[Numpy-discussion] ANN: v0.18.1 pandas Released

[Jeff Reback]

This is a minor bug-fix release from 0.18.0 and includes a large number of
bug fixes along several new features, enhancements, and performance
improvements. We recommend that all users upgrade to this version.

This was a release of 6 weeks with 210 commits by 60 authors encompassing
142 issues and 164 pull-requests.


*What is it:*

*pandas* is a Python package providing fast, flexible, and expressive
data structures
designed to make working with “relational” or “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. Additionally, it
has the broader goal of becoming the most powerful and flexible open source
data analysis / manipulation tool available in any language.


*Highlights*:

- .groupby(...) has been enhanced to provide convenient syntax when working
with .rolling(..), .expanding(..) and .resample(..) per group, see here
<http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#whatsnew-0181-deferred-ops>
- pd.to_datetime() has gained the ability to assemble dates from a
DataFrame, see here
<http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#whatsnew-0181-enhancements-assembling>
- Method chaining improvements, see here
<http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#whatsnew-0181-enhancements-method-chain>
- Custom business hour offset, see here
<http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#whatsnew-0181-enhancements-custombusinesshour>
- Many bug fixes in the handling of sparse, see here
<http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#whatsnew-0181-sparse>
- Expanded the Tutorials section
<http://pandas.pydata.org/pandas-docs/version/0.18.1/tutorials.html#tutorial-modern>
with
a feature on modern pandas, courtesy of @TomAugsburger
<https://twitter.com/TomAugspurger>.

See the Whatsnew
<http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html> for
much more information, and the full Documentation
<http://pandas.pydata.org/pandas-docs/stable/> link.


*How to get it:*

Source tarballs, windows wheels, and macosx wheels are available on PyPI
<https://pypi.python.org/pypi/pandas>. Windows wheels are courtesy of Christoph
Gohlke, and are built on Numpy 1.10. Macosx wheels are courtesy of Matthew
Brett.

Installation via conda is: conda install pandas
currently its available via the conda-forge channel: conda install pandas
-c conda-forge
It will be available on the main channel shortly.

Please report any issues on our issue tracker
<https://github.com/pydata/pandas/issues>:

Jeff Reback


*Thanks to all of the contributors*


* - Andrew Fiore-Gartland- Bastiaan- Benoît Vinot- Brandon Rhodes- DaCoEx-
Drew Fustin- Ernesto Freitas- Filip Ter- Gregory Livschitz- Gábor Lipták-
Hassan Kibirige- Iblis Lin- Israel Saeta Pérez- Jason Wolosonovich- Jeff
Reback- Joe Jevnik- Joris Van den Bossche- Joshua Storck- Ka Wo Chen- Kerby
Shedden- Kieran O'Mahony- Leif Walsh- Mahmoud Lababidi- Maoyuan Liu- Mark
Roth- Matt Wittmann- MaxU- Maximilian Roos- Michael Droettboom- Nick
Eubank- Nicolas Bonnotte- OXPHOS- Pauli Virtanen- Peter Waller- Pietro
Battiston- Prabhjot Singh- Robin Wilson- Roger Thomas- Sebastian Bank-
Stephen Hoover- Tim Hopper- Tom Augspurger- WANG Aiyong- Wes Turner-
Winand- Xbar- Yan Facai- adneu- ajenkins-cargometrics- behzad nouri-
chinskiy- gfyoung- jeps-journal- jonaslb- kotrfa- nileracecrew-
onesandzeroes- rs2- sinhrks- tsdlovell*