numpy scalars can be use in some python constructs where ndarrays can't.
So, for 0-d arrays a[()] and a[...] are supposed to be equivalent. Are they for higher-dimensional arrays, too? They strongly appear to be:
In [65]: a = arange(25).reshape(5, 5)
In [66]: a[()] is a[...]
Out[66]: False
In [67]: (a[()] == a[...]).all()
Out[67]: True
In [68]: a = arange(3**7).reshape((3,)*7)
In [69]: (a[()] == a[...]).all()
Out[69]: True
But, it is not syntactic sugar. Not for a high-dimensional array, and not even for a 0-d array:
In [76]: a[()] is a
Out[76]: False
In [77]: a[...] is a
Out[77]: True
In [79]: b = array(0)
In [80]: b[()] is b
Out[80]: False
In [81]: b[...] is b
Out[81]: True
And then there is the case of indexing by an empty list, which does something else altogether, but appears equivalent to indexing with an empty ndarray:
In [78]: a[[]]
Out[78]: array([], shape=(0, 3, 3, 3, 3, 3, 3), dtype=int64)
In [86]: a[arange(0)]
Out[86]: array([], shape=(0, 3, 3, 3, 3, 3, 3), dtype=int64)
In [82]: b[[]]
---------------------------------------------------------------------------
IndexError Traceback