On Tuesday, March 12, 2013 2:59:29 PM UTC-7, Oscar Benjamin wrote: > On 12 March 2013 20:21, llanitedave <llanited...@veawb.coop> wrote: > > > On Tuesday, March 12, 2013 10:47:25 AM UTC-7, Maarten wrote: > > >> On Tuesday, March 12, 2013 6:11:10 PM UTC+1, Norah Jones wrote: > > >> > > >> > I want to create a random float array of size 100, with the values in > >> > the array ranging from 0 to 5. I have tried random.sample(range(5),100) > >> > but that does not work. How can i get what i want to achieve? > > >> > > >> Use numpy > > [SNIP] > > > > > > While numpy would work, I fail to see how encouraging the op to download > > and install a separate library and learn a whole new set of tools would be > > beneficial by default, without knowing the purpose of the need. This is > > like recommending an RPG to fix a sticky door hinge. > > > > This suggestion comes after others that show how to use the stdlib's > > random module. I don't think it's unreasonable to recommend numpy for > > this. If you want to create *arrays* of random numbers then why not > > use a library that provides an API specifically for that? You can test > > yourself to see that numpy is 10x faster for large arrays: > > > > Python 2.7 on Linux: > > $ python -m timeit -s 'import random' -- '[random.uniform(0, 5) for x > > in range(1000)]' > > 1000 loops, best of 3: 729 usec per loop > > $ python -m timeit -s 'import random' -- '[random.random() * 5 for x > > in range(1000)]' > > 1000 loops, best of 3: 296 usec per loop > > $ python -m timeit -s 'import numpy' -- 'numpy.random.uniform(0, 5, 1000)' > > 10000 loops, best of 3: 32.2 usec per loop > > > > I would use numpy for this mainly because if I'm creating arrays of > > random numbers I probably want to use them in ways that are easier > > with numpy arrays. There's also a chance the OP might benefit more > > generally from using numpy depending on what they're working on. > > > > > > Oscar
I don't think numpy is unreasonable for you or me. I just started learning it recently, and I'm pretty jazzed about its possibilities. I obtained an app for work that uses it, and now it's up to me to maintain it, so learning it is a good idea for me regardless. Now I'm starting to fantasize about other things I could do with it. But the OP appears like a pretty basic beginner, and I really think that for such a entry-level knowledge scale, we should stick to the standard library until they're ready to take on more sophisticated tasks. "Premature Optimization" is the analogy that comes to mind. -- http://mail.python.org/mailman/listinfo/python-list
