Multiple Slice In List Indexing For Numpy Array

Numpy array admits a list of indices, for example a = np.arange(1000) l = list([1,44,66,33,90,345]) a[l] = 22 But this method don't work if we want to use a multiple slice indexin

Solution 1:

What comes to my mind:

a = np.arange(1000)
l = np.hstack(([1, 44, 66, 33, 90], np.arange(200, 300), np.arange(500, 600)))
a[l] = 22

I'm not sure if it's the simplest way, but it works.

Edit: you're right that this is slower than using slices; but you cannot create a slice object with arbitrary values. Maybe you should just do several assignments then:

%timeit a[np.hstack(([1, 44, 66, 33, 90], np.arange(200, 300), np.arange(500, 600)))] = 2210000 loops, best of 3: 39.5 us per loop

%timeit a[[1, 44, 66, 33, 90]] = 22; a[200:300] = 22; a[500:600] = 22100000 loops, best of 3: 18.4 us per loop

Solution 2:

You can use fancy indexing to build an index list.

l = numpy.array([1,44,66,33,90]+range(200,300)+range(500,600))
a[l] = 22

But as @Lev pointed out, this may not be any faster (though it almost certainly will be if you can precompute the index list).

However, fancy indexing applies per-axis. So you can fancy index on one axis, and slice the others, if that helps at all:

a = numpy.random.randn(4, 5, 6)
l = numpy.array([1, 2])
a[l, slice(None), slice(2, 4)] = 10