This is one of the 100 recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python.
Let's import NumPy and Numexpr.
import numpy as np import numexpr as ne
We generate three large vectors.
x, y, z = np.random.rand(3, 1000000)
Now, we evaluate the time taken by NumPy to calculate a complex algebraic expression involving our vectors.
%timeit x + (y**2 + (z*x + 1)*3)
And now, the same calculation performed by Numexpr. We need to give the formula as a string as Numexpr will parse it and compile it.
%timeit ne.evaluate('x + (y**2 + (z*x + 1)*3)')
Numexpr also makes use of multicore processors. Here, we have 4 physical cores and 8 virtual threads with hyperthreading. We can specify how many cores we want numexpr to use.
for i in range(1, 5): ne.set_num_threads(i) %timeit ne.evaluate('x + (y**2 + (z*x + 1)*3)')