v0.19.0.txt

.. _whatsnew_0190:

v0.19.0 (August ??, 2016)
-------------------------

This is a major release from 0.18.1 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 >= 0.19.0 will no longer silence numpy ufunc warnings upon import, see :ref:`here <whatsnew_0190.errstate>`.

Highlights include:

- :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0190.enhancements.asof_merge>`
- ``.rolling()`` are now time-series aware, see :ref:`here <whatsnew_0190.enhancements.rolling_ts>`
- pandas development api, see :ref:`here <whatsnew_0190.dev_api>`
- ``PeriodIndex`` now has its own ``period`` dtype, and changed to be more consistent with other ``Index`` classes. See ref:`here <whatsnew_0190.api.period>`
- Sparse data structures now gained enhanced support of ``int`` and ``bool`` dtypes, see :ref:`here <whatsnew_0190.sparse>`

.. contents:: What's new in v0.19.0
    :local:
    :backlinks: none

.. _whatsnew_0190.new_features:

New features
~~~~~~~~~~~~

.. _whatsnew_0190.dev_api:

pandas development API
^^^^^^^^^^^^^^^^^^^^^^

As part of making pandas APi more uniform and accessible in the future, we have created a standard
sub-package of pandas, ``pandas.api`` to hold public API's. We are starting by exposing type
introspection functions in ``pandas.api.types``. More sub-packages and officially sanctioned API's
will be published in future versions of pandas (:issue:`13147`, :issue:`13634`)

The following are now part of this API:

.. ipython:: python

   import pprint
   from pandas.api import types
   funcs = [ f for f in dir(types) if not f.startswith('_') ]
   pprint.pprint(funcs)

.. note::

   Calling these functions from the internal module ``pandas.core.common`` will now show a ``DeprecationWarning`` (:issue:`13990`)

.. _whatsnew_0190.enhancements.asof_merge:

``merge_asof`` for asof-style time-series joining
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A long-time requested feature has been added through the :func:`merge_asof` function, to
support asof style joining of time-series. (:issue:`1870`, :issue:`13695`, :issue:`13709`, :issue:`13902`). Full documentation is
:ref:`here <merging.merge_asof>`

The :func:`merge_asof` performs an asof merge, which is similar to a left-join
except that we match on nearest key rather than equal keys.

.. ipython:: python

   left = pd.DataFrame({'a': [1, 5, 10],
                        'left_val': ['a', 'b', 'c']})
   right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
                        'right_val': [1, 2, 3, 6, 7]})

   left
   right

We typically want to match exactly when possible, and use the most
recent value otherwise.

.. ipython:: python

   pd.merge_asof(left, right, on='a')

We can also match rows ONLY with prior data, and not an exact match.

.. ipython:: python

   pd.merge_asof(left, right, on='a', allow_exact_matches=False)


In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them.
This also illustrates using the ``by`` parameter to group data before merging.

.. ipython:: python

   trades = pd.DataFrame({
       'time': pd.to_datetime(['20160525 13:30:00.023',
                               '20160525 13:30:00.038',
                               '20160525 13:30:00.048',
                               '20160525 13:30:00.048',
                               '20160525 13:30:00.048']),
       'ticker': ['MSFT', 'MSFT',
                  'GOOG', 'GOOG', 'AAPL'],
       'price': [51.95, 51.95,
                 720.77, 720.92, 98.00],
       'quantity': [75, 155,
                    100, 100, 100]},
       columns=['time', 'ticker', 'price', 'quantity'])

   quotes = pd.DataFrame({
       'time': pd.to_datetime(['20160525 13:30:00.023',
                               '20160525 13:30:00.023',
                               '20160525 13:30:00.030',
                               '20160525 13:30:00.041',
                               '20160525 13:30:00.048',
                               '20160525 13:30:00.049',
                               '20160525 13:30:00.072',
                               '20160525 13:30:00.075']),
       'ticker': ['GOOG', 'MSFT', 'MSFT',
                  'MSFT', 'GOOG', 'AAPL', 'GOOG',
                  'MSFT'],
       'bid': [720.50, 51.95, 51.97, 51.99,
               720.50, 97.99, 720.50, 52.01],
       'ask': [720.93, 51.96, 51.98, 52.00,
               720.93, 98.01, 720.88, 52.03]},
       columns=['time', 'ticker', 'bid', 'ask'])

.. ipython:: python

   trades
   quotes

An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and
in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except
that forward filling happens automatically taking the most recent non-NaN value.

.. ipython:: python

   pd.merge_asof(trades, quotes,
                 on='time',
                 by='ticker')

This returns a merged DataFrame with the entries in the same order as the original left
passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged.

.. _whatsnew_0190.enhancements.rolling_ts:

``.rolling()`` are now time-series aware
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``.rolling()`` objects are now time-series aware and can accept a time-series offset (or convertible) for the ``window`` argument (:issue:`13327`, :issue:`12995`)
See the full documentation :ref:`here <stats.moments.ts>`.

.. ipython:: python

   dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
                      index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
   dft

This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.

.. ipython:: python

   dft.rolling(2).sum()
   dft.rolling(2, min_periods=1).sum()

Specifying an offset allows a more intuitive specification of the rolling frequency.

.. ipython:: python

   dft.rolling('2s').sum()

Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.

.. ipython:: python


   dft = DataFrame({'B': [0, 1, 2, np.nan, 4]},
                   index = pd.Index([pd.Timestamp('20130101 09:00:00'),
                                     pd.Timestamp('20130101 09:00:02'),
                                     pd.Timestamp('20130101 09:00:03'),
                                     pd.Timestamp('20130101 09:00:05'),
                                     pd.Timestamp('20130101 09:00:06')],
                                    name='foo'))

   dft
   dft.rolling(2).sum()

Using the time-specification generates variable windows for this sparse data.

.. ipython:: python

   dft.rolling('2s').sum()

Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the
default of the index) in a DataFrame.

.. ipython:: python

   dft = dft.reset_index()
   dft
   dft.rolling('2s', on='foo').sum()

.. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support:

``read_csv`` has improved support for duplicate column names
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. ipython:: python
   :suppress:

   from pandas.compat import StringIO

:ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether
they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`)

.. ipython :: python

   data = '0,1,2\n3,4,5'
   names = ['a', 'b', 'a']

Previous behaviour:

.. code-block:: ipython

   In [2]: pd.read_csv(StringIO(data), names=names)
   Out[2]:
      a  b  a
   0  2  1  2
   1  5  4  5

The first 'a' column contains the same data as the second 'a' column, when it should have
contained the array ``[0, 3]``.

New behaviour:

.. ipython :: python

   In [2]: pd.read_csv(StringIO(data), names=names)


.. _whatsnew_0190.enhancements.read_csv_categorical:

:func:`read_csv` supports parsing ``Categorical`` directly
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :func:`read_csv` function now supports parsing a ``Categorical`` column when
specified as a dtype (:issue:`10153`).  Depending on the structure of the data,
this can result in a faster parse time and lower memory usage compared to
converting to ``Categorical`` after parsing.  See the io :ref:`docs here <io.categorical>`

.. ipython:: python

   data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'

   pd.read_csv(StringIO(data))
   pd.read_csv(StringIO(data)).dtypes
   pd.read_csv(StringIO(data), dtype='category').dtypes

Individual columns can be parsed as a ``Categorical`` using a dict specification

.. ipython:: python

   pd.read_csv(StringIO(data), dtype={'col1': 'category'}).dtypes

.. note::

   The resulting categories will always be parsed as strings (object dtype).
   If the categories are numeric they can be converted using the
   :func:`to_numeric` function, or as appropriate, another converter
   such as :func:`to_datetime`.

