X-Received: by 10.66.141.13 with SMTP id rk13mr10790587pab.16.1434363167220; Mon, 15 Jun 2015 03:12:47 -0700 (PDT) X-Received: by 10.140.99.44 with SMTP id p41mr355956qge.3.1434363166918; Mon, 15 Jun 2015 03:12:46 -0700 (PDT) Path: csiph.com!v102.xanadu-bbs.net!xanadu-bbs.net!news.glorb.com!h15no3396984igd.0!news-out.google.com!4ni1694qgh.1!nntp.google.com!q107no1299920qgd.0!postnews.google.com!glegroupsg2000goo.googlegroups.com!not-for-mail Newsgroups: comp.lang.python Date: Mon, 15 Jun 2015 03:12:46 -0700 (PDT) Complaints-To: groups-abuse@google.com Injection-Info: glegroupsg2000goo.googlegroups.com; posting-host=88.107.162.82; posting-account=p2oMkQoAAADOwikrj6Ymrd9-XSQEUxmF NNTP-Posting-Host: 88.107.162.82 User-Agent: G2/1.0 MIME-Version: 1.0 Message-ID: Subject: python financial data cleaning From: Sebastian M Cheung Injection-Date: Mon, 15 Jun 2015 10:12:46 +0000 Content-Type: text/plain; charset=ISO-8859-1 Xref: csiph.com comp.lang.python:92623 How to do financial data cleaning ? Say I assume a list of 1000 finance series data in myList = Open, High, Low and Close. For missing Close Price data, What is best practice to clean data in Python