On 10/28/2011 8:49 PM, Michael McGlothlin wrote:
Better to think of a sequence of values, whether materialized as a 'list' or
not.
The final value will actually be a string but it seems it is usually
faster to join a list of strings than to concat them one by one.
.join() takes an iterable of strings as its argument
Comprehensions combine map and filter, both of which conceptually work on
each item of a pre-existing list independently. (I am aware that the
function passed can stash away values to create dependence.
The problem is I don't really have a pre-existing list.
So, as I said, map, filter, and comprehensions are not applicable to
your problem.
def do(func, N, value):
yield value
for i in range(1,N):
value = func(value)
yield value
For more generality, make func a function of both value and i.
If you need a list, "l = list(do(f,N,x))", but if you do not, you can do
"for item in do(f,N,x):" and skip making the list.
Generators seem considerably slower than using comprehension or
map/filter
So what? A saw may cut faster than a hammer builds, but I hope you don't
grab a saw when you need a hammer.
> /reduce.
Do you actually have an example where ''.join(reduce(update, N, start))
is faster than ''.join(update_gen(N, start))? Resuming a generator n
times should be *faster* than n calls to the update function of reduce
(which will actually have to build a list).
---
Terry Jan Reedy
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