Path: csiph.com!usenet.pasdenom.info!gegeweb.org!de-l.enfer-du-nord.net!feeder1.enfer-du-nord.net!newsfeed.eweka.nl!eweka.nl!feeder3.eweka.nl!newsfeed.xs4all.nl!newsfeed6.news.xs4all.nl!xs4all!newsgate.cistron.nl!newsgate.news.xs4all.nl!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 0.089 X-Spam-Evidence: '*H*': 0.83; '*S*': 0.01; 'python.': 0.02; 'newbie': 0.05; 'python': 0.09; 'ideally,': 0.16; 'received:98.138.89.195': 0.16; 'to:name:python-list@python.org': 0.20; 'trying': 0.21; 'form:': 0.22; 'wondering': 0.26; 'raw': 0.27; 'knows': 0.30; 'cleaning': 0.33; 'anyone': 0.33; 'to:addr:python-list': 0.33; 'reply-to:addr:yahoo.com': 0.34; 'list': 0.35; 'should': 0.36; 'charset:us-ascii': 0.36; 'resources': 0.37; 'data': 0.37; 'some': 0.38; 'to:addr:python.org': 0.39; 'here': 0.65; 'management': 0.65; 'subject:Data': 0.65; 'header:Reply-To:1': 0.68; 'teach': 0.69; 'analysis': 0.70; 'special': 0.73; 'basically,': 0.84; 'horrible': 0.84 X-Yahoo-Newman-Property: ymail-3 X-Yahoo-Newman-Id: 507181.20618.bm@omp1053.mail.ne1.yahoo.com DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=yahoo.com; s=s1024; t=1345708333; bh=5Vs0D4ZJWgEtMvwyc58TjjZJDd18xyL0FON27h8Ub0k=; h=X-YMail-OSG:Received:X-Mailer:Message-ID:Date:From:Reply-To:Subject:To:MIME-Version:Content-Type; b=RBIr3fMaN1D+3Jj0FmY5y7c+Xvbi8S09Udc0lADmOiAASstuVmEbYhXY4B89PdaS1fmbfLokwCbk67qfzY7dFyW6Rn9+wL979KiaTAugfz5duSpvgk8xeLJQqrdRaneuW3LclIYDsEqY6mmj0ocsGWQQqjRpN1TIX3YbsD0c2Oc= DomainKey-Signature: a=rsa-sha1; q=dns; c=nofws; s=s1024; d=yahoo.com; h=X-YMail-OSG:Received:X-Mailer:Message-ID:Date:From:Reply-To:Subject:To:MIME-Version:Content-Type; b=UFqlCWU0oXt8olnZWLzjx+PLqLjr1E1nc7vW0wlWA3IebO77QD8/KtbQR4UTZwv7aHYyeLjJo0Ov4K0trobiswh68MZK7/jmNagHreNAA77Y3JvP0bUGQUxHto6hV8dH97vsf3awgoTObLM6WjPN2n1GB0Yv7yG2295sqZ6l61c=; X-YMail-OSG: YuaX72kVM1kocweIOzd1LO4b0dqVnZVk6Q4mqAqzs0.EYTY 0aP8di8p9yQkIqr_JZJsplH5fCg1Db4_NBL4.QSQSUqw2vnDVzG8C0O95tnl sHNAI5JU08rXKGi7y7gMtJ3cnJAXFrnMNyA0H.AsSgeFpDMB68h0D..2YkxY QxmszGEaNIX34zMubx9wMz0RLJSY0J7QpBt_za08Ouxj_gPxCvFAP75l09T5 T_Ltsms91_iRZr_CgnLxKw2bIN9zKZTbOMCE1mtSpnydJ7m33J7boePRbuvQ Xde6IwC46fpN0Bgsr50cdSefFnZXKrRN0YXv__MxlYGLryeJveqZ.28c7.dm BZROjEOhZBm4sfGp3mmg7FUJ7h_9ILKCeiYZUc_duxz_epTHY.seT8aOLtlL aLmi20u9dduFEbQ-- X-Mailer: YahooMailWebService/0.8.121.416 Date: Thu, 23 Aug 2012 00:52:13 -0700 (PDT) From: Fg Nu Subject: Data cleaning workouts To: "python-list@python.org" MIME-Version: 1.0 Content-Type: text/plain; charset=us-ascii X-BeenThere: python-list@python.org X-Mailman-Version: 2.1.12 Precedence: list Reply-To: Fg Nu 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: 5 NNTP-Posting-Host: 2001:888:2000:d::a6 X-Trace: 1345708456 news.xs4all.nl 6855 [2001:888:2000:d::a6]:37682 X-Complaints-To: abuse@xs4all.nl Xref: csiph.com comp.lang.python:27703 List folk, I am a newbie trying to get used to Python. I was wondering if anyone knows of web resources that teach good practices in data cleaning and management for statistics/analytics/machine learning, particularly using Python. Ideally, these would be exercises of the form: here is some horrible raw data --> here is what it should look like after it has been cleaned. Guidelines about steps that should always be taken, practices that should be avoided; basically, workflow of data analysis in Python with special emphasis on the cleaning part.