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.

.. warning::

   pandas >= 0.15.0 will no longer support compatibility with NumPy versions <
   1.7.0. If you want to use the latest versions of pandas, please upgrade to
   NumPy >= 1.7.0.

- Highlights include:

  - The ``Categorical`` type was integrated as a first-class pandas type, see :ref:`here <whatsnew_0150.cat>`
  - New scalar type ``Timedelta``, and a new index type ``TimedeltaIndex``, see :ref:`here <whatsnew_0150.timedeltaindex>`
  - New datetimelike properties accessor ``.dt`` for Series, see :ref:`Datetimelike Properties <whatsnew_0150.dt>`
  - Split indexing documentation into :ref:`Indexing and Selecting Data <indexing>` and :ref:`MultiIndex / Advanced Indexing <advanced>`
  - API change in using Indexes in set operations, see :ref:`here <whatsnew_0150.index_set_ops>`
  - Internal refactoring of the ``Index`` class to no longer sub-class ``ndarray``, see :ref:`Internal Refactoring <whatsnew_0150.refactoring>`
  - dropping support for ``PyTables`` less than version 3.0.0, and ``numexpr`` less than version 2.1 (:issue:`7990`)

- :ref:`Other Enhancements <whatsnew_0150.enhancements>`

- :ref:`API Changes <whatsnew_0150.api>`

- :ref:`Performance Improvements <whatsnew_0150.performance>`

- :ref:`Deprecations <whatsnew_0150.deprecations>`

- :ref:`Bug Fixes <whatsnew_0150.bug_fixes>`

.. warning::

   In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray``
   but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be
   a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`)

.. _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>`.

- :func:`set_names`, :func:`set_labels`, and :func:`set_levels` methods now take an optional ``level`` keyword argument to all modification of specific level(s) of a MultiIndex. Additionally :func:`set_names` now accepts a scalar string value when operating on an ``Index`` or on a specific level of a ``MultiIndex`` (:issue:`7792`)

  .. ipython:: python

      idx = MultiIndex.from_product([['a'], range(3), list("pqr")], names=['foo', 'bar', 'baz'])
      idx.set_names('qux', level=0)
      idx.set_names(['qux','baz'], level=[0,1])
      idx.set_levels(['a','b','c'], level='bar')
      idx.set_levels([['a','b','c'],[1,2,3]], level=[1,2])

- 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`` when ``len(arg) < min_periods <= window`` rather
  than raising. (This makes all rolling functions consistent in this behavior), (:issue:`7766`)

  Prior to 0.15.0

  .. ipython:: python

     s = Series([10, 11, 12, 13])

  .. code-block:: python

     In [15]: rolling_min(s, window=10, min_periods=5)
     ValueError: min_periods (5) must be <= window (4)

  New behavior

  .. ipython:: python

     rolling_min(s, window=10, min_periods=5)

- :func:`rolling_max`, :func:`rolling_min`, :func:`rolling_sum`, :func:`rolling_mean`, :func:`rolling_median`,
  :func:`rolling_std`, :func:`rolling_var`, :func:`rolling_skew`, :func:`rolling_kurt`, :func:`rolling_quantile`,
  :func:`rolling_cov`, :func:`rolling_corr`, :func:`rolling_corr_pairwise`,
  :func:`rolling_window`, and :func:`rolling_apply` with ``center=True`` previously would return a result of the same
  structure as the input ``arg`` with ``NaN`` in the final ``(window-1)/2`` entries.
  Now the final ``(window-1)/2`` entries of the result are calculated as if the input ``arg`` were followed
  by ``(window-1)/2`` ``NaN`` values (or with shrinking windows, in the case of :func:`rolling_apply`).
  (:issue:`7925`, :issue:`8269`)

  Prior behavior (note final value is ``NaN``):

  .. code-block:: python

    In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
    Out[7]:
    0     1
    1     3
    2     6
    3   NaN
    dtype: float64

  New behavior (note final value is ``5 = sum([2, 3, NaN])``):

  .. ipython:: python

    rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)

- Removed ``center`` argument from :func:`expanding_max`, :func:`expanding_min`, :func:`expanding_sum`,
  :func:`expanding_mean`, :func:`expanding_median`, :func:`expanding_std`, :func:`expanding_var`,
  :func:`expanding_skew`, :func:`expanding_kurt`, :func:`expanding_quantile`, :func:`expanding_count`,
  :func:`expanding_cov`, :func:`expanding_corr`, :func:`expanding_corr_pairwise`, and :func:`expanding_apply`,
  as the results produced when ``center=True`` did not make much sense. (:issue:`7925`)

- Added optional ``ddof`` argument to :func:`expanding_cov` and :func:`rolling_cov`.
  The default value of ``1`` is backwards-compatible. (:issue:`8279`)