   .. ipython:: python

      df = pd.read_csv(StringIO(data), dtype='category')
      df.dtypes
      df['col3']
      df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories)
      df['col3']

.. _whatsnew_0190.enhancements.semi_month_offsets:

Semi-Month Offsets
^^^^^^^^^^^^^^^^^^

Pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonthBegin`` ('SMS').
These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively.
(:issue:`1543`)

.. ipython:: python

    from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin

SemiMonthEnd:

.. ipython:: python

    Timestamp('2016-01-01') + SemiMonthEnd()

    pd.date_range('2015-01-01', freq='SM', periods=4)

SemiMonthBegin:

.. ipython:: python

    Timestamp('2016-01-01') + SemiMonthBegin()

    pd.date_range('2015-01-01', freq='SMS', periods=4)

Using the anchoring suffix, you can also specify the day of month to use instead of the 15th.

.. ipython:: python

    pd.date_range('2015-01-01', freq='SMS-16', periods=4)

    pd.date_range('2015-01-01', freq='SM-14', periods=4)

.. _whatsnew_0190.enhancements.index:

New Index methods
^^^^^^^^^^^^^^^^^

Following methods and options are added to ``Index`` to be more consistent with ``Series`` and ``DataFrame``.

``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`)

.. ipython:: python

   idx = pd.Index(['a', 'b', 'c'])
   idx.where([True, False, True])


``Index`` now supports ``.dropna`` to exclude missing values (:issue:`6194`)

.. ipython:: python

   idx = pd.Index([1, 2, np.nan, 4])
   idx.dropna()

For ``MultiIndex``, values are dropped if any level is missing by default. Specifying
``how='all'`` only drops values where all levels are missing.

.. ipython:: python

   midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
                                       [1, 2, np.nan, np.nan]])
   midx
   midx.dropna()
   midx.dropna(how='all')

``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, the see :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)

.. ipython:: python

   idx = pd.Index(["a1a2", "b1", "c1"])
   idx.str.extractall("[ab](?P<digit>\d)")

``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`)

.. _whatsnew_0190.gbq:

Google BigQuery Enhancements
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- The :func:`pandas.io.gbq.read_gbq` method has gained the ``dialect`` argument to allow users to specify whether to use BigQuery's legacy SQL or BigQuery's standard SQL. See the :ref:`docs <io.bigquery_reader>` for more details (:issue:`13615`).

.. _whatsnew_0190.errstate:

Fine-grained numpy errstate
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Previous versions of pandas would permanently silence numpy's ufunc error handling when ``pandas`` was imported. Pandas did this in order to silence the warnings that would arise from using numpy ufuncs on missing data, which are usually represented as ``NaN`` s. Unfortunately, this silenced legitimate warnings arising in non-pandas code in the application. Starting with 0.19.0, pandas will use the ``numpy.errstate`` context manager to silence these warnings in a more fine-grained manner, only around where these operations are actually used in the pandas codebase. (:issue:`13109`, :issue:`13145`)

After upgrading pandas, you may see *new* ``RuntimeWarnings`` being issued from your code. These are likely legitimate, and the underlying cause likely existed in the code when using previous versions of pandas that simply silenced the warning. Use `numpy.errstate <http://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html>`__ around the source of the ``RuntimeWarning`` to control how these conditions are handled.

.. _whatsnew_0190.get_dummies_dtypes:

get_dummies dtypes
^^^^^^^^^^^^^^^^^^

The ``pd.get_dummies`` function now returns dummy-encoded columns as small integers, rather than floats (:issue:`8725`)

Previous behaviour:

.. code-block:: ipython

   In [1]:    pd.get_dummies(['a', 'b', 'a', 'c']).dtypes

   Out[1]:
   a    float64
   b    float64
   c    float64
   dtype: object

New Behavior:

.. ipython:: python

   pd.get_dummies(['a', 'b', 'a', 'c']).dtypes


.. _whatsnew_0190.enhancements.other:

Other enhancements
^^^^^^^^^^^^^^^^^^

- The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch [the application default credentials](https://developers.google.com/identity/protocols/application-default-credentials). See the :ref:`docs <io.bigquery_authentication>` for more details (:issue:`13577`).

- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`)
- ``pd.to_numeric()`` now accepts a ``downcast`` parameter, which will downcast the data if possible to smallest specified numerical dtype (:issue:`13352`)

  .. ipython:: python

     s = ['1', 2, 3]
     pd.to_numeric(s, downcast='unsigned')
     pd.to_numeric(s, downcast='integer')

- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`)

- ``Timestamp`` can now accept positional and keyword parameters similar to :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`)

  .. ipython:: python

    pd.Timestamp(2012, 1, 1)

    pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30)

- the ``.resample()`` function now accepts a ``on=`` or ``level=`` parameter for resampling on a datetimelike column or ``MultiIndex`` level (:issue:`13500`)

  .. ipython:: python

     df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
                        'a': np.arange(5)},
                       index=pd.MultiIndex.from_arrays([
                                [1,2,3,4,5],
                                pd.date_range('2015-01-01', freq='W', periods=5)],
                            names=['v','d']))
     df
     df.resample('M', on='date').sum()
     df.resample('M', level='d').sum()

- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``decimal`` option (:issue:`12933`)
- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`)
- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`)
- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`)

- The ``pd.read_html()`` has gained support for the ``na_values``, ``converters``, ``keep_default_na``  options (:issue:`13461`)

- ``Categorical.astype()`` now accepts an optional boolean argument ``copy``, effective when dtype is categorical (:issue:`13209`)
- ``DataFrame`` has gained the ``.asof()`` method to return the last non-NaN values according to the selected subset (:issue:`13358`)
- The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`)
- ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`)
- A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`, :issue:`:13763`, issue:`13846`)
- ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`)
- ``DataFrame.to_sql()`` now allows a single value as the SQL type for all columns (:issue:`11886`).
- ``Series.append`` now supports the ``ignore_index`` option (:issue:`13677`)
- ``.to_stata()`` and ``StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13536`)
- ``.to_stata()`` and ``StataWriter`` will automatically convert ``datetime64[ns]`` columns to Stata format ``%tc``, rather than raising a ``ValueError`` (:issue:`12259`)
- ``read_stata()`` and ``StataReader`` raise with a more explicit error message when reading Stata files with repeated value labels when ``convert_categoricals=True`` (:issue:`13923`)
- ``DataFrame.style`` will now render sparsified MultiIndexes (:issue:`11655`)
- ``DataFrame.style`` will now show column level names (e.g. ``DataFrame.columns.names``) (:issue:`13775`)
- ``DataFrame`` has gained support to re-order the columns based on the values
  in a row using ``df.sort_values(by='...', axis=1)`` (:issue:`10806`)

  .. ipython:: python

     df = pd.DataFrame({'A': [2, 7], 'B': [3, 5], 'C': [4, 8]},
                       index=['row1', 'row2'])
     df.sort_values(by='row2', axis=1)

- Added documentation to :ref:`I/O<io.dtypes>` regarding the perils of reading in columns with mixed dtypes and how to handle it (:issue:`13746`)
- :meth:`~DataFrame.to_html` now has a ``border`` argument to control the value in the opening ``<table>`` tag. The default is the value of the ``html.border`` option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter's CSS includes a border-width attribute, the visual effect is the same. (:issue:`11563`).
- Raise ``ImportError`` in the sql functions when ``sqlalchemy`` is not installed and a connection string is used (:issue:`11920`).
- Compatibility with matplotlib 2.0. Older versions of pandas should also work with matplotlib 2.0 (:issue:`13333`)

.. _whatsnew_0190.api:


API changes
~~~~~~~~~~~


