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v0.15.0.txt
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.. _whatsnew_0150:
v0.15.0 (???)
-------------
This is a major release from 0.14.1 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:
- The ``Categorical`` type was integrated as a first-class pandas type, see here: :ref:`Categorical Changes <whatsnew_0150.cat>`
- :ref:`Other Enhancements <whatsnew_0150.enhancements>`
- :ref:`API Changes <whatsnew_0150.api>`
- :ref:`Performance Improvements <whatsnew_0150.performance>`
- :ref:`Prior Deprecations <whatsnew_0150.prior_deprecations>`
- :ref:`Deprecations <whatsnew_0150.deprecations>`
- :ref:`Known Issues <whatsnew_0150.knownissues>`
- :ref:`Bug Fixes <whatsnew_0150.bug_fixes>`
.. _whatsnew_0150.api:
API changes
~~~~~~~~~~~
- Passing multiple levels to `DataFrame.stack()` will now work when multiple level
numbers are passed (:issue:`7660`), and will raise a ``ValueError`` when the
levels aren't all level names or all level numbers. See
:ref:`Reshaping by stacking and unstacking <reshaping.stack_multiple>`.
.. _whatsnew_0150.cat:
Categoricals in Series/DataFrame
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:class:`~pandas.Categorical` can now be included in `Series` and `DataFrames` and gained new
methods to manipulate. Thanks to Jan Schultz for much of this API/implementation. (:issue:`3943`, :issue:`5313`, :issue:`5314`, :issue:`7444`).
.. ipython:: python
df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
# convert the raw grades to a categorical
df["grade"] = pd.Categorical(df["raw_grade"])
# Alternative: df["grade"] = df["raw_grade"].astype("category")
df["grade"]
# Rename the levels
df["grade"].cat.levels = ["very good", "good", "very bad"]
# Reorder the levels and simultaneously add the missing levels
df["grade"].cat.reorder_levels(["very bad", "bad", "medium", "good", "very good"])
df["grade"]
df.sort("grade")
df.groupby("grade").size()
See the :ref:`Categorical introduction<_categorical>` and the :ref:`API documentation<api.categorical>`.
- `pandas.core.group_agg` and `pandas.core.factor_agg` were removed. As an alternative, construct
a dataframe and use `df.groupby(<group>).agg(<func>)`.
- Supplying "codes/labels and levels" to the `pandas.Categorical` constructor is deprecated and does
not work without supplying ``compat=True``. The default mode now uses "values and levels".
Please change your code to use the ``Categorical.from_codes(...)`` constructor.
- The `pandas.Categorical.labels` attribute was renamed to `pandas.Categorical.codes` and is read
only. If you want to manipulate the `Categorical`, please use one of the
:ref:`API methods on Categoricals<api.categorical>`.
.. _whatsnew_0150.prior_deprecations:
Prior Version Deprecations/Changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
There are prior version deprecations that are taking effect as of 0.15.0.
.. _whatsnew_0150.deprecations:
Deprecations
~~~~~~~~~~~~
.. _whatsnew_0150.knownissues:
Known Issues
~~~~~~~~~~~~
.. _whatsnew_0150.enhancements:
Enhancements
~~~~~~~~~~~~
- Added support for bool, uint8, uint16 and uint32 datatypes in ``to_stata`` (:issue:`7097`, :issue:`7365`)
.. _whatsnew_0150.performance:
Performance
~~~~~~~~~~~
- Performance improvements in ``DatetimeIndex.__iter__`` to allow faster iteration (:issue:`7683`)
.. _whatsnew_0150.experimental:
Experimental
~~~~~~~~~~~~
There are no experimental changes in 0.15.0
.. _whatsnew_0150.bug_fixes:
Bug Fixes
~~~~~~~~~
- Bug in Series 0-division with a float and integer operand dtypes (:issue:`7785`)
- Bug in ``Series.astype("unicode")`` not calling ``unicode`` on the values correctly (:issue:`7758`)
- Bug in ``DataFrame.as_matrix()`` with mixed ``datetime64[ns]`` and ``timedelta64[ns]`` dtypes (:issue:`7778`)
- Bug in pickles contains ``DateOffset`` may raise ``AttributeError`` when ``normalize`` attribute is reffered internally (:issue:`7748`)
- Bug in ``is_superperiod`` and ``is_subperiod`` cannot handle higher frequencies than ``S`` (:issue:`7760`, :issue:`7772`, :issue:`7803`)
- Bug in ``DataFrame.reset_index`` which has ``MultiIndex`` contains ``PeriodIndex`` or ``DatetimeIndex`` with tz raises ``ValueError`` (:issue:`7746`, :issue:`7793`)