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v0.13.1.txt
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.. _whatsnew_0131:
v0.13.1 (???)
-------------
This is a major release from 0.13.0 and includes a number of API changes, several new features and
enhancements along with a large number of bug fixes.
Highlights include:
Several experimental features are added, including:
There are several new or updated docs sections including:
- :ref:`Tutorials<tutorials>`, a guide to community developed pandas tutorials.
- :ref:`Pandas Ecosystem<ecosystem>`, a guide to complementary projects built on top of pandas.
API changes
~~~~~~~~~~~
Prior Version Deprecations/Changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
These were announced changes in 0.13 or prior that are taking effect as of 0.13.1
Deprecations
~~~~~~~~~~~~
Enhancements
~~~~~~~~~~~~
- ``Panel.apply`` will work on non-ufuncs. See :ref:`the docs<basics.apply_panel>`.
.. ipython:: python
import pandas.util.testing as tm
panel = tm.makePanel(5)
panel
panel['ItemA']
Specifying an ``apply`` that operates on a Series (to return a single element)
.. ipython:: python
panel.apply(lambda x: x.dtype, axis='items')
A similar reduction type operation
.. ipython:: python
panel.apply(lambda x: x.sum(), axis='major_axis')
This is equivalent to
.. ipython:: python
panel.sum('major_axis')
A transformation operation that returns a Panel, but is computing
the z-score across the major_axis
.. ipython:: python
result = panel.apply(lambda x: (x-x.mean())/x.std(), axis='major_axis')
result
result['ItemA']
- ``Panel.apply`` operating on cross-sectional slabs. (:issue:`1148`)
.. ipython:: python
f = lambda x: (x-x.mean(1)/x.std(1))
result = panel.apply(f, axis = ['items','major_axis'])
result
result.loc[:,:,'ItemA']
This is equivalent to the following
.. ipython:: python
result = Panel(dict([ (ax,f(panel.loc[:,:,ax])) for ax in panel.minor_axis ]))
result
result.loc[:,:,'ItemA']
- The ``ArrayFormatter``s for ``datetime`` and ``timedelta64`` now intelligently
limit precision based on the values in the array (:issue:`3401`)
Previously output might look like:
.. code-block:: python
age today diff
0 2001-01-01 00:00:00 2013-04-19 00:00:00 4491 days, 00:00:00
1 2004-06-01 00:00:00 2013-04-19 00:00:00 3244 days, 00:00:00
Now the output looks like:
.. ipython:: python
df = DataFrame([ Timestamp('20010101'),
Timestamp('20040601') ], columns=['age'])
df['today'] = Timestamp('20130419')
df['diff'] = df['today']-df['age']
df
Experimental
~~~~~~~~~~~~
Bug Fixes
~~~~~~~~~
See :ref:`V0.13.1 Bug Fixes<release.bug_fixes-0.13.1>` for an extensive list of bugs that have been fixed in 0.13.1.
See the :ref:`full release notes
<release>` or issue tracker
on GitHub for a complete list of all API changes, Enhancements and Bug Fixes.