Path: csiph.com!newsfeed.hal-mli.net!feeder3.hal-mli.net!newsfeed.hal-mli.net!feeder1.hal-mli.net!nntp-feed.chiark.greenend.org.uk!ewrotcd!news.nosignal.org!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 0.003 X-Spam-Evidence: '*H*': 0.99; '*S*': 0.00; 'value,': 0.03; 'modify': 0.05; 'processed': 0.05; 'say,': 0.05; 'dict': 0.09; 'iterate': 0.09; 'keyed': 0.09; 'deletions': 0.16; 'from:addr:mrabarnett.plus.com': 0.16; 'from:addr:python': 0.16; 'from:name:mrab': 0.16; 'include.': 0.16; 'integers.': 0.16; 'keys:': 0.16; 'message-id:@mrabarnett.plus.com': 0.16; 'numpy': 0.16; 'received:84.93': 0.16; 'received:84.93.230': 0.16; 'wrote:': 0.17; 'assuming': 0.22; 'idea': 0.24; 'header:In-Reply- To:1': 0.25; 'header:User-Agent:1': 0.26; 'common': 0.26; 'values': 0.26; 'dictionary': 0.29; 'exclude': 0.29; 'received:192.168.1.3': 0.29; 'array': 0.29; 'returned': 0.30; 'generally': 0.32; 'received:84': 0.32; 'running': 0.32; 'problem': 0.33; 'to:addr:python-list': 0.33; 'skip:d 20': 0.34; 'text': 0.34; 'list': 0.35; 'display': 0.36; 'bad': 0.37; 'data': 0.37; 'subject:: ': 0.38; 'delete': 0.38; 'to:addr:python.org': 0.39; 'received:192': 0.39; 'where': 0.40; 'received:192.168': 0.40; 'your': 0.60; 'relationship': 0.60; 'remove': 0.61; 'to,': 0.65; 'header:Reply-To:1': 0.68; 'reply-to:no real name:2**0': 0.72; 'reply-to:addr:python.org': 0.84; 'subject:removal': 0.84; 'average': 0.93 X-CM-Score: 0.00 X-CNFS-Analysis: v=2.0 cv=HO4d4PRv c=1 sm=1 a=0nF1XD0wxitMEM03M9B4ZQ==:17 a=O2Kvzccb_dQA:10 a=ihvODaAuJD4A:10 a=OUOv7kDek9cA:10 a=8nJEP1OIZ-IA:10 a=EBOSESyhAAAA:8 a=8AHkEIZyAAAA:8 a=9mgJdUYtfwEA:10 a=1g6k1cUcj4sHB4xldzgA:9 a=wPNLvfGTeEIA:10 a=0nF1XD0wxitMEM03M9B4ZQ==:117 X-AUTH: mrabarnett:2500 Date: Sun, 06 Jan 2013 23:18:43 +0000 From: MRAB User-Agent: Mozilla/5.0 (Windows NT 5.1; rv:17.0) Gecko/17.0 Thunderbird/17.0 MIME-Version: 1.0 To: python-list@python.org Subject: Re: Numpy outlier removal References: <50e9fbd5$0$6848$e4fe514c@news2.news.xs4all.nl> In-Reply-To: <50e9fbd5$0$6848$e4fe514c@news2.news.xs4all.nl> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit X-BeenThere: python-list@python.org X-Mailman-Version: 2.1.15 Precedence: list Reply-To: python-list@python.org 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: 36 NNTP-Posting-Host: 2001:888:2000:d::a6 X-Trace: 1357514503 news.xs4all.nl 6977 [2001:888:2000:d::a6]:58819 X-Complaints-To: abuse@xs4all.nl Xref: csiph.com comp.lang.python:36303 On 2013-01-06 22:33, Hans Mulder wrote: > On 6/01/13 20:44:08, Joseph L. Casale wrote: >> I have a dataset that consists of a dict with text descriptions and values that are integers. If >> required, I collect the values into a list and create a numpy array running it through a simple >> routine: data[abs(data - mean(data)) < m * std(data)] where m is the number of std deviations >> to include. >> >> >> The problem is I loos track of which were removed so the original display of the dataset is >> misleading when the processed average is returned as it includes the removed key/values. >> >> >> Ayone know how I can maintain the relationship and when I exclude a value, remove it from >> the dict? > > Assuming your data and the dictionary are keyed by a common set of keys: > > for key in descriptions: > if abs(data[key] - mean(data)) >= m * std(data): > del data[key] > del descriptions[key] > It's generally a bad idea to modify a collection over which you're iterating. It's better to, say, make a list of what you're going to delete and then iterate over that list to make the deletions: deletions = [] for key in in descriptions: if abs(data[key] - mean(data)) >= m * std(data): deletions.append(key) for key in deletions: del data[key] del descriptions[key]