On 3 June 2013 08:33, David Cournapeau cournape@gmail.com wrote:

(around 50 on my own machine, but that's platform specific).

In my machine, it is around 10. You also have to be aware of the data container: it is not the same to iterate over lists than over arrays.

In [6]: a = np.arange(50)

In [7]: %timeit np.sin(a) 100000 loops, best of 3: 3.95 us per loop

In [8]: %timeit np.array([math.sin(i) for i in a]) # We are working with arrays, we keep the array output. 10000 loops, best of 3: 32.9 us per loop

In [9]: %timeit [math.sin(i) for i in a] # Keep the list output. 10000 loops, best of 3: 21.3 us per loop

But, if you stick to lists:

In [12]: b = range(50)

In [13]: %timeit [math.sin(i) for i in b] 100000 loops, best of 3: 12.4 us per loop

In any case, switching between iterators and numpy functions is, in my opinion, error prone. I would use math only for scalars, and numpy for the rest, except if optimisation is needed (as a last step). For the record, another interesting example:

In [18]: a = np.arange(10)

In [19]: b = range(10)

In [20]: %timeit a + b ## Mixed container 100000 loops, best of 3: 10.4 us per loop

In [21]: %timeit [i + j for i, j in zip(a, b)] 100000 loops, best of 3: 12 us per loop

In [22]: %timeit a + a ## Pure array 1000000 loops, best of 3: 1.23 us per loop

In [23]: %timeit [i + j for i, j in zip(a, a)] 100000 loops, best of 3: 7.21 us per loop

In [24]: %timeit b + b ## Pure list 1000000 loops, best of 3: 197 ns per loop ## OOOOPS

In [25]: %timeit [i + j for i, j in zip(b, b)] 100000 loops, best of 3: 1.68 us per loop

Here, b + b is just attaching the lists, not adding them. In a program, that may be an error due to forgotten conversion.

Out of curiosity, VPython, a visual package oriented to beginners, has its own functions that redirect to math if applied on scalars and to numpy otherwise.

David.