Hi, I find this a bit misleading:
a = np.arange(10)
a[np.array(0)] 0
a[np.array([0])] array([0])
a[[0]] array([0])
But, for regular python lists we have:
l = a.tolist()
l[np.array(0)] 0
l[np.array([0])] 0
i.e. indexing with a rank-0 array and a rank-1 array with one single element return the same result, which I find inconsistent with the expected behaviour for this case, i.e.:
l[[0]]
TypeError Traceback (most recent call last) /tmp/tables-2.2/<ipython console> in <module>() TypeError: list indices must be integers, not list The ultimate reason for this behaviour is this:
np.array(0).__index__() 0
np.array([0]).__index__() 0
But I wonder why NumPy needs the latter behaviour, instead of the more logical:
np.array([0]).__index__()
TypeError Traceback (most recent call last) /tmp/tables-2.2/<ipython console> in <module>() TypeError: only rank-0 integer arrays can be converted to an index This inconsistency has indeed introduced a bug in my application and for solving this I'd need something like: """ def is_idx(index): """Check if an object can work as an index or not.""" if hasattr(index, "__index__"): # Only works on Python 2.5 on if (hasattr(index, "shape") and index.shape == (1,)): return False try: # (as per PEP 357) idx = index.__index__() return True except TypeError: return False return False """ i.e. for determining if an object can be an index or not, I need to explicitly check for a shape different from (1,), which is unnecessarily complicated. So I find the current behaviour prone to introduce errors in apps and I'm wondering why exactly np.array([1]) should work as an index at all. It would not be better if that would raise a ``TypeError``? Thanks, -- Francesc Alted