This is one of the 100 recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python.

# 5.2. Accelerating array computations with Numexpr¶

Let's import NumPy and Numexpr.

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import numpy as np
import numexpr as ne


We generate three large vectors.

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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.

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%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.

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%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.

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ne.ncores

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for i in range(1, 5):