sorting numpy structured and record arrays is very slow

Question

it looks like sorting numpy structured and record arrays by a single column is much slower than doing a sort on a similar standalone array:

In [111]: a = np         


        

Answer 1

As Jaime have said, you can use argsort to sort the record array.

inds = np.argsort(rec['f0'])

And use take to avoid making a copy

np.take(rec, inds, out=rec)

Answer 2

What´s slowing you is the use of order, not the fact that you have a record array. If you want to sort by a single field, do it like this:

In [12]: %timeit np.argsort(rec['f0'])
1000 loops, best of 3: 829 us per loop

Once order is used, performance goes south no matter how many fields you want to sort by:

In [16]: %timeit np.argsort(rec, order=['f0'])
10 loops, best of 3: 27.9 ms per loop

In [17]: %timeit np.argsort(rec, order=['f0', 'f1'])
10 loops, best of 3: 28.4 ms per loop