On Sep 13, 9:27 pm, Hrvoje Niksic <[EMAIL PROTECTED]> wrote:
> Note that, despite appearances, it's not as built-in as one might
> wish. sum(seq) is still completely generic and works on all
> number-like objects (in fact on all objects that define an __add__
> operation except strings, which are
Terry Reedy <[EMAIL PROTECTED]> writes:
>> Of course, sum() is even faster than reduce:
>>
> Timer('sum(xrange(1))').repeat(number=1)
>> [9.814924955368042, 8.7169640064239502, 9.5062401294708252]
>
> 'Of course', because the irreducible difference between
> reduce(add.seq) and sum(seq
Steven D'Aprano wrote:
If you want to see reduce really shine, time it with a C-based function
rather than one written in pure Python:
Timer('reduce(add, xrange(1))',
... 'from operator import add').repeat(number=1)
[19.724750995635986, 19.410486936569214, 19.614511013031006]
Timer
On Sat, 13 Sep 2008 01:06:22 -0700, cnb wrote:
> This must be because of implementation right? Shouldn't reduce be faster
> since it iterates once over the list? doesnt sum first construct the
> list then sum it?
What makes you think that?
Given the speed of sum(), it sure doesn't look like it's
On Sep 13, 10:06 am, cnb <[EMAIL PROTECTED]> wrote:
> This must be because of implementation right? Shouldn't reduce be
> faster since it iterates once over the list?
> doesnt sum first construct the list then sum it?
No, sum also iterates the sequence just once and doesn't create a
list. It is pr
> doesnt sum first construct the list then sum it?
> def com(lst):
> return sum(x for x in lst)
You construct a generator over an existing list in your code.
Try sum([x for x in lst]) to see the effect of additional list
construction. And while you're at it, try the simple sum(lst).
Cheers,
On Sat, 13 Sep 2008 01:06:22 -0700, cnb wrote:
> This must be because of implementation right? Shouldn't reduce be faster
> since it iterates once over the list? doesnt sum first construct the
> list then sum it?
No it doesn't. Why should it?
> also, using range instead of xrange doesnt seem to
