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* -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/