[Numpy-discussion] [pydata] ANN: pandas v0.17.0rc2 - RELEASE CANDIDATE 2
matthew.brett at gmail.com
Sun Oct 4 01:35:42 EDT 2015
On Sat, Oct 3, 2015 at 2:33 PM, Jeff Reback <jeffreback at gmail.com> wrote:
> I'm pleased to announce the availability of the second release candidate of
> Pandas 0.17.0.
> Please try this RC and report any issues here: Pandas Issues
> We will be releasing officially on October 9.
> **RELEASE CANDIDATE 2**
> From RC 1 we have:
> compat for Python 3.5
> compat for matplotlib 1.5.0
> .convert_objects is now restored to the original, and is deprecated
> This is a major release from 0.16.2 and includes a small number of API
> changes, several new features, enhancements, and performance improvements
> along with a large number of bug fixes. We recommend that all users upgrade
> to this version.
> Highlights include:
> Release the Global Interpreter Lock (GIL) on some cython operations, see
> Plotting methods are now available as attributes of the .plot accessor, see
> The sorting API has been revamped to remove some long-time inconsistencies,
> see here
> Support for a datetime64[ns] with timezones as a first-class dtype, see here
> The default for to_datetime will now be to raise when presented with
> unparseable formats, previously this would return the original input, see
> The default for dropna in HDFStore has changed to False, to store by default
> all rows even if they are all NaN, see here
> Support for Series.dt.strftime to generate formatted strings for
> datetime-likes, see here
> Development installed versions of pandas will now have PEP440 compliant
> version strings GH9518
> Development support for benchmarking with the Air Speed Velocity library
> Support for reading SAS xport files, see here
> Removal of the automatic TimeSeries broadcasting, deprecated since 0.8.0,
> see here
> Display format with plain text can optionally align with Unicode East Asian
> Width, see here
> Compatibility with Python 3.5 GH11097
> Compatibility with matplotlib 1.5.0 GH11111
> See the Whatsnew for much more information.
> Best way to get this is to install via conda from our development channel.
> Builds for osx-64,linux-64,win-64 for Python 2.7, Python 3.4, and Python 3.5
> (for osx/linux) are all available.
> conda install pandas -c pandas
I built OSX wheels for Pythons 2.7, 3.4, 3.5. To test:
pip install --pre -f http://wheels.scipy.org pandas
There were some test failures for Python 3.3 - issue here:
More information about the NumPy-Discussion