- ``Timestamp.to_pydatetime`` will issue a ``UserWarning`` when ``warn=True``, and the instance has a non-zero number of nanoseconds (:issue:`14101`)
- ``Panel.to_sparse`` will raise a ``NotImplementedError`` exception when called (:issue:`13778`)
- ``Index.reshape`` will raise a ``NotImplementedError`` exception when called (:issue:`12882`)
- ``pd.read_csv()``, ``pd.read_table()``, and ``pd.read_hdf()`` raise the builtin ``FileNotFoundError`` exception for Python 3.x when called on a nonexistent file, and this is back-ported as IOError in Python 2.x (:issue:`14086`)
- Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception  (:issue:`10001`)
- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules.  New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`)
- An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`)
- Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`)
- ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`)
- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`)
- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`)
- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`)
- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`)
- Passing ``Period`` with multiple frequencies to normal ``Index`` now returns ``Index`` with ``object`` dtype (:issue:`13664`)
- ``PeriodIndex.fillna`` with ``Period`` has different freq now coerces to ``object`` dtype (:issue:`13664`)
- More informative exceptions are passed through the csv parser. The exception type would now be the original exception type instead of ``CParserError``. (:issue:`13652`)
- ``astype()`` will now accept a dict of column name to data types mapping as the ``dtype`` argument. (:issue:`12086`)
- The ``pd.read_json`` and ``DataFrame.to_json`` has gained support for reading and writing json lines with ``lines`` option see :ref:`Line delimited json <io.jsonl>` (:issue:`9180`)
- ``pd.Timedelta(None)`` is now accepted and will return ``NaT``, mirroring ``pd.Timestamp`` (:issue:`13687`)
- ``Timestamp``, ``Period``, ``DatetimeIndex``, ``PeriodIndex`` and ``.dt`` accessor have gained a ``.is_leap_year`` property to check whether the date belongs to a leap year. (:issue:`13727`)
- ``pd.read_hdf`` will now raise a ``ValueError`` instead of ``KeyError``, if a mode other than ``r``, ``r+`` and ``a`` is supplied. (:issue:`13623`)
- ``pd.read_csv()`` in the C engine will now issue a ``ParserWarning`` or raise a ``ValueError`` when ``sep`` encoded is more than one character long (:issue:`14065`)
- ``DataFrame.values`` will now return ``float64`` with a ``DataFrame`` of mixed ``int64`` and ``uint64`` dtypes, conforming to ``np.find_common_type`` (:issue:`10364`, :issue:`13917`)
- ``Series.unique()`` with datetime and timezone now returns return array of ``Timestamp`` with timezone (:issue:`13565`)


.. _whatsnew_0190.api.tolist:

``Series.tolist()`` will now return Python types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behaviour (:issue:`10904`)


.. ipython:: python

   s = pd.Series([1,2,3])
   type(s.tolist()[0])

Previous Behavior:

.. code-block:: ipython

   In [7]: type(s.tolist()[0])
   Out[7]:
    <class 'numpy.int64'>

New Behavior:

.. ipython:: python

   type(s.tolist()[0])

.. _whatsnew_0190.api.series_ops:

``Series`` operators for different indexes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Following ``Series`` operators has been changed to make all operators consistent,
including ``DataFrame`` (:issue:`1134`, :issue:`4581`, :issue:`13538`)

- ``Series`` comparison operators now raise ``ValueError`` when ``index`` are different.
- ``Series`` logical operators align both ``index``.

.. warning::
   Until 0.18.1, comparing ``Series`` with the same length has been succeeded even if
   these ``index`` are different (the result ignores ``index``). As of 0.19.0, it raises ``ValueError`` to be more strict. This section also describes how to keep previous behaviour or align different indexes using flexible comparison methods like ``.eq``.


As a result, ``Series`` and ``DataFrame`` operators behave as below:

Arithmetic operators
""""""""""""""""""""

Arithmetic operators align both ``index`` (no changes).

.. ipython:: python

   s1 = pd.Series([1, 2, 3], index=list('ABC'))
   s2 = pd.Series([2, 2, 2], index=list('ABD'))
   s1 + s2

   df1 = pd.DataFrame([1, 2, 3], index=list('ABC'))
   df2 = pd.DataFrame([2, 2, 2], index=list('ABD'))
   df1 + df2

Comparison operators
""""""""""""""""""""

Comparison operators raise ``ValueError`` when ``index`` are different.

Previous Behavior (``Series``):

``Series`` compares values ignoring ``index`` as long as both lengthes are the same.

.. code-block:: ipython

   In [1]: s1 == s2
   Out[1]:
   A    False
   B     True
   C    False
   dtype: bool

New Behavior (``Series``):

.. code-block:: ipython

   In [2]: s1 == s2
   Out[2]:
   ValueError: Can only compare identically-labeled Series objects

.. note::
   To achieve the same result as previous versions (compare values based on locations ignoring ``index``), compare both ``.values``.

   .. ipython:: python

      s1.values == s2.values

   If you want to compare ``Series`` aligning its ``index``, see flexible comparison methods section below.

Current Behavior (``DataFrame``, no change):

.. code-block:: ipython

   In [3]: df1 == df2
   Out[3]:
   ValueError: Can only compare identically-labeled DataFrame objects

Logical operators
"""""""""""""""""

Logical operators align both ``index``.

Previous Behavior (``Series``):

Only left hand side ``index`` is kept.

.. code-block:: ipython

   In [4]: s1 = pd.Series([True, False, True], index=list('ABC'))
   In [5]: s2 = pd.Series([True, True, True], index=list('ABD'))
   In [6]: s1 & s2
   Out[6]:
   A     True
   B    False
   C    False
   dtype: bool

New Behavior (``Series``):

.. ipython:: python

   s1 = pd.Series([True, False, True], index=list('ABC'))
   s2 = pd.Series([True, True, True], index=list('ABD'))
   s1 & s2

.. note::
   ``Series`` logical operators fill ``NaN`` result with ``False``.

.. note::
   To achieve the same result as previous versions (compare values based on locations ignoring ``index``), compare both ``.values``.

   .. ipython:: python

      s1.values & s2.values

Current Behavior (``DataFrame``, no change):

.. ipython:: python

   df1 = pd.DataFrame([True, False, True], index=list('ABC'))
   df2 = pd.DataFrame([True, True, True], index=list('ABD'))
   df1 & df2

Flexible comparison methods
"""""""""""""""""""""""""""

``Series`` flexible comparison methods like ``eq``, ``ne``, ``le``, ``lt``, ``ge`` and ``gt`` now align both ``index``. Use these operators if you want to compare two ``Series``
which has the different ``index``.

.. ipython:: python

   s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
   s2 = pd.Series([2, 2, 2], index=['b', 'c', 'd'])
   s1.eq(s2)
   s1.ge(s2)

Previously, it worked as the same as comparison operators (see above).

