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| References | <mailman.3697.1345708456.4697.python-list@python.org> <5042082c-5764-4c87-897a-776793753f55@r1g2000pbq.googlegroups.com> |
|---|---|
| Date | 2012-08-23 23:48 -0700 |
| From | Fg Nu <fgnu32@yahoo.com> |
| Subject | Re: Data cleaning workouts |
| Newsgroups | comp.lang.python |
| Message-ID | <mailman.3741.1345791030.4697.python-list@python.org> (permalink) |
Thanks. I will try the SciPy list. It was a bit of a hail mary anyway. Pretty sure elevated Python types don't actually get their hands dirty with data. ;) ----- Original Message ----- From: rusi <rustompmody@gmail.com> To: python-list@python.org Cc: Sent: Thursday, August 23, 2012 11:01 PM Subject: Re: Data cleaning workouts On Aug 23, 12:52 pm, Fg Nu <fgn...@yahoo.com> wrote: > 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. Since no one has answered, I suggest you narrow your searching from 'python' to 'scipy' (or 'numpy'). Also perhaps ipython. And then perhaps try those specific mailing lists/fora. Since I dont know this area much, not saying more. -- http://mail.python.org/mailman/listinfo/python-list
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Data cleaning workouts Fg Nu <fgnu32@yahoo.com> - 2012-08-23 00:52 -0700
Re: Data cleaning workouts rusi <rustompmody@gmail.com> - 2012-08-23 23:01 -0700
Re: Data cleaning workouts Fg Nu <fgnu32@yahoo.com> - 2012-08-23 23:48 -0700
Re: Data cleaning workouts Mark Lawrence <breamoreboy@yahoo.co.uk> - 2012-08-24 09:16 +0100
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