Path: csiph.com!v102.xanadu-bbs.net!xanadu-bbs.net!nntp.club.cc.cmu.edu!micro-heart-of-gold.mit.edu!bloom-beacon.mit.edu!bloom-beacon.mit.edu!panix!roy From: Roy Smith Newsgroups: comp.lang.python Subject: pyflakes best practices? Date: Thu, 29 May 2014 20:13:41 -0400 Organization: PANIX Public Access Internet and UNIX, NYC Lines: 10 Message-ID: NNTP-Posting-Host: localhost X-Trace: reader1.panix.com 1401408821 20118 127.0.0.1 (30 May 2014 00:13:41 GMT) X-Complaints-To: abuse@panix.com NNTP-Posting-Date: Fri, 30 May 2014 00:13:41 +0000 (UTC) User-Agent: MT-NewsWatcher/3.5.3b3 (Intel Mac OS X) Xref: csiph.com comp.lang.python:72263 We've recently started using pyflakes. The results seem to be similar to most tools of this genre. It found a few real problems. It generated a lot of noise about things which weren't really wrong, but were easy to fix (mostly, unused imports), and a few plain old false positives which have no easy "fix" (in the sense of, things I can change which will make pyflakes STFU). So, what's the best practice here? How do people deal with the false positives? Is there some way to annotate the source code to tell pyflakes to ignore something?