time series calculation in list comprehension?

[Raymond Hettinger]

[Peter Otten]
> from __future__ import division
>
> from itertools import islice, tee, izip
 . . .
> def moving_average2(items, n):
>     first_items, last_items = tee(items)
>     accu = sum(islice(last_items, n-1))
>     for first, last in izip(first_items, last_items):
>         accu += last
>         yield accu/n
>         accu -= first
>
> While moving_average1() is even slower than your inefficient variant,
> moving_average2() seems to be a tad faster than the efficient one.

This is nicely done and scales-up well.  Given an n-average of m-items,
it has O(n) memory consumption and O(m) running time.  In contrast, the
other variants do more work than necessary by pulling the whole
sequence into memory or by re-summing all n items at every step,
resulting in O(m) memory consumption and O(m*n) running time.

This recipe gets my vote for the best solution.


Raymond