Python's sum vs. NumPy's numpy.sum

Question

What are the differences in performance and behavior between using Python\'s native sum function and NumPy\'s numpy.sum? sum works on

Answer 1

This is an extension to the the answer post above by Akavall. From that answer you can see that np.sum performs faster for np.array objects, whereas sum performs faster for list objects. To expand upon that:

On running np.sum for an np.array object Vs. sum for a list object, it seems that they perform neck to neck.

# I'm running IPython

In [1]: x = range(1000) # list object

In [2]: y = np.array(x) # np.array object

In [3]: %timeit sum(x)
100000 loops, best of 3: 14.1 µs per loop

In [4]: %timeit np.sum(y)
100000 loops, best of 3: 14.3 µs per loop

Above, sum is a tiny bit faster than np.array, although, at times I've seen np.sum timings to be 14.1 µs, too. But mostly, it's 14.3 µs.