Fastest way to calculate exponential [exp()] function of large complex array in Python

We could leverage numexpr module, which works really efficiently on large data involving transcendental operations -

In [91]: arr = np.random.rand(2**14) + 1j *np.random.rand(2**14)
    ...: float_value = 0.5
    ...: 

In [92]: %timeit np.exp(float_value * arr)
1000 loops, best of 3: 739 µs per loop

In [94]: import numexpr as ne

In [95]: %timeit ne.evaluate('exp(float_value*arr)')
1000 loops, best of 3: 241 µs per loop

This seems to be coherent with the expected performance as stated in the docs.