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pythonnumpyarray-indexing

Using python range objects to index into numpy arrays

I've seen it once or twice before, but I can't seem to find any official docs on it: Using python range objects as indices in numpy.

import numpy as np
a = np.arange(9).reshape(3,3)
a[range(3), range(2,-1,-1)]
# array([2, 4, 6])

Let's trigger an index error just to confirm that ranges are not in the official range (pun intended) of legal indexing methods:

a['x']

# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
# IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

Now, a slight divergence between numpy and its docs is not entirely unheard of and does not necessarily indicate that a feature is not intended (see for example here).

So, does anybody know why this works at all? And if it is an intended feature what are the exact semantics / what is it good for? And are there any ND generalizations?

Solution

  • Just to wrap this up (thanks to @WarrenWeckesser in the comments): This behavior is actually documented. One only has to realize that range objects are python sequences in the strict sense.

    So this is just a case of fancy indexing. Be warned, though, that it is very slow:

    >>> a = np.arange(100000)
>>> timeit(lambda: a[range(100000)], number=1000)
12.969507368048653
>>> timeit(lambda: a[list(range(100000))], number=1000)
7.990526253008284
>>> timeit(lambda: a[np.arange(100000)], number=1000)
0.22483703796751797