Numpy is optimised for large amounts of data. Give it a tiny 3 length array and, unsurprisingly, it performs poorly.

Consider a separate test

```
import timeit
reps = 100
pythonTest = timeit.Timer('a = [0.] * 1000000')
numpyTest = timeit.Timer('a = numpy.zeros(1000000)', setup='import numpy')
uninitialised = timeit.Timer('a = numpy.empty(1000000)', setup='import numpy')
# empty simply allocates the memory. Thus the initial contents of the array
# is random noise
print 'python list:', pythonTest.timeit(reps), 'seconds'
print 'numpy array:', numpyTest.timeit(reps), 'seconds'
print 'uninitialised array:', uninitialised.timeit(reps), 'seconds'
```

And the output is

```
python list: 1.22042918205 seconds
numpy array: 1.05412316322 seconds
uninitialised array: 0.0016028881073 seconds
```

It would seem that it is the zeroing of the array that is taking all the time for numpy. So unless you need the array to be initialised then try using empty.