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References: <mailman.3697.1345708456.4697.python-list@python.org> <5042082c-5764-4c87-897a-776793753f55@r1g2000pbq.googlegroups.com>
Date: Thu, 23 Aug 2012 23:48:17 -0700 (PDT)
From: Fg Nu <fgnu32@yahoo.com>
Subject: Re: Data cleaning workouts
To: rusi <rustompmody@gmail.com>, "python-list@python.org" <python-list@python.org>
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Xref: csiph.com comp.lang.python:27780

=0A=0AThanks. I will try the=A0SciPy=A0list. It was a bit of a hail mary an=
yway. Pretty sure elevated Python types don't actually get their hands dirt=
y with data. ;)=0A=0A=0A=0A----- Original Message -----=0AFrom: rusi <rusto=
mpmody@gmail.com>=0ATo: python-list@python.org=0ACc: =0ASent: Thursday, Aug=
ust 23, 2012 11:01 PM=0ASubject: Re: Data cleaning workouts=0A=0AOn Aug 23,=
 12:52=A0pm, Fg Nu <fgn...@yahoo.com> wrote:=0A> List folk,=0A>=0A> 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 st=
atistics/analytics/machine learning, particularly using Python.=0A>=0A> Ide=
ally, 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.=0A=0ASince no one has answered, I suggest you narrow your s=
earching from=0A'python' to 'scipy' (or 'numpy').=0AAlso perhaps ipython.=
=0AAnd then perhaps try those specific mailing lists/fora.=0A=0ASince I don=
t know this area much, not saying more.=0A-- =0Ahttp://mail.python.org/mail=
man/listinfo/python-list=0A