.. _whatsnew_0190.api.promote:

``Series`` type promotion on assignment
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`)


.. ipython:: python

   s = pd.Series()

Previous Behavior:

.. code-block:: ipython

   In [2]: s["a"] = pd.Timestamp("2016-01-01")

   In [3]: s["b"] = 3.0
   TypeError: invalid type promotion

New Behavior:

.. ipython:: python

   s["a"] = pd.Timestamp("2016-01-01")
   s["b"] = 3.0
   s
   s.dtype

.. _whatsnew_0190.api.to_datetime_coerce:

``.to_datetime()`` changes
^^^^^^^^^^^^^^^^^^^^^^^^^^

Previously if ``.to_datetime()`` encountered mixed integers/floats and strings, but no datetimes with ``errors='coerce'`` it would convert all to ``NaT``.

Previous Behavior:

.. code-block:: ipython

   In [2]: pd.to_datetime([1, 'foo'], errors='coerce')
   Out[2]: DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None)

This will now convert integers/floats with the default unit of ``ns``.

.. ipython:: python

   pd.to_datetime([1, 'foo'], errors='coerce')

- Bug in ``pd.to_datetime()`` when passing integers or floats, and no ``unit`` and ``errors='coerce'`` (:issue:`13180`).
- Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`)
- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, and ``int16`` dtypes (:issue:`13451`)
- Bug in ``pd.to_datetime()`` raise ``AttributeError`` with NaN and the other string is not valid when errors='ignore' (:issue:`12424`)
- Bug in ``pd.to_datetime()`` did not cast floats correctly when ``unit`` was specified, resulting in truncated datetime (:issue:`13845`)

.. _whatsnew_0190.api.merging:

Merging changes
^^^^^^^^^^^^^^^

Merging will now preserve the dtype of the join keys (:issue:`8596`)

.. ipython:: python

   df1 = pd.DataFrame({'key': [1], 'v1': [10]})
   df1
   df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]})
   df2

Previous Behavior:

.. code-block:: ipython

   In [5]: pd.merge(df1, df2, how='outer')
   Out[5]:
      key    v1
   0  1.0  10.0
   1  1.0  20.0
   2  2.0  30.0

   In [6]: pd.merge(df1, df2, how='outer').dtypes
   Out[6]:
   key    float64
   v1     float64
   dtype: object

New Behavior:

We are able to preserve the join keys

.. ipython:: python

   pd.merge(df1, df2, how='outer')
   pd.merge(df1, df2, how='outer').dtypes

Of course if you have missing values that are introduced, then the
resulting dtype will be upcast, which is unchanged from previous.

.. ipython:: python

   pd.merge(df1, df2, how='outer', on='key')
   pd.merge(df1, df2, how='outer', on='key').dtypes

.. _whatsnew_0190.api.describe:

``.describe()`` changes
^^^^^^^^^^^^^^^^^^^^^^^

Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`)

.. ipython:: python

   s = pd.Series([0, 1, 2, 3, 4])
   df = pd.DataFrame([0, 1, 2, 3, 4])

Previous Behavior:

The percentiles were rounded to at most one decimal place, which could raise ``ValueError`` for a data frame if the percentiles were duplicated.

.. code-block:: ipython

   In [3]: s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
   Out[3]:
   count     5.000000
   mean      2.000000
   std       1.581139
   min       0.000000
   0.0%      0.000400
   0.1%      0.002000
   0.1%      0.004000
   50%       2.000000
   99.9%     3.996000
   100.0%    3.998000
   100.0%    3.999600
   max       4.000000
   dtype: float64

   In [4]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
   Out[4]:
   ...
   ValueError: cannot reindex from a duplicate axis

New Behavior:

.. ipython:: python

   s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
   df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])

Furthermore:

- Passing duplicated ``percentiles`` will now raise a ``ValueError``.
- Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`)

.. _whatsnew_0190.api.period:

``Period`` changes
^^^^^^^^^^^^^^^^^^

``PeriodIndex`` now has ``period`` dtype
""""""""""""""""""""""""""""""""""""""""

``PeriodIndex`` now has its own ``period`` dtype. The ``period`` dtype is a
pandas extension dtype like ``category`` or :ref:`timezone aware dtype <timeseries.timezone_series>` (``datetime64[ns, tz]``). (:issue:`13941`).
As a consequence of this change, ``PeriodIndex`` no longer has an integer dtype:

Previous Behavior:

.. code-block:: ipython

   In [1]: pi = pd.PeriodIndex(['2016-08-01'], freq='D')

   In [2]: pi
   Out[2]: PeriodIndex(['2016-08-01'], dtype='int64', freq='D')

   In [3]: pd.api.types.is_integer_dtype(pi)
   Out[3]: True

   In [4]: pi.dtype
   Out[4]: dtype('int64')

New Behavior:

.. ipython:: python

   pi = pd.PeriodIndex(['2016-08-01'], freq='D')
   pi
   pd.api.types.is_integer_dtype(pi)
   pd.api.types.is_period_dtype(pi)
   pi.dtype
   type(pi.dtype)

.. _whatsnew_0190.api.periodnat:

``Period('NaT')`` now returns ``pd.NaT``
""""""""""""""""""""""""""""""""""""""""

Previously, ``Period`` has its own ``Period('NaT')`` representation different from ``pd.NaT``. Now ``Period('NaT')`` has been changed to return ``pd.NaT``. (:issue:`12759`, :issue:`13582`)

Previous Behavior:

.. code-block:: ipython

   In [5]: pd.Period('NaT', freq='D')
   Out[5]: Period('NaT', 'D')

New Behavior:

These result in ``pd.NaT`` without providing ``freq`` option.

.. ipython:: python

   pd.Period('NaT')
   pd.Period(None)


To be compat with ``Period`` addition and subtraction, ``pd.NaT`` now supports addition and subtraction with ``int``. Previously it raises ``ValueError``.

Previous Behavior:

.. code-block:: ipython

   In [5]: pd.NaT + 1
   ...
   ValueError: Cannot add integral value to Timestamp without freq.

New Behavior:

.. ipython:: python

   pd.NaT + 1
   pd.NaT - 1

``PeriodIndex.values`` now returns array of ``Period`` object
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

``.values`` is changed to return array of ``Period`` object, rather than array
of ``int64`` (:issue:`13988`)

.. code-block:: ipython
   In [6]: pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
   In [7]: pi.values
   array([492, 493])

.. ipython:: python

   pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
   pi.values

.. _whatsnew_0190.api.difference:

``Index.difference`` and ``.symmetric_difference`` changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``Index.difference`` and ``Index.symmetric_difference`` will now, more consistently, treat ``NaN`` values as any other values. (:issue:`13514`)

.. ipython:: python

   idx1 = pd.Index([1, 2, 3, np.nan])
   idx2 = pd.Index([0, 1, np.nan])

Previous Behavior:

.. code-block:: ipython

   In [3]: idx1.difference(idx2)
   Out[3]: Float64Index([nan, 2.0, 3.0], dtype='float64')

   In [4]: idx1.symmetric_difference(idx2)
   Out[4]: Float64Index([0.0, nan, 2.0, 3.0], dtype='float64')

New Behavior:

.. ipython:: python

   idx1.difference(idx2)
   idx1.symmetric_difference(idx2)

.. _whatsnew_0190.api.unique_index:

``Index.unique`` consistently returns ``Index``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``Index.unique()`` now returns unique values as an
``Index`` of the appropriate ``dtype``. (:issue:`13395`)

Previously, most ``Index`` classes returned ``np.ndarray``, and ``DatetimeIndex``,
``TimedeltaIndex`` and ``PeriodIndex`` returned ``Index`` to keep metadata like timezone.

