Mailman 3 May 2015 - Pandas-dev

Pandas development meeting: tuesday June 2 at 17:00 UTC
by Joris Van den Bossche June 3, 2015

June 3, 2015

Hi all, We are planning a next online Pandas Development Meeting coming Monday, June 2nd, at 17:00 UTC (which should correspond to 19:00 CEST in most of Europe, and 13:00 EST and 10am PST in the two sides of America). Some first provisional topics to discuss are listed here: https://docs.google.com/document/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-lico… If you think of other points, or have remarks on them, feel free to add them in the google docs. If you are interested in joining (and you don't need to be a core developer of pandas for that!), send a notice, then we ensure to invite you for the google hang-out. Regards, Joris

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Upcoming Index repr changes
by Joris Van den Bossche May 22, 2015

May 22, 2015

Hi all, We have a PR pending to unify the string representation of the different Index objects: https://github.com/pydata/pandas/pull/9901 What are the most important changes: - We propose to reduce the default number of values shown from 100 to 10 (an option controllable as pd.options.display.max_seq_items). - The datetime-like indices (DatetimeIndex, TimedeltaIndex, PeriodIndex) were always somewhat different and get a new repr that is now more consistent with how it is for other Index types like Int64Index. This is the biggest change. So for eg Int64Index not much changes (only 'name' is now also shown, and the number of shown values has changed), but for DatetimeIndex the change is larger. *But we would like to get some feedback on this!* Do you like the changes? For DatetimeIndex? For the number of shown values? Would you want different behaviour for repr() and str()? Some examples of the changes with the current state of the PR are shown below: Previous Behavior In [1]: pd.get_option('max_seq_items') Out[1]: 100 In [2]: pd.Index(range(4), name='foo') Out[2]: Int64Index([0, 1, 2, 3], dtype='int64') In [3]: pd.Index(range(104), name='foo') Out[3]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64') In [4]: pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern') Out[4]: <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01 00:00:00-05:00, ..., 2013-01-04 00:00:00-05:00] Length: 4, Freq: D, Timezone: US/Eastern In [5]: pd.date_range('20130101', periods=104, name='foo', tz='US/Eastern') Out[5]: <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01 00:00:00-05:00, ..., 2013-04-14 00:00:00-04:00] Length: 104, Freq: D, Timezone: US/Eastern New Behavior In [1]: pd.get_option('max_seq_items') Out[1]: 10 In [9]: pd.Index(range(4), name='foo') Out[9]: Int64Index([0, 1, 2, 3], dtype='int64', name=u'foo') In [10]: pd.Index(range(104), name='foo') Out[10]: Int64Index([0, 1, ..., 102, 103], dtype='int64', name=u'foo', length=104) In [11]: pd.date_range('20130101', periods=4, name='foo', tz='US/Eastern') Out[11]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00', '2013-01-03 00:00:00-05:00', '2013-01-04 00:00:00-05:00'], dtype='datetime64[ns]', name=u'foo', freq='D', tz='US/Eastern') In [12]: pd.date_range('20130101', periods=104 ,name='foo', tz='US/Eastern') Out[12]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00', ..., '2013-04-13 00:00:00-04:00', '2013-04-14 00:00:00-04:00'], dtype='datetime64[ns]', name=u'foo', length=104, freq='D', tz='US/Eastern')

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ANN: pandas 0.16.1 released
by Jeff Reback May 11, 2015

May 11, 2015

Hello, We are proud to announce v0.16.1 of pandas, a minor release from 0.16.0. This release includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. This was a release of 7 weeks with 222 commits by 57 authors encompassing 85 issues. We recommend that all users upgrade to this version. *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 of this release include: - Support for *CategoricalIndex*, a category based index, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0161-enh…> - New section on how-to-contribute to *pandas*, see here <http://pandas.pydata.org/pandas-docs/stable/contributing.html> - Revised "Merge, join, and concatenate" documentation, including graphical examples to make it easier to understand each operations, see here <http://pandas.pydata.org/pandas-docs/stable/merging.html> - New method *sample* for drawing random samples from Series, DataFrames and Panels. See here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0161-enh…> - The default *Index* printing has changed to a more uniform format, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0161-ind…> - *BusinessHour* datetime-offset is now supported, see here <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#business-hour> - Further enhancement to the *.str* accessor to make string operations easier, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0161-enh…> See the Whatsnew in v0.16.1 <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-16-1-may-11-20…> Documentation: http://pandas.pydata.org/pandas-docs/stable/ Source tarballs, windows binaries are available on PyPI: https://pypi.python.org/pypi/pandas windows binaries are courtesy of Christoph Gohlke and are built on Numpy 1.8 macosx wheels are courtesy of Matthew Brett Please report any issues here: https://github.com/pydata/pandas/issues Thanks The Pandas Development Team Contributors to the 0.16.1 release - - Alfonso MHC - Andy Hayden - Artemy Kolchinsky - Chris Gilmer - Chris Grinolds - Dan Birken - David BROCHART - David Hirschfeld - David Stephens - Dr. Leo - Evan Wright - Frans van Dunné - Hatem Nassrat - Henning Sperr - Hugo Herter - Jan Schulz - Jeff Blackburne - Jeff Reback - Jim Crist - Jonas Abernot - Joris Van den Bossche - Kerby Shedden - Leo Razoumov - Manuel Riel - Mortada Mehyar - Nick Burns - Nick Eubank - Olivier Grisel - Phillip Cloud - Pietro Battiston - Roy Hyunjin Han - Sam Zhang - Scott Sanderson - Stephan Hoyer - Tiago Antao - Tom Ajamian - Tom Augspurger - Tomaz Berisa - Vikram Shirgur - Vladimir Filimonov - William Hogman - Yasin A - Younggun Kim - behzad nouri - dsm054 - floydsoft - flying-sheep - gfr - jnmclarty - jreback - ksanghai - lucas - mschmohl - ptype - rockg - scls19fr - sinhrks

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