Computational Thinking, Linear Algebra, Linear Regression, Python, Jupyter Notebook.
proc_numpy(x,y): z = np.zeros(nobs, dtype='float32') x = x*2 - ( y * 55 ) y = x + y*2 z = x + y + 99 z = z * ( z - .88 ) return z np.allclose( proc_numpy(x,y), proc_python(x,y), atol=1e-4 ) True %timeit proc_numpy(x,y) # Typed and vectorized 35.9 µs ± 166 ns per loop (mean ± std. dev. of 7 runs,