In Introduction 2.1 Simple Example - most benefit is not from vectorization

In chapter "2.1 Simple example" you have example of "Vectorized approach" which leaves impression that most performance benefits come from itertools.accumulate(). This is not true - the main speed gain comes from use of random.choices() instead of random.randint() in previous sample.

>>> import random
>>> from timeit import timeit

>>> # original example
... def random_walk(n):
...     position = 0
...     walk = [position]
...     for i in range(n):
...         position += 2*random.randint(0, 1)-1
...         walk.append(position)
...     return walk
...
>>>
>>> timeit("random_walk(n=10000)", number=100, globals=globals())
1.7758303454734055
>>> # lets use random.choices() instead
... def random_walk_with_choices(n):
...     position = 0
...     steps = random.choices([1,-1], k=n)
...     walk = [position]
...     for step in steps:
...         position += step
...         walk.append(position)
...     return walk
...
>>> timeit("random_walk_with_choices(n=10000)", number=100, globals=globals())
0.39364298073223836
>>>
>>> # original random_walk_faster with accumulate
... def random_walk_faster(n=1000):
...   from itertools import accumulate
...   steps = random.choices([1,-1], k=n)
...   return list(accumulate(steps))
...
>>> timeit("random_walk_faster(n=10000)", number=100, globals=globals())
0.264255993680635

It is clearly visible that accumulate has minor effect on overall speed (which is mainly driven by itertools() native implementation).

As such example is clearly misleading.

Another minor bug with example: random_walk() returns N+1 elements while random_walk_faster() returns N elements.

len(random_walk(1000)) 1001 len(random_walk_faster(1000)) 1000

rougier / from-python-to-numpy

In Introduction 2.1 Simple Example - most benefit is not from vectorization #40