[SciPy-User] ANN: pandas 0.9.1 released

[Virgil Stokes]

On 15-Nov-2012 04:33, Wes McKinney wrote:
> hi all,
>
> I'm pleased to announce the 0.9.1 release of pandas. This is
> primarily a bugfix release but contains a number of new features
> and enhancements. There are also a few minor API changes having
> to do with time series using the Period data type. I strongly
> recommend that all users upgrade to this release.
>
> Note that I plan to make a 0.9.2 release shortly within the next
> few weeks (hopefully!) that includes the new file parser
> branch (http://wesmckinney.com/blog/?p=543). This includes vastly
> improved performance and memory usage, per-column user data type
> specification (in addition to the default type inference), column
> subsetting by name (for those "really wide" CSV files), improved
> support for European decimal formats, and many other goodies. I'm
> hopeful someone will help port this code to go in a near-future
> version of NumPy.
>
> As always source archives and Windows installers can be found on
> PyPI. Thanks to all who contributed to this release, especially
> Yoval P, Chang She, Jeff Reback, and everyone listed below. A
> special thanks also goes out to all those who answer questions on
> StackOverflow and the mailing lists, helping to make the pandas
> community vibrant and helpful.
>
> What's new: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html
>
> $ git log v0.9.0..v0.9.1 --pretty=format:%aN | sort | uniq -c | sort -rn
>       90 Wes McKinney
>       66 y-p
>       57 Chang She
>        7 Jeff Reback
>        6 Tobias Brandt
>        4 Wouter Overmeire
>        4 Brenda Moon
>        2 timmie
>        1 Martin Blais
>        1 K.-Michael Aye
>        1 Justin C Johnson
>
> 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
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Great job Wes --- keep up the good work.
Best regards,
--V :-)