Previous Behavior:

.. code-block:: ipython

   In [1]: pd.Index([1, 2, 3]).unique()
   Out[1]: array([1, 2, 3])
   In [2]: pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz='Asia/Tokyo').unique()
   Out[2]: DatetimeIndex(['2011-01-01 00:00:00+09:00', '2011-01-02 00:00:00+09:00',
                          '2011-01-03 00:00:00+09:00'],
                         dtype='datetime64[ns, Asia/Tokyo]', freq=None)

New Behavior:

.. ipython:: python

   pd.Index([1, 2, 3]).unique()
   pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz='Asia/Tokyo').unique()

.. _whatsnew_0190.api.multiindex:

``MultiIndex`` constructors preserve categorical dtypes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``MultiIndex.from_arrays`` and ``MultiIndex.from_product`` will now preserve categorical dtype
in ``MultiIndex`` levels.

.. ipython:: python

   cat = pd.Categorical(['a', 'b'], categories=list("bac"))
   lvl1 = ['foo', 'bar']
   midx = pd.MultiIndex.from_arrays([cat, lvl1])

Previous Behavior:

.. code-block:: ipython

   In [4]: midx.levels[0]
   Out[4]: Index(['b', 'a', 'c'], dtype='object')

   In [5]: midx.get_level_values[0]
   Out[5]: Index(['a', 'b'], dtype='object')

New Behavior:

.. ipython:: python

   midx.levels[0]
   midx.get_level_values(0)

An analogous change has been made to ``MultiIndex.from_product``.
As a consequence, ``groupby`` and ``set_index`` also preserve categorical dtypes in indexes (:issue:`13743`).

.. ipython:: python

   df = pd.DataFrame({'A': [0, 1], 'B': [10, 11], 'C': cat})
   df_grouped = df.groupby(by=['A', 'C']).first()
   df_set_idx = df.set_index(['A', 'C'])

Previous Behavior:

.. code-block:: ipython

   In [11]: df_grouped.index.levels[1]
   Out[11]: Index(['b', 'a', 'c'], dtype='object', name='C')
   In [12]: df_grouped.reset_index().dtypes
   Out[12]:
   A      int64
   C     object
   B    float64
   dtype: object

   In [13]: df_set_idx.index.levels[1]
   Out[13]: Index(['b', 'a', 'c'], dtype='object', name='C')
   In [14]: df_set_idx.reset_index().dtypes
   Out[14]:
   A      int64
   C     object
   B      int64
   dtype: object

New Behavior:

.. ipython:: python

   df_grouped.index.levels[1]
   df_grouped.reset_index().dtypes

   df_set_idx.index.levels[1]
   df_set_idx.reset_index().dtypes

.. _whatsnew_0190.api.autogenerated_chunksize_index:

``read_csv`` will progressively enumerate chunks
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

When :func:`read_csv` is called with ``chunksize='n'`` and without specifying an index,
each chunk used to have an independently generated index from `0`` to ``n-1``.
They are now given instead a progressive index, starting from ``0`` for the first chunk,
from ``n`` for the second, and so on, so that, when concatenated, they are identical to
the result of calling :func:`read_csv` without the ``chunksize=`` argument.
(:issue:`12185`)

.. ipython :: python

   data = 'A,B\n0,1\n2,3\n4,5\n6,7'

Previous behaviour:

.. code-block:: ipython

   In [2]: pd.concat(pd.read_csv(StringIO(data), chunksize=2))
   Out[2]:
      A  B
   0  0  1
   1  2  3
   0  4  5
   1  6  7

New behaviour:

.. ipython :: python

   pd.concat(pd.read_csv(StringIO(data), chunksize=2))

.. _whatsnew_0190.sparse:

Sparse Changes
^^^^^^^^^^^^^^

These changes allow pandas to handle sparse data with more dtypes, and for work to make a smoother experience with data handling.


``int64`` and ``bool`` support enhancements
"""""""""""""""""""""""""""""""""""""""""""

Sparse data structures now gained enhanced support of ``int64`` and ``bool`` ``dtype`` (:issue:`667`, :issue:`13849`)

Previously, sparse data were ``float64`` dtype by default, even if all inputs were ``int`` or ``bool`` dtype. You had to specify ``dtype`` explicitly to create sparse data with ``int64`` dtype. Also, ``fill_value`` had to be specified explicitly becuase it's default was ``np.nan`` which doesn't appear in ``int64`` or ``bool`` data.

.. code-block:: ipython

   In [1]: pd.SparseArray([1, 2, 0, 0])
   Out[1]:
   [1.0, 2.0, 0.0, 0.0]
   Fill: nan
   IntIndex
   Indices: array([0, 1, 2, 3], dtype=int32)

   # specifying int64 dtype, but all values are stored in sp_values because
   # fill_value default is np.nan
   In [2]: pd.SparseArray([1, 2, 0, 0], dtype=np.int64)
   Out[2]:
   [1, 2, 0, 0]
   Fill: nan
   IntIndex
   Indices: array([0, 1, 2, 3], dtype=int32)

   In [3]: pd.SparseArray([1, 2, 0, 0], dtype=np.int64, fill_value=0)
   Out[3]:
   [1, 2, 0, 0]
   Fill: 0
   IntIndex
   Indices: array([0, 1], dtype=int32)

As of v0.19.0, sparse data keeps the input dtype, and assign more appropriate ``fill_value`` default (``0`` for ``int64`` dtype, ``False`` for ``bool`` dtype).

.. ipython :: python

   pd.SparseArray([1, 2, 0, 0], dtype=np.int64)
   pd.SparseArray([True, False, False, False])

See the :ref:`docs <sparse.dtype>` for more details.

Operators now preserve dtypes
"""""""""""""""""""""""""""""

- Sparse data structure now can preserve ``dtype`` after arithmetic ops (:issue:`13848`)

.. ipython:: python

   s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
   s.dtype

   s + 1

- Sparse data structure now support ``astype`` to convert internal ``dtype`` (:issue:`13900`)

.. ipython:: python

   s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
   s
   s.astype(np.int64)

``astype`` fails if data contains values which cannot be converted to specified ``dtype``.
Note that the limitation is applied to ``fill_value`` which default is ``np.nan``.

