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

Solution 1:

Not a proper answer, but too long for comment.

In fact, it seems to work with about any indexable object:

import numpy as np

classMyIndex:
    def__init__(self, n):
        self.n = n
    def__getitem__(self, i):
        if i < 0or i >= self.n:
            raise IndexError
        return i
    def__len__(self):
        return self.n

a = np.array([1, 2, 3])
print(a[MyIndex(2)])
# [1 2]

I think the relevant lines in NumPy's code are below this comment in core/src/multiarray/mapping.c:

/*
 * Some other type of short sequence - assume we should unpack it like a
 * tuple, and then decide whether that was actually necessary.
 */

But I'm not entirely sure. For some reason, this hangs if you remove the if i < 0 or i >= self.n: raise IndexError, even though there is a __len__, so at some point it seems to be iterating through the given object until IndexError is raised.

Solution 2:

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