Path: csiph.com!usenet.pasdenom.info!gegeweb.org!de-l.enfer-du-nord.net!feeder2.enfer-du-nord.net!cs.uu.nl!news.stack.nl!newsfeed.xs4all.nl!newsfeed2.news.xs4all.nl!xs4all!post.news.xs4all.nl!not-for-mail Return-Path: X-Original-To: python-list@python.org Delivered-To: python-list@mail.python.org X-Spam-Status: OK 0.003 X-Spam-Evidence: '*H*': 0.99; '*S*': 0.00; 'float': 0.05; 'that?': 0.05; 'encouraging': 0.07; 'api': 0.09; 'subject:How': 0.09; 'python': 0.09; 'subject:create': 0.09; 'cc:addr:python-list': 0.10; 'subject:python': 0.11; '2.7': 0.13; 'library': 0.15; '100,': 0.16; '10x': 0.16; 'arrays.': 0.16; 'cc:name:python list': 0.16; 'numpy': 0.16; 'subject:array': 0.16; 'subject:random': 0.16; 'wrote:': 0.17; 'fix': 0.17; 'default,': 0.22; 'work,': 0.22; 'cc:2**0': 0.23; 'work.': 0.23; 'random': 0.24; 'specifically': 0.24; 'tried': 0.25; 'cc:addr:python.org': 0.25; 'header:In-Reply-To:1': 0.25; 'creating': 0.26; 'values': 0.26; 'separate': 0.27; 'module.': 0.27; 'message-id:@mail.gmail.com': 0.27; 'arrays': 0.29; 'array': 0.29; 'probably': 0.29; 'this.': 0.29; "i'm": 0.29; 'install': 0.29; "skip:' 10": 0.30; 'generally': 0.32; "skip:' 20": 0.32; 'suggestion': 0.32; 'received:google.com': 0.34; 'fail': 0.35; 'faster': 0.35; 'received:209.85.220': 0.35; 'received:209.85': 0.35; 'but': 0.36; '12,': 0.36; 'test': 0.36; 'does': 0.37; 'why': 0.37; 'received:209': 0.37; 'subject:: ': 0.38; 'easier': 0.38; 'think': 0.40; 'chance': 0.61; 'door': 0.63; 'more': 0.63; 'show': 0.63; '10000': 0.65; 'benefit': 0.70; 'yourself': 0.77; '2013': 0.84; 'oscar': 0.84; 'maarten': 0.91; 'ranging': 0.91; 'beneficial': 0.93 DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=x-received:mime-version:in-reply-to:references:from:date:message-id :subject:to:cc:content-type:content-transfer-encoding; bh=hzO54B64XxZdWJG79ZJxqq/oXJ+ySfTiLdAnayRmeN4=; b=rJeVjauNOfTZWIw7VJwYqdYDHFO8SLDOhrhqfPk0UmJ/VIaC79W9bLWNhbCxqb4FxZ mA2rFlU7UVvpNQTJZAEiAN9I1BoHuiw/4pNH8BZ48uIPrwAAHMr6WeF2vZv6DLU46Xog 5HHc8Uzl7+JOUh6nyNGD1pMpEem+hiKBZ9K08eWb8K0sQNDs734ri/r+2okUpOdRwLq0 MYSWb8F09MUE4jIALquz4IFrDXoX048c36SWTaIqbv+drZj9rtWnVwS/hyHbvHPY1wbj QwArNKkt3WRGmq6UiL7t5c4TQGZNZON+uqM3sJugGhCoYGuq7B4h6p2HCw7INW+hxG8m TcFg== X-Received: by 10.58.116.244 with SMTP id jz20mr7444482veb.27.1363125589681; Tue, 12 Mar 2013 14:59:49 -0700 (PDT) MIME-Version: 1.0 In-Reply-To: References: <0aa38f5a-0e5e-43cd-b6ba-69af6f37e94e@googlegroups.com> From: Oscar Benjamin Date: Tue, 12 Mar 2013 21:59:29 +0000 Subject: Re: How can i create a random array of floats from 0 to 5 in python To: llanitedave Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Cc: Python List X-BeenThere: python-list@python.org X-Mailman-Version: 2.1.15 Precedence: list List-Id: General discussion list for the Python programming language List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Newsgroups: comp.lang.python Message-ID: Lines: 39 NNTP-Posting-Host: 2001:888:2000:d::a6 X-Trace: 1363125597 news.xs4all.nl 6914 [2001:888:2000:d::a6]:53710 X-Complaints-To: abuse@xs4all.nl Xref: csiph.com comp.lang.python:41152 On 12 March 2013 20:21, llanitedave 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 li= ke 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