Are NumPy's math functions faster than Python's?

Question

I have a function defined by a combination of basic math functions (abs, cosh, sinh, exp, ...).

I was wondering if it makes a difference (in speed) to use, for examp

Answer 1

In fact, on numpy array

built in abs calls numpy's implementation via __abs__, see Why built-in functions like abs works on numpy array?

So, in theory there shouldn't be much performance difference.

import timeit

x = np.random.standard_normal(10000)

def pure_abs():
    return abs(x)

def numpy_abs():
    return np.absolute(x)

n = 10000

t1 = timeit.timeit(pure_abs, number = n)
print 'Pure Python abs:', t1
t2 = timeit.timeit(numpy_abs, number = n)
print 'Numpy abs:', t2
Pure Python abs: 0.435754060745
Numpy abs: 0.426516056061