- Documented the ``ddof`` argument to :func:`expanding_var`, :func:`expanding_std`,
  :func:`rolling_var`, and :func:`rolling_std`. These functions' support of a
  ``ddof`` argument (with a default value of ``1``) was previously undocumented. (:issue:`8064`)

- :func:`ewma`, :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, :func:`ewmcov`, and :func:`ewmcorr`
  now interpret ``min_periods`` in the same manner that the ``rolling_*`` and ``expanding_*`` functions do:
  a given result entry will be ``NaN`` if the (expanding, in this case) window does not contain
  at least ``min_periods`` values. The previous behavior was to set to ``NaN`` the ``min_periods`` entries
  starting with the first non- ``NaN`` value. (:issue:`7977`)

  Prior behavior (note values start at index ``2``, which is ``min_periods`` after index ``0``
  (the index of the first non-empty value)):

  .. ipython:: python

    s  = Series([1, None, None, None, 2, 3])

  .. code-block:: python

	In [51]: ewma(s, com=3., min_periods=2)
	Out[51]:
	0         NaN
	1         NaN
	2    1.000000
	3    1.000000
	4    1.571429
	5    2.189189
	dtype: float64

  New behavior (note values start at index ``4``, the location of the 2nd (since ``min_periods=2``) non-empty value):

  .. ipython:: python

    ewma(s, com=3., min_periods=2)

- :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, :func:`ewmcov`, and :func:`ewmcorr`
  now have an optional ``adjust`` argument, just like :func:`ewma` does,
  affecting how the weights are calculated.
  The default value of ``adjust`` is ``True``, which is backwards-compatible.
  See :ref:`Exponentially weighted moment functions <stats.moments.exponentially_weighted>` for details. (:issue:`7911`)

- :func:`ewma`, :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, :func:`ewmcov`, and :func:`ewmcorr`
  now have an optional ``ignore_na`` argument.
  When ``ignore_na=False`` (the default), missing values are taken into account in the weights calculation.
  When ``ignore_na=True`` (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation.
  (:issue:`7543`)

  .. ipython:: python

     ewma(Series([None, 1., 8.]), com=2.)
     ewma(Series([1., None, 8.]), com=2., ignore_na=True)  # pre-0.15.0 behavior
     ewma(Series([1., None, 8.]), com=2., ignore_na=False)  # new default

- Bug in passing a ``DatetimeIndex`` with a timezone that was not being retained in DataFrame construction from a dict (:issue:`7822`)

  In prior versions this would drop the timezone.

  .. ipython:: python

        i = date_range('1/1/2011', periods=3, freq='10s', tz = 'US/Eastern')
        i
        df = DataFrame( {'a' : i } )
        df
        df.dtypes

  This behavior is unchanged.

  .. ipython:: python

        df = DataFrame( )
        df['a'] = i
        df
        df.dtypes

- ``SettingWithCopy`` raise/warnings (according to the option ``mode.chained_assignment``) will now be issued when setting a value on a sliced mixed-dtype DataFrame using chained-assignment. (:issue:`7845`, :issue:`7950`)

  .. code-block:: python

     In [1]: df = DataFrame(np.arange(0,9), columns=['count'])

     In [2]: df['group'] = 'b'

     In [3]: df.iloc[0:5]['group'] = 'a'
     /usr/local/bin/ipython:1: SettingWithCopyWarning:
     A value is trying to be set on a copy of a slice from a DataFrame.
     Try using .loc[row_indexer,col_indexer] = value instead

     See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

- The ``infer_types`` argument to :func:`~pandas.io.html.read_html` now has no
  effect (:issue:`7762`, :issue:`7032`).

- ``DataFrame.to_stata`` and ``StataWriter`` check string length for
  compatibility with limitations imposed in dta files where fixed-width
  strings must contain 244 or fewer characters.  Attempting to write Stata
  dta files with strings longer than 244 characters raises a ``ValueError``. (:issue:`7858`)

- ``read_stata`` and ``StataReader`` can import missing data information into a
  ``DataFrame`` by setting the argument ``convert_missing`` to ``True``. When
  using this options, missing values are returned as ``StataMissingValue``
  objects and columns containing missing values have ``object`` data type. (:issue:`8045`)

- ``Index.isin`` now supports a ``level`` argument to specify which index level
  to use for membership tests (:issue:`7892`, :issue:`7890`)

  .. code-block:: python

     In [1]: idx = pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']])

     In [2]: idx.values
     Out[2]: array([(0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')], dtype=object)

     In [3]: idx.isin(['a', 'c', 'e'], level=1)
     Out[3]: array([ True, False,  True,  True, False,  True], dtype=bool)

