ANN: pandas 0.8.0 released

hi all,

(apologies for the cross-post)

I'm very pleased to announce the pandas 0.8.0 release. This is a
massive release introducing, among other things, a substantial
overhaul of pandas's time series processing with substantially
increased performance, decreased memory usage, and dozens of new
features. It also incorporates portions of the inactive
scikits.timeseries codebase, so scikits.timeseries users will be
able to migrate. Since pandas now utilizes NumPy's datetime64
dtype, users will need to use NumPy 1.6 or higher from now on.

New time series features include:

- High performance resampling: upsampling and downsampling
- Nanosecond-level Timestamp support
- Frequency inference capabilities
- Simplified frequency specification
- Robust, high performance time zone localization and conversion
- Enhanced date parsing
- New Period and PeriodIndex objects derived partially from
 legacy scikits.timeseries Date and DateArray

In addition to enhanced time series capabilities, pandas has also
acquired many new plotting functions and features, which will
continue as Vytautas Jancauskas, our GSoC 2012 student, continues
implementing new features. There are too many other improvements and performance
enhancements to mention, see the What's New page and full release
notes for more:

What's new: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html

Many thanks to all those who contributed to making this milestone
release happen:

$ git log v0.7.3..v0.8.0 --pretty=format:%aN | sort | uniq -c | sort -rn
  499 Wes McKinney
  257 Chang She
  162 Adam Klein
   17 Skipper Seabold
   13 Vytautas Jancauskas
   13 Kieran O'Mahony
   10 Wouter Overmeire
    8 Thomas Kluyver
    8 Luca Beltrame
    7 Takafumi Arakaki
    5 Mark Wiebe
    5 Marc Abramowitz
    3 Yaroslav Halchenko
    3 timmie
    2 RuiDC
    2 Roy Hyunjin Han
    2 Paddy Mullen
    2 Jacques Kvam
    2 Eric Chlebek
    1 thuske
    1 Stefan van der Walt
    1 Senthil Palanisami
    1 Peng Yu
    1 Lorenzo Bolla
    1 Kelsey Jordahl
    1 Kamil Kisiel
    1 David Zaslavsky

Happy data hacking!

- Wes

What is it
==========
pandas is a Python package providing fast, flexible, and
expressive data structures designed to make working with
relational, time series, or any other kind of labeled data both
easy and intuitive. It aims to be the fundamental high-level
building block for doing practical, real world data analysis in
Python.

Links
=====
Release Notes: http://github.com/pydata/pandas/blob/master/RELEASE.rst
Documentation: http://pandas.pydata.org
Installers: http://pypi.python.org/pypi/pandas
Code Repository: http://github.com/pydata/pandas
Mailing List: http://groups.google.com/group/pydata
Blogs: http://blog.wesmckinney.com and http://blog.lambdafoundry.com