v0.15.0.txt

.. _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>`.

- Raise a ``ValueError`` in ``df.to_hdf`` with 'fixed' format, if ``df`` has non-unique columns as the resulting file will be broken (:issue:`7761`)

- :func:`rolling_min`, :func:`rolling_max`, :func:`rolling_cov`, and :func:`rolling_corr`
  now return objects with all ``NaN``s when ``len(arg) < min_periods <= window``
  (like all other rolling functions do) rather than producing an error message.  (:issue:`7766`)
  For example, this is the old behavior:
	.. ipython:: python
		In [14]: s = Series([10, 11, 12, 13])

		In [15]: rolling_min(s, window=10, min_periods=5)
		---------------------------------------------------------------------------
		ValueError                                Traceback (most recent call last)
		<ipython-input-15-f622819d7987> in <module>()
		----> 1 rolling_min(s, window=10, min_periods=5)
		...
		ValueError: min_periods (5) must be <= window (4)
  whereas this is the new behavior:
	.. ipython:: python
		In [16]: rolling_min(s, window=10, min_periods=5)
		Out[16]:
		0   NaN
		1   NaN
		2   NaN
		3   NaN
		dtype: float64

.. _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`)


- ``PeriodIndex`` supports ``resolution`` as the same as ``DatetimeIndex`` (:issue:`7708`)





























.. _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 ``HDFStore.select_column()`` not preserving UTC timezone info when selecting a DatetimeIndex (:issue:`7777`)


- Bug in pickles contains ``DateOffset`` may raise ``AttributeError`` when ``normalize`` attribute is reffered internally (:issue:`7748`)

- Bug in pickle deserialization that failed for pre-0.14.1 containers with dup items trying to avoid ambiguity
  when matching block and manager items, when there's only one block there's no ambiguity (:issue:`7794`)


- 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`)



- Bug in ``DataFrame.plot`` with ``subplots=True`` may draw unnecessary minor xticks and yticks (:issue:`7801`)