- ``tz_localize(None)`` for tz-aware ``Timestamp`` and ``DatetimeIndex`` now removes timezone holding local time,
  previously results in ``Exception`` or ``TypeError`` (:issue:`7812`)

  .. ipython:: python

     ts = Timestamp('2014-08-01 09:00', tz='US/Eastern')
     ts
     ts.tz_localize(None)

     didx = DatetimeIndex(start='2014-08-01 09:00', freq='H', periods=10, tz='US/Eastern')
     didx
     didx.tz_localize(None)

- ``DataFrame.tz_localize`` and ``DataFrame.tz_convert`` now accepts an optional ``level`` argument
  for localizing a specific level of a MultiIndex (:issue:`7846`)
- ``Timestamp.tz_localize`` and ``Timestamp.tz_convert`` now raise ``TypeError`` in error cases, rather than ``Exception`` (:issue:`8025`)
- ``Timestamp.__repr__`` displays ``dateutil.tz.tzoffset`` info (:issue:`7907`)
- ``merge``, ``DataFrame.merge``, and ``ordered_merge`` now return the same type
  as the ``left`` argument.  (:issue:`7737`)

- Histogram from ``DataFrame.plot`` with ``kind='hist'`` (:issue:`7809`), See :ref:`the docs<visualization.hist>`.
- Boxplot from ``DataFrame.plot`` with ``kind='box'`` (:issue:`7998`), See :ref:`the docs<visualization.box>`.
- Consistency when indexing with ``.loc`` and a list-like indexer when no values are found.

  .. ipython:: python

     df = DataFrame([['a'],['b']],index=[1,2])
     df

  In prior versions there was a difference in these two constructs:

    - ``df.loc[[3]]`` would (prior to 0.15.0) return a frame reindexed by 3 (with all ``np.nan`` values)
    - ``df.loc[[3],:]`` would raise ``KeyError``.

  Both will now raise a ``KeyError``. The rule is that *at least 1* indexer must be found when using a list-like and ``.loc`` (:issue:`7999`)

  There was also a difference between ``df.loc[[1,3]]`` (returns a frame reindexed by ``[1, 3]``) and ``df.loc[[1, 3],:]`` (would raise ``KeyError`` prior to 0.15.0). Both will now return a reindexed frame.

  .. ipython:: python

     df.loc[[1,3]]
     df.loc[[1,3],:]

  This can also be seen in multi-axis indexing with a ``Panel``.

  .. ipython:: python

     p = Panel(np.arange(2*3*4).reshape(2,3,4),
               items=['ItemA','ItemB'],major_axis=[1,2,3],minor_axis=['A','B','C','D'])
     p

  The following would raise ``KeyError`` prior to 0.15.0:

  .. ipython:: python

     p.loc[['ItemA','ItemD'],:,'D']

  Furthermore, ``.loc`` will raise If no values are found in a multi-index with a list-like indexer:

  .. ipython:: python
     :okexcept:

     s = Series(np.arange(3,dtype='int64'),index=MultiIndex.from_product([['A'],['foo','bar','baz']],
                                                                         names=['one','two'])).sortlevel()
     s
     s.loc[['D']]

- ``Index`` now supports ``duplicated`` and ``drop_duplicates``. (:issue:`4060`)

  .. ipython:: python

     idx = Index([1, 2, 3, 4, 1, 2])
     idx
     idx.duplicated()
     idx.drop_duplicates()

- Assigning values to ``None`` now considers the dtype when choosing an 'empty' value (:issue:`7941`).

  Previously, assigning to ``None`` in numeric containers changed the
  dtype to object (or errored, depending on the call). It now uses
  NaN:

  .. ipython:: python

     s = Series([1, 2, 3])
     s.loc[0] = None
     s

  ``NaT`` is now used similarly for datetime containers.

  For object containers, we now preserve None values (previously these
  were converted to NaN values).

  .. ipython:: python

     s = Series(["a", "b", "c"])
     s.loc[0] = None
     s

  To insert a NaN, you must explicitly use ``np.nan``. See the :ref:`docs <missing.inserting>`.

- Previously an enlargement with a mixed-dtype frame would act unlike ``.append`` which will preserve dtypes (related :issue:`2578`, :issue:`8176`):

  .. ipython:: python

     df = DataFrame([[True, 1],[False, 2]], columns = ["female","fitness"])
     df
     df.dtypes

     # dtypes are now preserved
     df.loc[2] = df.loc[1]
     df
     df.dtypes

- ``Series.to_csv()`` now returns a string when ``path=None``, matching the behaviour of
    ``DataFrame.to_csv()`` (:issue:`8215`).

