[Solved] Why numpy arrays are slower than lists with for loops?

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np.arrays are much faster when using vectorized functions or to say it another way, specifically when you are not iterating over the elements.

Here is an example. We will add 1 to each number in the ordered list you created.

The setup is similar except I use lists instead of ranges so I can operate on the values.

length = 150000000
my_list = list(range(length))
my_array = np.array(my_list)

Using the %%timeit magic function in jupyter notebooks

%%timeit
for i in range(len(my_list)):
    my_list[i] += 1

gives

38.6 s ± 3.71 s per loop (mean ± std. dev. of 7 runs, 1 loop each)

thats about 38 seconds per loop

On the other hand

%%timeit
new_array = my_array + 1

gives

772 ms ± 58.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

The operation on the numpy array took under one second (~772ms) per loop while iterating through the list and adding one to each element too around 36 seconds per loop.

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solved Why numpy arrays are slower than lists with for loops?