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From: "Joseph L. Casale" <jcasale@activenetwerx.com>
To: =?windows-1256?Q?Python_=FD=5Bpython-list=40python=2Eorg=5D=FD?= <python-list@python.org>
Subject: Numpy outlier removal
Thread-Topic: Numpy outlier removal
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Date: Sun, 6 Jan 2013 19:44:08 +0000
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Xref: csiph.com comp.lang.python:36279

I have a dataset that consists of a dict with text descriptions and values =
that are integers. If=0A=
required, I collect the values into a list and create a numpy array running=
 it through a simple=0A=
routine:=A0data[abs(data - mean(data)) < m * std(data)] where m is the numb=
er of std deviations=0A=
to include.=0A=
=0A=
=0A=
The problem is I loos track of which were removed so the original display o=
f the dataset is=0A=
misleading when the processed average is returned as it includes the remove=
d key/values.=0A=
=0A=
=0A=
Ayone know how I can maintain the relationship and when I exclude a value, =
remove it from=0A=
the dict?=0A=
=0A=
Thanks!=0A=
jlc=