.. _whatsnew_0150.index_set_ops:

- The Index set operations ``+`` and ``-`` were deprecated in order to provide these for numeric type operations on certain index types. ``+`` can be replace by ``.union()`` or ``|``, and ``-`` by ``.difference()``. Further the method name ``Index.diff()`` is deprecated and can be replaced by ``Index.difference()`` (:issue:`8226`)

  .. code-block:: python

     # +
     Index(['a','b','c']) + Index(['b','c','d'])

     # should be replaced by
     Index(['a','b','c']).union(Index(['b','c','d']))

  .. code-block:: python

     # -
     Index(['a','b','c']) - Index(['b','c','d'])

     # should be replaced by
     Index(['a','b','c']).difference(Index(['b','c','d']))

- ``DataFrame.info()`` now ends its output with a newline character (:issue:`8114`)

.. _whatsnew_0150.dt:

.dt accessor
~~~~~~~~~~~~

``Series`` has gained an accessor to succinctly return datetime like properties for the *values* of the Series, if its a datetime/period like Series. (:issue:`7207`)
This will return a Series, indexed like the existing Series. See the :ref:`docs <basics.dt_accessors>`

.. ipython:: python

   # datetime
   s = Series(date_range('20130101 09:10:12',periods=4))
   s
   s.dt.hour
   s.dt.second
   s.dt.day
   s.dt.freq

This enables nice expressions like this:

.. ipython:: python

   s[s.dt.day==2]

You can easily produce tz aware transformations:

.. ipython:: python

   stz = s.dt.tz_localize('US/Eastern')
   stz
   stz.dt.tz

The ``.dt`` accessor works for period and timedelta dtypes.

.. ipython:: python

   # period
   s = Series(period_range('20130101',periods=4,freq='D'))
   s
   s.dt.year
   s.dt.day

.. ipython:: python

   # timedelta
   s = Series(timedelta_range('1 day 00:00:05',periods=4,freq='s'))
   s
   s.dt.days
   s.dt.seconds
   s.dt.components

.. _whatsnew_0150.refactoring:

Internal Refactoring
~~~~~~~~~~~~~~~~~~~~

In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray``
but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be
a transparent change with only very limited API implications (:issue:`5080`, :issue:`7439`, :issue:`7796`, :issue:`8024`)

- you may need to unpickle pandas version < 0.15.0 pickles using ``pd.read_pickle`` rather than ``pickle.load``. See :ref:`pickle docs <io.pickle>`
- when plotting with a ``PeriodIndex``. The ``matplotlib`` internal axes will now be arrays of ``Period`` rather than a ``PeriodIndex``. (this is similar to how a ``DatetimeIndex`` passes arrays of ``datetimes`` now)
- MultiIndexes will now raise similary to other pandas objects w.r.t. truth testing, See :ref:`here <gotchas.truth>` (:issue:`7897`).

.. _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`, :issue:`7839`, :issue:`7848`, :issue:`7864`, :issue:`7914`, :issue:`7768`, :issue:`8006`, :issue:`3678`,
:issue:`8075`, :issue:`8076`, :issue:`8143`).

For full docs, see the :ref:`Categorical introduction <categorical>` and the
:ref:`API documentation <api.categorical>`.

.. 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()

- ``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 :class:`~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 :meth:`~pandas.Categorical.from_codes` constructor.

- The ``Categorical.labels`` attribute was renamed to ``Categorical.codes`` and is read
  only. If you want to manipulate codes, please use one of the
  :ref:`API methods on Categoricals <api.categorical>`.

.. _whatsnew_0150.timedeltaindex:

TimedeltaIndex/Scalar
~~~~~~~~~~~~~~~~~~~~~

We introduce a new scalar type ``Timedelta``, which is a subclass of ``datetime.timedelta``, and behaves in a similar manner,
but allows compatibility with ``np.timedelta64`` types as well as a host of custom representation, parsing, and attributes.
This type is very similar to how ``Timestamp`` works for ``datetimes``. It is a nice-API box for the type. See the :ref:`docs <timedeltas.timedeltas>`.
(:issue:`3009`, :issue:`4533`, :issue:`8209`, :issue:`8187`, :issue:`8190`, :issue:`7869`, :issue:`7661`)

.. warning::

   ``Timedelta`` scalars (and ``TimedeltaIndex``) component fields are *not the same* as the component fields on a ``datetime.timedelta`` object. For example, ``.seconds`` on a ``datetime.timedelta`` object returns the total number of seconds combined between ``hours``, ``minutes`` and ``seconds``. In contrast, the pandas ``Timedelta`` breaks out hours, minutes, microseconds and nanoseconds separately.

   .. ipython:: python

      # Timedelta accessor
      tds = Timedelta('31 days 5 min 3 sec')
      tds.minutes
      tds.seconds

      # datetime.timedelta accessor
      # this is 5 minutes * 60 + 3 seconds
      tds.to_pytimedelta().seconds

.. warning::

       Prior to 0.15.0 ``pd.to_timedelta`` would return a ``Series`` for list-like/Series input, and a ``np.timedelta64`` for scalar input.
       It will now return a ``TimedeltaIndex`` for list-like input, ``Series`` for Series input, and ``Timedelta`` for scalar input.

       The arguments to ``pd.to_timedelta`` are now ``(arg,unit='ns',box=True)``, previously were ``(arg,box=True,unit='ns')`` as these are more logical.

Consruct a scalar

.. ipython:: python

   Timedelta('1 days 06:05:01.00003')
   Timedelta('15.5us')
   Timedelta('1 hour 15.5us')

   # negative Timedeltas have this string repr
   # to be more consistent with datetime.timedelta conventions
   Timedelta('-1us')

   # a NaT
   Timedelta('nan')

Access fields for a Timedelta

.. ipython:: python

   td = Timedelta('1 hour 3m 15.5us')
   td.hours
   td.minutes
   td.microseconds
   td.nanoseconds

Construct a ``TimedeltaIndex``

.. ipython:: python
   :suppress:

   import datetime
   from datetime import timedelta

.. ipython:: python

   TimedeltaIndex(['1 days','1 days, 00:00:05',
                   np.timedelta64(2,'D'),timedelta(days=2,seconds=2)])

Constructing a ``TimedeltaIndex`` with a regular range

.. ipython:: python

   timedelta_range('1 days',periods=5,freq='D')
   timedelta_range(start='1 days',end='2 days',freq='30T')

You can now use a ``TimedeltaIndex`` as the index of a pandas object

.. ipython:: python

   s = Series(np.arange(5),
              index=timedelta_range('1 days',periods=5,freq='s'))
   s

You can select with partial string selections

.. ipython:: python

   s['1 day 00:00:02']
   s['1 day':'1 day 00:00:02']

Finally, the combination of ``TimedeltaIndex`` with ``DatetimeIndex`` allow certain combination operations that are NaT preserving:

.. ipython:: python

   tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days'])
   tdi.tolist()
   dti = date_range('20130101',periods=3)
   dti.tolist()

   (dti + tdi).tolist()
   (dti - tdi).tolist()

- iteration of a ``Series`` e.g. ``list(Series(...))`` of ``timedelta64[ns]`` would prior to v0.15.0 return ``np.timedelta64`` for each element. These will now be wrapped in ``Timedelta``.

.. _whatsnew_0150.prior_deprecations:

Prior Version Deprecations/Changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

There are no prior version deprecations that are taking effect as of 0.15.0.

.. _whatsnew_0150.deprecations:

Deprecations
~~~~~~~~~~~~

- The ``convert_dummies`` method has been deprecated in favor of
  ``get_dummies`` (:issue:`8140`)
- The ``infer_dst`` argument in ``tz_localize`` will be deprecated in favor of
  ``ambiguous`` to allow for more flexibility in dealing with DST transitions.
  Replace ``infer_dst=True`` with ``ambiguous='infer'`` for the same behavior (:issue:`7943`).
  See :ref:`the docs<timeseries.timezone_ambiguous>` for more details.

.. _whatsnew_0150.enhancements:

