NOTICE: This package has been removed from pkgsrc

./wip/py-pandas, Pythonic cross-section, time series, and statistical analysis

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Branch: CURRENT, Version: 0.9.0, Package name: py27-pandas-0.9.0, Maintainer: jihbed.research

pandas provides NumPy-based data structures and statistical tools for
common time series and cross-sectional data sets. It is intended to
accomplish the following:

* Simplify working with possibly labeled 1, 2, and 3 dimensional
heterogeneous data sets commonly found in statistics, finance, and
econometrics.

* Provide IO utilities for getting data in and out of pandas

* Implement common statistical models with a convenient interface,
handling missing data and other common problems associated with
messy statistical data sets


Required to run:
[time/py-dateutil] [math/py-numpy]

Master sites:

SHA1: 1e6f153556ef88578a0ada4ce26d8db8d3285c79
RMD160: 1f6ad317d19107a96192a0384292969db0a3dee9
Filesize: 2254.023 KB

Version history: (Expand)

CVS history: (Expand)


   2014-05-04 16:17:49 by othyro | Files touched by this commit (300) | Package removed
Log message:
Removing old/duplicate/archive copies of packages in HEAD.
   2014-01-16 11:40:56 by Thomas Klausner | Files touched by this commit (8) 
Log message:
Convert to use versioned_dependencies.mk.
   2012-11-17 21:15:25 by Sergey Litvinov | Files touched by this commit (3) 
Log message:
Update to version 0.9.0
   2012-10-07 15:57:25 by Aleksej Saushev | Files touched by this commit (211) 
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
   2012-01-18 13:10:11 by ndb | Files touched by this commit (1) 
Log message:
corrected distinfo.
   2011-03-07 20:53:09 by Kamel Derouiche | Files touched by this commit (2) | Package updated
Log message:
update pandans move to 0.3.0
   2010-06-13 01:28:58 by Kamel Derouiche | Files touched by this commit (4) | Imported package
Log message:
Import py26-pandas-0.2 as wip/py-pandas.

pandas provides NumPy-based data structures and statistical tools for
common time series and cross-sectional data sets. It is intended to
accomplish the following:

* Simplify working with possibly labeled 1, 2, and 3 dimensional
  heterogeneous data sets commonly found in statistics, finance, and
  econometrics.

* Provide IO utilities for getting data in and out of pandas

* Implement common statistical models with a convenient interface,
  handling missing data and other common problems associated with
  messy statistical data sets