.. code-block:: ipython

   In [7]: pd.SparseSeries([1., np.nan, 2., np.nan], fill_value=np.nan).astype(np.int64)
   Out[7]:
   ValueError: unable to coerce current fill_value nan to int64 dtype

Other sparse fixes
""""""""""""""""""

- Subclassed ``SparseDataFrame`` and ``SparseSeries`` now preserve class types when slicing or transposing. (:issue:`13787`)
- ``SparseArray`` with ``bool`` dtype now supports logical (bool) operators (:issue:`14000`)
- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing may raise ``IndexError`` (:issue:`13144`)
- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing result may have normal ``Index`` (:issue:`13144`)
- Bug in ``SparseDataFrame`` in which ``axis=None`` did not default to ``axis=0`` (:issue:`13048`)
- Bug in ``SparseSeries`` and ``SparseDataFrame`` creation with ``object`` dtype may raise ``TypeError`` (:issue:`11633`)
- Bug in ``SparseDataFrame`` doesn't respect passed ``SparseArray`` or ``SparseSeries`` 's dtype and ``fill_value``  (:issue:`13866`)
- Bug in ``SparseArray`` and ``SparseSeries`` don't apply ufunc to ``fill_value`` (:issue:`13853`)
- Bug in ``SparseSeries.abs`` incorrectly keeps negative ``fill_value`` (:issue:`13853`)
- Bug in single row slicing on multi-type ``SparseDataFrame``s, types were previously forced to float (:issue:`13917`)
- Bug in ``SparseSeries`` slicing changes integer dtype to float (:issue:`8292`)
- Bug in ``SparseDataFarme`` comparison ops may raise ``TypeError`` (:issue:`13001`)
- Bug in ``SparseDataFarme.isnull`` raises ``ValueError`` (:issue:`8276`)
- Bug in ``SparseSeries`` representation with ``bool`` dtype may raise ``IndexError`` (:issue:`13110`)
- Bug in ``SparseSeries`` and ``SparseDataFrame`` of ``bool`` or ``int64`` dtype may display its values like ``float64`` dtype (:issue:`13110`)
- Bug in sparse indexing using ``SparseArray`` with ``bool`` dtype may return incorrect result  (:issue:`13985`)
- Bug in ``SparseArray`` created from ``SparseSeries`` may lose ``dtype`` (:issue:`13999`)
- Bug in ``SparseSeries`` comparison with dense returns normal ``Series`` rather than ``SparseSeries`` (:issue:`13999`)


.. _whatsnew_0190.indexer_dtype:

Indexer dtype Changes
^^^^^^^^^^^^^^^^^^^^^

.. note::

   This change only affects 64 bit python running on Windows, and only affects relatively advanced
   indexing operations

Methods such as ``Index.get_indexer`` that return an indexer array, coerce that array to a "platform int", so that it can be
directly used in 3rd party library operations like ``numpy.take``.  Previously, a platform int was defined as ``np.int_``
which corresponds to a C integer, but the correct type, and what is being used now, is ``np.intp``, which corresponds
to the C integer size that can hold a pointer. (:issue:`3033`, :issue:`13972`)

These types are the same on many platform, but for 64 bit python on Windows,
``np.int_`` is 32 bits, and ``np.intp`` is 64 bits.  Changing this behavior improves performance for many
operations on that platform.

Previous behaviour:

.. code-block:: ipython

   In [1]: i = pd.Index(['a', 'b', 'c'])

   In [2]: i.get_indexer(['b', 'b', 'c']).dtype
   Out[2]: dtype('int32')

New behaviour:

.. code-block:: ipython

   In [1]: i = pd.Index(['a', 'b', 'c'])

   In [2]: i.get_indexer(['b', 'b', 'c']).dtype
   Out[2]: dtype('int64')

.. _whatsnew_0190.deprecations:

Deprecations
~~~~~~~~~~~~
- ``Categorical.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`)
- ``Series.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`)

- ``PeriodIndex.to_datetime`` has been deprecated in favour of ``PeriodIndex.to_timestamp`` (:issue:`8254`)
- ``Timestamp.to_datetime`` has been deprecated in favour of ``Timestamp.to_pydatetime`` (:issue:`8254`)
- ``Index.to_datetime`` and ``DatetimeIndex.to_datetime`` have been deprecated in favour of ``pd.to_datetime`` (:issue:`8254`)
- ``SparseList`` has been deprecated and will be removed in a future version (:issue:`13784`)
- ``DataFrame.to_html()`` and ``DataFrame.to_latex()`` have dropped the ``colSpace`` parameter in favor of ``col_space`` (:issue:`13857`)
- ``DataFrame.to_sql()`` has deprecated the ``flavor`` parameter, as it is superfluous when SQLAlchemy is not installed (:issue:`13611`)
- ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`)
- ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`)
- ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`)
- ``skip_footer`` has been deprecated in ``pd.read_csv()`` in favor of ``skipfooter`` and will be removed in a future version (:issue:`13349`)
- top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`)
- ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`)
- ``pd.tseries.util.pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here <cookbook.pivot>` (:issue:`736`)
- ``pd.tseries.util.isleapyear`` has been deprecated and will be removed in a subsequent release. Datetime-likes now have a ``.is_leap_year`` property. (:issue:`13727`)
- ``Panel4D`` and ``PanelND`` constructors are deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion. (:issue:`13564`)
- ``pandas.tseries.frequencies.get_standard_freq`` is deprecated. Use  ``pandas.tseries.frequencies.to_offset(freq).rule_code`` instead. (:issue:`13874`)
- ``pandas.tseries.frequencies.to_offset``'s ``freqstr`` keyword is deprecated in favor of ``freq``. (:issue:`13874`)
- ``Categorical.from_array`` has been deprecated and will be removed in a future version (:issue:`13854`)

.. _whatsnew_0190.prior_deprecations:

Removal of prior version deprecations/changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The ``SparsePanel`` class has been removed (:issue:`13778`)
- The ``pd.sandbox`` module has been removed in favor of the external library ``pandas-qt`` (:issue:`13670`)
- The ``pandas.io.data`` and ``pandas.io.wb`` modules are removed in favor of
  the `pandas-datareader package <https://github.com/pydata/pandas-datareader>`__ (:issue:`13724`).
- The ``pandas.tools.rplot`` module has been removed in favor of
  the `seaborn package <https://github.com/mwaskom/seaborn>`__ (:issue:`13855`)
- ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`)
- ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`)
- ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`)
- ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`)
- ``DataFrame.to_sql()`` has dropped the ``mysql`` option for the ``flavor`` parameter (:issue:`13611`)
- ``Panel.shift()`` has dropped the ``lags`` parameter in favour of ``periods`` (:issue:`14041`)
- ``pd.Index`` has dropped the ``diff`` method in favour of ``difference`` (:issue:`13669`)

- ``pd.DataFrame`` has dropped the ``to_wide`` method in favour of ``to_panel`` (:issue:`14039`)
- ``Series.to_csv`` has dropped the ``nanRep`` parameter in favor of ``na_rep`` (:issue:`13804`)
- ``Series.xs``, ``DataFrame.xs``, ``Panel.xs``, ``Panel.major_xs``, and ``Panel.minor_xs`` have dropped the ``copy`` parameter (:issue:`13781`)
- ``str.split`` has dropped the ``return_type`` parameter in favor of ``expand`` (:issue:`13701`)
- Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`, :issue:`13868`)

  Previous Behavior:

  .. code-block:: ipython

     In [2]: pd.date_range('2016-07-01', freq='W@MON', periods=3)
     pandas/tseries/frequencies.py:465: FutureWarning: Freq "W@MON" is deprecated, use "W-MON" as alternative.
     Out[2]: DatetimeIndex(['2016-07-04', '2016-07-11', '2016-07-18'], dtype='datetime64[ns]', freq='W-MON')

  Now legacy time rules raises ``ValueError``. For the list of currently supported offsets, see :ref:`here <timeseries.offset_aliases>`

- The ``tquery`` and ``uquery`` functions in the ``pandas.io.sql`` module are removed (:issue:`5950`).


.. _whatsnew_0190.performance:

Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~

- Improved performance of sparse ``IntIndex.intersect`` (:issue:`13082`)
- Improved performance of sparse arithmetic with ``BlockIndex`` when the number of blocks are large, though recommended to use ``IntIndex`` in such cases (:issue:`13082`)
- increased performance of ``DataFrame.quantile()`` as it now operates per-block (:issue:`11623`)

- Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`)
- Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`)
- Improved performance of ``Index`` and ``Series`` ``.duplicated`` (:issue:`10235`)
- Improved performance of ``Index.difference`` (:issue:`12044`)
- Improved performance of ``RangeIndex.is_monotonic_increasing`` and ``is_monotonic_decreasing`` (:issue:`13749`)
- Improved performance of datetime string parsing in ``DatetimeIndex`` (:issue:`13692`)
- Improved performance of hashing ``Period`` (:issue:`12817`)
- Improved performance of ``factorize`` of datetime with timezone (:issue:`13750`)



.. _whatsnew_0190.bug_fixes:

Bug Fixes
~~~~~~~~~

