Numba and Cython aren't improving the performance compared to CPython significantly, maybe I am using it incorrectly?
Here's what I think is happening with Numba:
Numba works on Numpy arrays. Nothing else. Everything else has nothing to do with Numba.
zip returns an iterator of arbitrary items, which Numba cannot see into. Thus Numba cannot do much compiling.
Looping over the indexes with a for i in range(...) is likely to produce a much better result and allow much stronger type inference.
Using the builtin sum() could be causing problems.
Here's linear regression code that will run faster in Numba:
@numba.jitdef ols(x, y): """Simple OLS for two data sets.""" M = x.size x_sum = 0. y_sum = 0. x_sq_sum = 0. x_y_sum = 0. for i in range(M): x_sum += x[i] y_sum += y[i] x_sq_sum += x[i] ** 2 x_y_sum += x[i] * y[i] slope = (M * x_y_sum - x_sum * y_sum) / (M * x_sq_sum - x_sum**2) intercept = (y_sum - slope * x_sum) / M return slope, intercept