Number of loops in Numpy Element wise operation

Question

I am taking element wise power of a numpy array as well as a python list. Why are there 10000 loops for the numpy operation?

In [1]: a = np.arange(1000)

In [2]: %timeit a**5
10000 loops, best of 3: 77.8 µs per loop

In [3]: b = range(1000)

In [4]: %timeit [i**5 for i in b]
1000 loops, best of 3: 1.64 ms per loop

Answer 1

From the documentation ( https://docs.python.org/2/library/timeit.html#command-line-interface ):

If -n is not given, a suitable number of loops is calculated by trying successive powers of 10 until the total time is at least 0.2 seconds.

In other words, timeit runs your statement 10000 times because that's about how many it can do in .2 seconds. It has nothing to do with the number you passed to arange .