Enhancements
~~~~~~~~~~~~

- Added support for a ``chunksize`` parameter to ``to_sql`` function.  This allows DataFrame to be written in chunks and avoid packet-size overflow errors (:issue:`8062`)
- Added support for writing ``datetime.date`` and ``datetime.time`` object columns with ``to_sql`` (:issue:`6932`).
- Added support for specifying a ``schema`` to read from/write to with ``read_sql_table`` and ``to_sql`` (:issue:`7441`, :issue:`7952`).
  For example:

  .. code-block:: python

     df.to_sql('table', engine, schema='other_schema')
     pd.read_sql_table('table', engine, schema='other_schema')

- Added support for writing ``NaN`` values with ``to_sql`` (:issue:`2754`).
- Added support for writing datetime64 columns with ``to_sql`` for all database flavors (:issue:`7103`).

- Added support for bool, uint8, uint16 and uint32 datatypes in ``to_stata`` (:issue:`7097`, :issue:`7365`)

- Added ``layout`` keyword to ``DataFrame.plot`` (:issue:`6667`)
- Allow to pass multiple axes to ``DataFrame.plot``, ``hist`` and ``boxplot`` (:issue:`5353`, :issue:`6970`, :issue:`7069`)
- Added support for ``c``, ``colormap`` and ``colorbar`` arguments for
  ``DataFrame.plot`` with ``kind='scatter'`` (:issue:`7780`)


- ``PeriodIndex`` supports ``resolution`` as the same as ``DatetimeIndex`` (:issue:`7708`)
- ``pandas.tseries.holiday`` has added support for additional holidays and ways to observe holidays (:issue:`7070`)
- ``pandas.tseries.holiday.Holiday`` now supports a list of offsets in Python3 (:issue:`7070`)
- ``pandas.tseries.holiday.Holiday`` now supports a days_of_week parameter (:issue:`7070`)
- ``GroupBy.nth()`` now supports selecting multiple nth values (:issue:`7910`)

  .. ipython:: python

    business_dates = date_range(start='4/1/2014', end='6/30/2014', freq='B')
    df = DataFrame(1, index=business_dates, columns=['a', 'b'])
    # get the first, 4th, and last date index for each month
    df.groupby((df.index.year, df.index.month)).nth([0, 3, -1])

- ``Period`` and ``PeriodIndex`` supports addition/subtraction with ``timedelta``-likes (:issue:`7966`)

  If ``Period`` freq is ``D``, ``H``, ``T``, ``S``, ``L``, ``U``, ``N``, ``timedelta``-like can be added if the result can have same freq. Otherwise, only the same ``offsets`` can be added.

  .. ipython:: python

    idx = pd.period_range('2014-07-01 09:00', periods=5, freq='H')
    idx
    idx + pd.offsets.Hour(2)
    idx + timedelta(minutes=120)
    idx + np.timedelta64(7200, 's')

    idx = pd.period_range('2014-07', periods=5, freq='M')
    idx
    idx + pd.offsets.MonthEnd(3)



- The ``get_dummies`` method can now be used on DataFrames. By default only
  catagorical columns are encoded as 0's and 1's, while other columns are
  left untouched.

  .. ipython:: python

    df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
                       'C': [1, 2, 3]})
    pd.get_dummies(df)








- ``tz_localize`` now accepts the ``ambiguous`` keyword which allows for passing an array of bools
  indicating whether the date belongs in DST or not, 'NaT' for setting transition times to NaT,
  'infer' for inferring DST/non-DST, and 'raise' (default) for an AmbiguousTimeError to be raised (:issue:`7943`).
  See :ref:`the docs<timeseries.timezone_ambiguous>` for more details.






.. _whatsnew_0150.performance:

Performance
~~~~~~~~~~~

- Performance improvements in ``DatetimeIndex.__iter__`` to allow faster iteration (:issue:`7683`)
- Performance improvements in ``Period`` creation (and ``PeriodIndex`` setitem) (:issue:`5155`)
- Improvements in Series.transform for significant performance gains (revised) (:issue:`6496`)
- Performance improvements in ``StataReader`` when reading large files (:issue:`8040`, :issue:`8073`)
- Performance improvements in ``StataWriter`` when writing large files (:issue:`8079`)
- Performance and memory usage improvements in multi-key ``groupby`` (:issue:`8128`)
- Performance improvements in groupby ``.agg`` and ``.apply`` where builtins max/min were not mapped to numpy/cythonized versions (:issue:`7722`)
- Performance improvement in writing to sql (``to_sql``) of up to 50% (:issue:`8208`).




















.. _whatsnew_0150.bug_fixes:

