Building Machine Learning Systems with Python - Third Edition

Comparing the runtime

Let's compare the runtime behavior of NumPy to normal Python lists. In the following code, we will calculate the sum of all squared numbers from 1 to 1,000 and see how much time it will take. We will perform it 10,000 times and report the total time so that our measurement is accurate enough:

import timeitnormal_py_sec = timeit.timeit('sum(x*x for x in range(1000))',                              number=10000)naive_np_sec = timeit.timeit('sum(na*na)',                             setup="import numpy as np; na=np.arange(1000)",                              number=10000)good_np_sec = timeit.timeit('na.dot(na)',                            setup="import numpy as np; na=np.arange(1000)",                             number=10000)print("Normal Python: %f sec" % normal_py_sec) print("Naive NumPy: %f sec" % naive_np_sec) print("Good NumPy: %f sec" % good_np_sec)  ...