On Fri, 19 Dec 2014 10:32:15 +0100 Peter Otten <__pete...@web.de> wrote:
> Basically > > from random import randint, seed > > is equivalent to > > import random > randint = random.randint > seed = random.seed > del random > > From that you can deduce that the whole random module is loaded into memory > in both cases. A small speed advantage may be caused when the attribute > lookup is avoided in a tight loop Thanks for the clarification, that's really helpful. So I guess the import style is more about name space management than controlling the details of what gets loaded... > or -- if you go back to the original problem -- with some effort: > > >>> N = 3 > >>> numpy.random.randint(1, 10, N) + numpy.arange(0, N*10, 10) > array([ 5, 11, 27]) > > In return the latter is likely significantly more efficient for large N than > the generic list comprehension. Ha! That's fun. Seven seems to be a magic number in the original problem, so maybe a little tweak: import numpy as np N=7 (np.random.randint(1,N,N) + np.arange(0,N*N,N)).tolist() _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: https://mail.python.org/mailman/listinfo/tutor