- Bug in ``groupby().shift()``, which could cause a segfault or corruption in rare circumstances when grouping by columns with missing values (:issue:`13813`)
- Bug in ``groupby().cumsum()`` calculating ``cumprod`` when ``axis=1``. (:issue:`13994`)
- Bug in ``pd.to_timedelta()`` in which the ``errors`` parameter was not being respected (:issue:`13613`)
- Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`)
- Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`)
- Bug in area plot draws legend incorrectly if subplot is enabled or legend is moved after plot (matplotlib 1.5.0 is required to draw area plot legend properly) (issue:`9161`, :issue:`13544`)
- Bug in ``DataFrame`` assignment with an object-dtyped ``Index`` where the resultant column is mutable to the original object. (:issue:`13522`)
- Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`)
- Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`)
- Bug in ``Categorical.from_codes()`` where an unhelpful error was raised when an invalid ``ordered`` parameter was passed in (:issue:`14058`)
- Bug in ``Series`` construction from a tuple of integers on windows not returning default dtype (int64) (:issue:`13646`)

- Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`)
- Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`)

- Bug in calling ``.memory_usage()`` on object which doesn't implement (:issue:`12924`)

- Regression in ``Series.quantile`` with nans (also shows up in ``.median()`` and ``.describe()`` ); furthermore now names the ``Series`` with the quantile (:issue:`13098`, :issue:`13146`)

- Bug in ``SeriesGroupBy.transform`` with datetime values and missing groups (:issue:`13191`)
- Bug where empty ``Series`` were incorrectly coerced in datetime-like numeric operations (:issue:`13844`)

- Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError``  (:issue:`13156`)
- Bug in ``Series.str.extractall()`` with single group and quantifier  (:issue:`13382`)
- Bug in ``DatetimeIndex`` and ``Period`` subtraction raises ``ValueError`` or ``AttributeError`` rather than ``TypeError`` (:issue:`13078`)
- Bug in ``Index`` and ``Series`` created with ``NaN`` and ``NaT`` mixed data may not have ``datetime64`` dtype  (:issue:`13324`)
- Bug in ``Index`` and ``Series`` may ignore ``np.datetime64('nat')`` and ``np.timdelta64('nat')`` to infer dtype (:issue:`13324`)
- Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`)
- Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`)
- Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`)
- Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`)
- Bug in ``groupby(..).apply(..)`` when the passed function returns scalar values per group (:issue:`13468`).
- Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`)
- Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`)
- Bug in ``.tz_localize`` with ``dateutil.tz.tzlocal`` may return incorrect result (:issue:`13583`)
- Bug in ``DatetimeTZDtype`` dtype with ``dateutil.tz.tzlocal`` cannot be regarded as valid dtype (:issue:`13583`)
- Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`)
- Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`)
- Bug in ``Series`` indexing with tuple-valued data and a numeric index (:issue:`13509`)

- Bug in printing ``pd.DataFrame`` where unusual elements with the ``object`` dtype were causing segfaults (:issue:`13717`)
- Bug in ranking ``Series`` which could result in segfaults (:issue:`13445`)
- Bug in various index types, which did not propagate the name of passed index (:issue:`12309`)
- Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`)
- Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`)

- Bug in ``DataFrame.to_csv()`` in which float values were being quoted even though quotations were specified for non-numeric values only (:issue:`12922`, :issue:`13259`)
- Bug in ``DataFrame.describe()`` raising ``ValueError`` with only boolean columns (:issue:`13898`)
- Bug in ``MultiIndex`` slicing where extra elements were returned when level is non-unique (:issue:`12896`)
- Bug in ``.str.replace`` does not raise ``TypeError`` for invalid replacement (:issue:`13438`)
- Bug in ``MultiIndex.from_arrays`` which didn't check for input array lengths matching (:issue:`13599`)


- Bug in ``pd.read_csv()`` which may cause a segfault or corruption when iterating in large chunks over a stream/file under rare circumstances (:issue:`13703`)
- Bug in ``pd.read_csv()`` which caused errors to be raised when a dictionary containing scalars is passed in for ``na_values`` (:issue:`12224`)
- Bug in ``pd.read_csv()`` which caused BOM files to be incorrectly parsed by not ignoring the BOM (:issue:`4793`)
- Bug in ``pd.read_csv()`` with ``engine='python'`` which raised errors when a numpy array was passed in for ``usecols`` (:issue:`12546`)
- Bug in ``pd.read_csv()`` where the index columns were being incorrectly parsed when parsed as dates with a ``thousands`` parameter (:issue:`14066`)
- Bug in ``pd.read_csv()`` with ``engine='python'`` in which ``NaN`` values weren't being detected after data was converted to numeric values (:issue:`13314`)
- Bug in ``pd.read_csv()`` in which the ``nrows`` argument was not properly validated for both engines (:issue:`10476`)
- Bug in ``pd.read_csv()`` with ``engine='python'`` in which infinities of mixed-case forms were not being interpreted properly (:issue:`13274`)
- Bug in ``pd.read_csv()`` with ``engine='python'`` in which trailing ``NaN`` values were not being parsed (:issue:`13320`)
- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a ``tempfile.TemporaryFile`` on Windows with Python 3 (:issue:`13398`)
- Bug in ``pd.read_csv()`` that prevents ``usecols`` kwarg from accepting single-byte unicode strings (:issue:`13219`)
- Bug in ``pd.read_csv()`` that prevents ``usecols`` from being an empty set (:issue:`13402`)
- Bug in ``pd.read_csv()`` in the C engine where the NULL character was not being parsed as NULL (:issue:`14012`)
- Bug in ``pd.read_csv()`` with ``engine='c'`` in which NULL ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`)
- Bug in ``pd.read_csv()`` with ``engine='c'`` in which fields were not properly cast to float when quoting was specified as non-numeric (:issue:`13411`)
- Bug in ``pd.read_csv()`` in Python 2.x with non-UTF8 encoded, multi-character separated data (:issue:`3404`)
- Bug in ``pd.read_csv()``, where aliases for utf-xx (e.g. UTF-xx, UTF_xx, utf_xx) raised UnicodeDecodeError (:issue:`13549`)
- Bug in ``pd.read_csv``, ``pd.read_table``, ``pd.read_fwf``, ``pd.read_stata`` and ``pd.read_sas`` where files were opened by parsers but not closed if both ``chunksize`` and ``iterator`` were ``None``. (:issue:`13940`)
- Bug in ``StataReader``, ``StataWriter``, ``XportReader`` and ``SAS7BDATReader`` where a file was not properly closed when an error was raised. (:issue:`13940`)


