Question

A simply example of numpy indexing:

In: a = numpy.arange(10)
In: sel_id = numpy.arange(5)
In: a[sel_id]
Out: array([0,1,2,3,4])

How do I return the rest of the array that are not indexed by sel_id? What I can think of is:

In: numpy.array([x for x in a if x not in a[id]])
out: array([5,6,7,8,9])

Is there any easier way?

Answer 1

For this simple 1D case, I’d actually use a boolean mask:

a = numpy.arange(10)
include_index = numpy.arange(4)
include_idx = set(include_index)  #Set is more efficient, but doesn't reorder your elements if that is desireable
mask = numpy.array([(i in include_idx) for i in xrange(len(a))])

Now you can get your values:

included = a[mask]  # array([0, 1, 2, 3])
excluded = a[~mask] # array([4, 5, 6, 7, 8, 9])

Note that a[mask] doesn’t necessarily yield the same thing as a[include_index] since the order of include_index matters for the output in that scenario (it should be roughly equivalent to a[sorted(include_index)]). However, since the order of your excluded items isn’t well defined, this should work Ok.

---

EDIT

A better way to create the mask is:

mask = np.zeros(a.shape,dtype=bool)
mask[include_idx] = True

(thanks to seberg).