Jeff Reback
2015-11-21 13:43:52 UTC
Hi,
We are proud to announce that *pandas* has become a sponsored project of
the NUMFocus organization
<http://numfocus.org/news/2015/10/09/numfocus-announces-new-fiscally-sponsored-project-pandas/>
This will help ensure the success of development of *pandas* as a
world-class open-source project.
This is a minor bug-fix release from 0.17.0 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 5 weeks with 176 commits by 61 authors encompassing
84 issues and 128 pull-requests.
*What is it:*
*pandas* is a Python package providing fast, flexible, and expressive data
structures designed to make working with ârelationalâ or âlabeledâ 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*:
- Support for Conditional HTML Formatting, see here
<http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-style>
- Releasing the GIL on the csv reader & other ops, see here
<http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-performance>
- Fixed regression in DataFrame.drop_duplicates from 0.16.2, causing
incorrect results on integer values see Issue 11376
See the Whatsnew
<http://pandas.pydata.org/pandas-docs/version/0.17.1/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>
Installation via conda is:
- conda install pandas
windows wheels are courtesy of Christoph Gohlke and are built on Numpy 1.9
macosx wheels are courtesy of Matthew Brett
*Issues:*
Please report any issues on our issue tracker
<https://github.com/pydata/pandas/issues>:
Jeff
*Thanks to all of the contributors*
* - Aleksandr Drozd - Alex Chase - Anthonios Partheniou - BrenBarn - Brian
J. McGuirk - Chris - Christian Berendt - Christian Perez - Cody Piersall -
Data & Code Expert Experimenting with Code on Data - DrIrv - Evan Wright -
Guillaume Gay - Hamed Saljooghinejad - Iblis Lin - Jake VanderPlas - Jan
Schulz - Jean-Mathieu Deschenes - Jeff Reback - Jimmy Callin - Joris Van
den Bossche - K.-Michael Aye - Ka Wo Chen - Loïc Séguin-C - Luo Yicheng -
Magnus Jöud - Manuel Leonhardt - Matthew Gilbert - Maximilian Roos -
Michael - Nicholas Stahl - Nicolas Bonnotte - Pastafarianist - Petra Chong
- Phil Schaf - Philipp A - Rob deCarvalho - Roman Khomenko - Rémy Léone -
Sebastian Bank - Thierry Moisan - Tom Augspurger - Tux1 - Varun - Wieland
Hoffmann - Winterflower - Yoav Ram - Younggun Kim - Zeke - ajcr - azuranski
- behzad nouri - cel4 - emilydolson - hironow - lexual - llllllllll - rockg
- silentquasar - sinhrks - taeold *
We are proud to announce that *pandas* has become a sponsored project of
the NUMFocus organization
<http://numfocus.org/news/2015/10/09/numfocus-announces-new-fiscally-sponsored-project-pandas/>
This will help ensure the success of development of *pandas* as a
world-class open-source project.
This is a minor bug-fix release from 0.17.0 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 5 weeks with 176 commits by 61 authors encompassing
84 issues and 128 pull-requests.
*What is it:*
*pandas* is a Python package providing fast, flexible, and expressive data
structures designed to make working with ârelationalâ or âlabeledâ 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*:
- Support for Conditional HTML Formatting, see here
<http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-style>
- Releasing the GIL on the csv reader & other ops, see here
<http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-performance>
- Fixed regression in DataFrame.drop_duplicates from 0.16.2, causing
incorrect results on integer values see Issue 11376
See the Whatsnew
<http://pandas.pydata.org/pandas-docs/version/0.17.1/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>
Installation via conda is:
- conda install pandas
windows wheels are courtesy of Christoph Gohlke and are built on Numpy 1.9
macosx wheels are courtesy of Matthew Brett
*Issues:*
Please report any issues on our issue tracker
<https://github.com/pydata/pandas/issues>:
Jeff
*Thanks to all of the contributors*
* - Aleksandr Drozd - Alex Chase - Anthonios Partheniou - BrenBarn - Brian
J. McGuirk - Chris - Christian Berendt - Christian Perez - Cody Piersall -
Data & Code Expert Experimenting with Code on Data - DrIrv - Evan Wright -
Guillaume Gay - Hamed Saljooghinejad - Iblis Lin - Jake VanderPlas - Jan
Schulz - Jean-Mathieu Deschenes - Jeff Reback - Jimmy Callin - Joris Van
den Bossche - K.-Michael Aye - Ka Wo Chen - Loïc Séguin-C - Luo Yicheng -
Magnus Jöud - Manuel Leonhardt - Matthew Gilbert - Maximilian Roos -
Michael - Nicholas Stahl - Nicolas Bonnotte - Pastafarianist - Petra Chong
- Phil Schaf - Philipp A - Rob deCarvalho - Roman Khomenko - Rémy Léone -
Sebastian Bank - Thierry Moisan - Tom Augspurger - Tux1 - Varun - Wieland
Hoffmann - Winterflower - Yoav Ram - Younggun Kim - Zeke - ajcr - azuranski
- behzad nouri - cel4 - emilydolson - hironow - lexual - llllllllll - rockg
- silentquasar - sinhrks - taeold *