ANN: pandas 0.9.1 released

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ANN: pandas 0.9.1 released

Wes McKinney
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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Re: 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
> _______________________________________________
> SciPy-User mailing list
> [hidden email]
> http://mail.scipy.org/mailman/listinfo/scipy-user
Great job Wes --- keep up the good work.
Best regards,
--V :-)
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