Bug Fixes
~~~~~~~~~

- Bug in ``read_csv`` where ``squeeze=True`` would return a view (:issue:`8217`)
- Bug in checking of table name in ``read_sql`` in certain cases (:issue:`7826`).
- Bug in ``DataFrame.groupby`` where ``Grouper`` does not recognize level when frequency is specified (:issue:`7885`)
- Bug in multiindexes dtypes getting mixed up when DataFrame is saved to SQL table (:issue:`8021`)
- 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 ``to_datetime`` when ``format='%Y%m%d'`` and ``coerce=True`` are specified, where previously an object array was returned (rather than
  a coerced time-series with ``NaT``), (:issue:`7930`)
- Bug in ``DatetimeIndex`` and ``PeriodIndex`` in-place addition and subtraction cause different result from normal one (:issue:`6527`)
- Bug in adding and subtracting ``PeriodIndex`` with ``PeriodIndex`` raise ``TypeError`` (:issue:`7741`)
- Bug in ``combine_first`` with ``PeriodIndex`` data raises ``TypeError`` (:issue:`3367`)
- Bug in multi-index slicing with missing indexers (:issue:`7866`)
- Bug in multi-index slicing with various edge cases (:issue:`8132`)
- Regression in multi-index indexing with a non-scalar type object (:issue:`7914`)
- Bug in Timestamp comparisons with ``==`` and dtype of int64 (:issue:`8058`)
- Bug in pickles contains ``DateOffset`` may raise ``AttributeError`` when ``normalize`` attribute is reffered internally (:issue:`7748`)
- Bug in Panel when using ``major_xs`` and ``copy=False`` is passed (deprecation warning fails because of missing ``warnings``) (:issue:`8152`).
- 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 putting a ``PeriodIndex`` into a ``Series`` would convert to ``int64`` dtype, rather than ``object`` of ``Periods`` (:issue:`7932`)
- Bug in HDFStore iteration when passing a where (:issue:`8014`)
- Bug in DataFrameGroupby.transform when transforming with a passed non-sorted key (:issue:`8046`)
- Bug in repeated timeseries line and area plot may result in ``ValueError`` or incorrect kind (:issue:`7733`)
- Bug in inference in a MultiIndex with ``datetime.date`` inputs (:issue:`7888`)
- Bug in ``get`` where an ``IndexError`` would not cause the default value to be returned (:issue:`7725`)
- Bug in ``offsets.apply``, ``rollforward`` and ``rollback`` may reset nanosecond (:issue:`7697`)
- Bug in ``offsets.apply``, ``rollforward`` and ``rollback`` may raise ``AttributeError`` if ``Timestamp`` has ``dateutil`` tzinfo (:issue:`7697`)
- Bug in sorting a multi-index frame with a Float64Index (:issue:`8017`)

- Bug in ``is_superperiod`` and ``is_subperiod`` cannot handle higher frequencies than ``S`` (:issue:`7760`, :issue:`7772`, :issue:`7803`)
- Bug in 32-bit platforms with ``Series.shift`` (:issue:`8129`)
- Bug in ``PeriodIndex.unique`` returns int64 ``np.ndarray`` (:issue:`7540`)

- 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`)
- Bug in ``StataReader`` which did not read variable labels in 117 files due to difference between Stata documentation and implementation (:issue:`7816`)
- Bug in ``StataReader`` where strings were always converted to 244 characters-fixed width irrespective of underlying string size (:issue:`7858`)

- Bug in :func:`expanding_cov`, :func:`expanding_corr`, :func:`rolling_cov`, :func:`rolling_cor`, :func:`ewmcov`, and :func:`ewmcorr`
  returning results with columns sorted by name and producing an error for non-unique columns;
  now handles non-unique columns and returns columns in original order
  (except for the case of two DataFrames with ``pairwise=False``, where behavior is unchanged) (:issue:`7542`)
- Bug in :func:`rolling_count` and ``expanding_*`` functions unnecessarily producing error message for zero-length data (:issue:`8056`)
- Bug in :func:`rolling_apply` and :func:`expanding_apply` interpreting ``min_periods=0`` as ``min_periods=1`` (:issue:`8080`)
- Bug in :func:`expanding_std` and :func:`expanding_var` for a single value producing a confusing error message (:issue:`7900`)
- Bug in :func:`rolling_std` and :func:`rolling_var` for a single value producing ``0`` rather than ``NaN`` (:issue:`7900`)

- Bug in :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, and :func:`ewmcov`
  calculation of de-biasing factors when ``bias=False`` (the default).
  Previously an incorrect constant factor was used, based on ``adjust=True``, ``ignore_na=True``,
  and an infinite number of observations.
  Now a different factor is used for each entry, based on the actual weights
  (analogous to the usual ``N/(N-1)`` factor).
  In particular, for a single point a value of ``NaN`` is returned when ``bias=False``,
  whereas previously a value of (approximately) ``0`` was returned.

  For example, consider the following pre-0.15.0 results for ``ewmvar(..., bias=False)``,
  and the corresponding debiasing factors:

  .. ipython:: python

     s = Series([1., 2., 0., 4.])

  .. code-block:: python

	 In [69]: ewmvar(s, com=2., bias=False)
	 Out[69]:
	 0   -2.775558e-16
	 1    3.000000e-01
	 2    9.556787e-01
	 3    3.585799e+00
	 dtype: float64

	 In [70]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)
