Implement fast Cython Series iterator, for speeding up DataFrame.apply

Having tons of calls to Series.__new__ seriously degrades performance because most of the logic isn’t necessary. Could play tricks in Cython with the data pointers to avoid this.

[wesm]

OK I made some further tweaks and things so apply actually beats apply_along_axis quite a bit in the axis=1 case with your example (most of the time is spent calling unique in axis=0 case):

In [6]: timeit data.apply(fn, axis=1, raw=True)
1 loops, best of 3: 288 ms per loop

In [7]: timeit data.apply(fn, axis=0, raw=True)
10 loops, best of 3: 82 ms per loop

In [8]: timeit np.apply_along_axis(fn, 1, data.values)
1 loops, best of 3: 518 ms per loop

In [9]: timeit np.apply_along_axis(fn, 0, data.values)
10 loops, best of 3: 82.7 ms per loop

[natekupp]

Thanks Wes!