- Bug in ``pd.pivot_table()`` where ``margins_name`` is ignored when ``aggfunc`` is a list (:issue:`13354`)
- Bug in ``pd.Series.str.zfill``, ``center``, ``ljust``, ``rjust``, and ``pad`` when passing non-integers, did not raise ``TypeError`` (:issue:`13598`)
- Bug in checking for any null objects in a ``TimedeltaIndex``, which always returned ``True`` (:issue:`13603`)



- Bug in ``Series`` arithmetic raises ``TypeError`` if it contains datetime-like as ``object`` dtype (:issue:`13043`)

- Bug ``Series.isnull()`` and ``Series.notnull()`` ignore ``Period('NaT')``  (:issue:`13737`)
- Bug ``Series.fillna()`` and ``Series.dropna()`` don't affect to ``Period('NaT')``  (:issue:`13737`
- Bug in ``.fillna(value=np.nan)`` incorrectly raises ``KeyError`` on a ``category`` dtyped ``Series`` (:issue:`14021`)

- Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`)
- Bug in ``.resample(..)`` where incorrect warnings were triggered by IPython introspection (:issue:`13618`)
- Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`)
- Bug in ``Series`` comparison may output incorrect result if rhs contains ``NaT`` (:issue:`9005`)
- Bug in ``Series`` and ``Index`` comparison may output incorrect result if it contains ``NaT`` with ``object`` dtype (:issue:`13592`)
- Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`)
- Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`)
- Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`)
- Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`)
- Clean some compile time warnings in datetime parsing (:issue:`13607`)
- Bug in ``factorize`` raises ``AmbiguousTimeError`` if data contains datetime near DST boundary (:issue:`13750`)
- Bug in ``.set_index`` raises ``AmbiguousTimeError`` if new index contains DST boundary and multi levels (:issue:`12920`)
- Bug in ``.shift`` raises ``AmbiguousTimeError`` if data contains datetime near DST boundary (:issue:`13926`)
- Bug in ``pd.read_hdf()`` returns incorrect result when a ``DataFrame`` with a ``categorical`` column and a query which doesn't match any values (:issue:`13792`)


- Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`)
- Bug in ``.combine_first`` may return incorrect ``dtype`` (:issue:`7630`, :issue:`10567`)
- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`)
- Bug in ``groupby(..).nth()`` where the group key is included inconsistently if called after ``.head()/.tail()`` (:issue:`12839`)
- Bug in ``.to_html``, ``.to_latex`` and ``.to_string`` silently ignore custom datetime formatter passed through the ``formatters`` key word (:issue:`10690`)
- Bug in ``DataFrame.iterrows()``, not yielding a ``Series`` subclasse if defined (:issue:`13977`)

- Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`)
- Bug in invalid ``Timedelta`` arithmetic and comparison may raise ``ValueError`` rather than ``TypeError`` (:issue:`13624`)
- Bug in invalid datetime parsing in ``to_datetime`` and ``DatetimeIndex`` may raise ``TypeError`` rather than ``ValueError`` (:issue:`11169`, :issue:`11287`)
- Bug in ``Index`` created with tz-aware ``Timestamp`` and mismatched ``tz`` option incorrectly coerces timezone (:issue:`13692`)
- Bug in ``DatetimeIndex`` with nanosecond frequency does not include timestamp specified with ``end`` (:issue:`13672`)

- Bug in ``Index`` raises ``OutOfBoundsDatetime`` if ``datetime`` exceeds ``datetime64[ns]`` bounds, rather than coercing to ``object`` dtype (:issue:`13663`)
- Bug in ``Index`` may ignore specified ``datetime64`` or ``timedelta64`` passed as ``dtype``  (:issue:`13981`)
- Bug in ``RangeIndex`` can be created without no arguments rather than raises ``TypeError`` (:issue:`13793`)
- Bug in ``.value_counts`` raises ``OutOfBoundsDatetime`` if data exceeds ``datetime64[ns]`` bounds (:issue:`13663`)
- Bug in ``DatetimeIndex`` may raise ``OutOfBoundsDatetime`` if input ``np.datetime64`` has other unit than ``ns`` (:issue:`9114`)
- Bug in ``Series`` creation with ``np.datetime64`` which has other unit than ``ns`` as ``object`` dtype results in incorrect values (:issue:`13876`)
- Bug in ``resample`` with timedelta data where data was casted to float (:issue:`13119`).
- Bug in ``pd.isnull()`` ``pd.notnull()`` raise ``TypeError`` if input datetime-like has other unit than ``ns`` (:issue:`13389`)
- Bug in ``pd.merge()`` may raise ``TypeError`` if input datetime-like has other unit than ``ns`` (:issue:`13389`)

- Bug in ``HDFStore``/``read_hdf()`` discarded ``DatetimeIndex.name`` if ``tz`` was set (:issue:`13884`)

- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`)
- Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`)
- Bug in ``df.groupby(...)[...]`` where getitem with ``Int64Index`` raised an error (:issue:`13731`)

- Bug in the CSS classes assigned to ``DataFrame.style`` for index names. Previously they were assigned ``"col_heading level<n> col<c>"`` where ``n`` was the number of levels + 1. Now they are assigned ``"index_name level<n>"``, where ``n`` is the correct level for that MultiIndex.
- Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient  (:issue:`13454`)
- Bug in ``Index.union`` returns an incorrect result with a named empty index (:issue:`13432`)
- Bugs in ``Index.difference`` and ``DataFrame.join`` raise in Python3 when using mixed-integer indexes (:issue:`13432`, :issue:`12814`)
- Bug in ``.to_excel()`` when DataFrame contains a MultiIndex which contains a label with a NaN value (:issue:`13511`)
- Bug in invalid frequency offset string like "D1", "-2-3H" may not raise ``ValueError (:issue:`13930`)
- Bug in ``concat`` and ``groupby`` for hierarchical frames with ``RangeIndex`` levels (:issue:`13542`).

- Bug in ``agg()`` function on groupby dataframe changes dtype of ``datetime64[ns]`` column to ``float64`` (:issue:`12821`)
- Bug in using NumPy ufunc with ``PeriodIndex`` to add or subtract integer raise ``IncompatibleFrequency``. Note that using standard operator like ``+`` or ``-`` is recommended, because standard operators use more efficient path (:issue:`13980`)
- Bug in operations on ``NaT`` returning ``float`` instead of ``datetime64[ns]`` (:issue:`12941`)
- Bug in ``Series`` flexible arithmetic methods (like ``.add()``) raises ``ValueError`` when ``axis=None`` (:issue:`13894`)
- Bug in ``DataFrame.to_csv()`` with ``MultiIndex`` columns in which a stray empty line was added (:issue:`6618`)


- Bug in ``Index`` raises ``KeyError`` displaying incorrect column when column is not in the df and columns contains duplicate values (:issue:`13822`)
- Bug in ``Period`` and ``PeriodIndex`` creating wrong dates when frequency has combined offset aliases (:issue:`13874`)
- Bug in ``.to_string()`` when called with an integer ``line_width`` and ``index=False`` raises an UnboundLocalError exception because ``idx`` referenced before assignment.

- Bug in ``eval()`` where the ``resolvers`` argument would not accept a list (:issue`14095`)
- Bugs in ``stack``, ``get_dummies``, ``make_axis_dummies`` which don't preserve categorical dtypes in (multi)indexes (:issue:`13854`)