	 Out[70]:
	 0    1.25
	 1    1.25
	 2    1.25
	 3    1.25
	 dtype: float64

  Note that entry ``0`` is approximately 0, and the debiasing factors are a constant 1.25.
  By comparison, the following 0.15.0 results have a ``NaN`` for entry ``0``,
  and the debiasing factors are decreasing (towards 1.25):

  .. ipython:: python

     ewmvar(s, com=2., bias=False)
     ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)

  See :ref:`Exponentially weighted moment functions <stats.moments.exponentially_weighted>` for details. (:issue:`7912`)

- Bug in ``DataFrame.plot`` and ``Series.plot`` may ignore ``rot`` and ``fontsize`` keywords (:issue:`7844`)


- Bug in ``DatetimeIndex.value_counts`` doesn't preserve tz  (:issue:`7735`)
- Bug in ``PeriodIndex.value_counts`` results in ``Int64Index`` (:issue:`7735`)
- Bug in ``DataFrame.join`` when doing left join on index and there are multiple matches (:issue:`5391`)



- Bug in ``GroupBy.transform()`` where int groups with a transform that
  didn't preserve the index were incorrectly truncated (:issue:`7972`).

- Bug in ``groupby`` where callable objects without name attributes would take the wrong path,
  and produce a ``DataFrame`` instead of a ``Series`` (:issue:`7929`)

- Bug in ``groupby`` error message when a DataFrame grouping column is duplicated (:issue:`7511`)

- Bug in ``read_html`` where the ``infer_types`` argument forced coercion of
  date-likes incorrectly (:issue:`7762`, :issue:`7032`).


- Bug in ``Series.str.cat`` with an index which was filtered as to not include the first item (:issue:`7857`)


- Bug in ``Timestamp`` cannot parse ``nanosecond`` from string (:issue:`7878`)
- Bug in ``Timestamp`` with string offset and ``tz`` results incorrect (:issue:`7833`)

- Bug in ``tslib.tz_convert`` and ``tslib.tz_convert_single`` may return different results (:issue:`7798`)
- Bug in ``DatetimeIndex.intersection`` of non-overlapping timestamps with tz raises ``IndexError`` (:issue:`7880`)



- Bug in ``GroupBy.filter()`` where fast path vs. slow path made the filter
  return a non scalar value that appeared valid but wasn't (:issue:`7870`).
- Bug in ``date_range()``/``DatetimeIndex()`` when the timezone was inferred from input dates yet incorrect
  times were returned when crossing DST boundaries (:issue:`7835`, :issue:`7901`).
- Bug in ``to_excel()`` where a negative sign was being prepended to positive infinity and was absent for negative infinity (:issue`7949`)
- Bug in area plot draws legend with incorrect ``alpha`` when ``stacked=True`` (:issue:`8027`)
- ``Period`` and ``PeriodIndex`` addition/subtraction with ``np.timedelta64`` results in incorrect internal representations (:issue:`7740`)
- Bug in ``Holiday`` with no offset or observance (:issue:`7987`)

- Bug in ``DataFrame.to_latex`` formatting when columns or index is a ``MultiIndex`` (:issue:`7982`).

- Bug in ``DateOffset`` around Daylight Savings Time produces unexpected results (:issue:`5175`).





- Bug in ``DataFrame.shift`` where empty columns would throw ``ZeroDivisionError`` on numpy 1.7 (:issue:`8019`)





- Bug in installation where ``html_encoding/*.html`` wasn't installed and
  therefore some tests were not running correctly (:issue:`7927`).

- Bug in ``read_html`` where ``bytes`` objects were not tested for in
  ``_read`` (:issue:`7927`).

- Bug in ``DataFrame.stack()`` when one of the column levels was a datelike (:issue:`8039`)
- Bug in broadcasting numpy scalars with DataFrames (:issue:`8116`)


- Bug in ``pivot_table`` performed with nameless ``index`` and ``columns`` raises ``KeyError`` (:issue:`8103`)

- Bug in ``DataFrame.plot(kind='scatter')`` draws points and errorbars with different colors when the color is specified by ``c`` keyword (:issue:`8081`)




- Bug in ``Float64Index`` where ``iat`` and ``at`` were not testing and were
  failing (:issue:`8092`).
- Bug in ``DataFrame.boxplot()`` where y-limits were not set correctly when
  producing multiple axes (:issue:`7528`, :issue:`5517`).

- Bug in ``read_csv`` where line comments were not handled correctly given
  a custom line terminator or ``delim_whitespace=True`` (:issue:`8122`).

- Bug in ``read_html`` where empty tables caused a ``StopIteration`` (:issue:`7575`)

- Bug in accessing groups from a ``GroupBy`` when the original grouper
  was a tuple (:issue:`8121`).

- Bug with kde plot and NaNs (:issue:`8182`)
- Bug in ``GroupBy.count`` with float32 data type were nan values were not excluded (:issue:`8169`).
- Bug with stacked barplots and NaNs (:issue:`8175`).



- Bug in interpolation methods with the ``limit`` keyword when no values needed interpolating (:issue:`7173`).
- Bug where ``col_space`` was ignored in ``DataFrame.to_string()`` when ``header=False`` (:issue:`8230`).
- Bug with ``DatetimeIndex.asof`` incorrectly matching partial strings and returning the wrong date (:issue:`8245`).
- Bug in plotting methods modifying the global matplotlib rcParams (:issue:`8242`).
- Bug in ``DataFrame.__setitem__`` that caused errors when setting a dataframe column to a sparse array (:issue:`8131`)