How to select inverse of indexes of a numpy array?

Question

I have a large set of data in which I need to compare the distances of a set of samples from this array with all the other elements of the array. Below is a very simple exa

Answer 1

You may also use setdiff1d:

In [11]: data[np.setdiff1d(np.arange(data.shape[0]), sample_indexes)]
Out[11]: 
array([[ 0.93825827,  0.26701143],
       [ 0.27309625,  0.38925281],
       [ 0.06510739,  0.58445673],
       [ 0.61469637,  0.05420098],
       [ 0.92685408,  0.62715114],
       [ 0.22587817,  0.56819403],
       [ 0.28400409,  0.21112043]])

Answer 2

mask = np.ones(len(data), np.bool)
mask[sample_indexes] = 0
other_data = data[mask]

not the most elegant for what perhaps should be a single-line statement, but its fairly efficient, and the memory overhead is minimal too.

If memory is your prime concern, np.delete would avoid the creation of the mask, and fancy-indexing creates a copy anyway.

On second thought; np.delete does not modify the existing array, so its pretty much exactly the single line statement you are looking for.

Answer 3

I'm not familiar with the specifics on numpy, but here's a general solution. Suppose you have the following list:
a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
You create another list of indices you don't want:
inds = [1, 3, 6].
Now simply do this:
good_data = [x for x in a if x not in inds], resulting in good_data = [0, 2, 4, 5, 7, 8, 9].

Answer 4

You may want to try in1d

In [5]:

select = np.in1d(range(data.shape[0]), sample_indexes)
In [6]:

print data[select]
[[ 0.99121108  0.35582816]
 [ 0.90154837  0.86254049]
 [ 0.83149103  0.42222948]]
In [7]:

print data[~select]
[[ 0.93825827  0.26701143]
 [ 0.27309625  0.38925281]
 [ 0.06510739  0.58445673]
 [ 0.61469637  0.05420098]
 [ 0.92685408  0.62715114]
 [ 0.22587817  0.56819403]
 [ 0.28400409  0.21112043]]