v0.23.0.txt

.. _whatsnew_0230:

v0.23.0
-------

This is a major release from 0.21.1 and includes a number of API changes,
deprecations, new features, enhancements, and performance improvements along
with a large number of bug fixes. We recommend that all users upgrade to this
version.

.. _whatsnew_0230.enhancements:

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

.. _whatsnew_0210.enhancements.limit_area:

``DataFrame.interpolate`` has gained the ``limit_area`` kwarg
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

:meth:`DataFrame.interpolate` has gained a ``limit_area`` parameter to allow further control of which ``NaN`` s are replaced.
Use `limit_area='inside'` to fill only NaNs surrounded by valid values or use `limit_area='outside'` to fill only ``NaN`` s
outside the existing valid values while preserving those inside.  (:issue:`16284`) See the :ref:`full documentation here <missing_data.interp_limits>`.


.. ipython:: python

   ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13, np.nan, np.nan])
   ser

Fill one consecutive inside value in both directions

.. ipython:: python

   ser.interpolate(limit_direction='both', limit_area='inside', limit=1)

Fill all consecutive outside values backward

.. ipython:: python

   ser.interpolate(limit_direction='backward', limit_area='outside')

Fill all consecutive outside values in both directions

.. ipython:: python

   ser.interpolate(limit_direction='both', limit_area='outside')

.. _whatsnew_0210.enhancements.get_dummies_dtype:

``get_dummies`` now supports ``dtype`` argument
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :func:`get_dummies` now accepts a ``dtype`` argument, which specifies a dtype for the new columns. The default remains uint8. (:issue:`18330`)

.. ipython:: python

   df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
   pd.get_dummies(df, columns=['c']).dtypes
   pd.get_dummies(df, columns=['c'], dtype=bool).dtypes


.. _whatsnew_0230.enhancements.merge_on_columns_and_levels:

Merging on a combination of columns and index levels
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Strings passed to :meth:`DataFrame.merge` as the ``on``, ``left_on``, and ``right_on``
parameters may now refer to either column names or index level names.
This enables merging ``DataFrame`` instances on a combination of index levels
and columns without resetting indexes. See the :ref:`Merge on columns and
levels <merging.merge_on_columns_and_levels>` documentation section.
(:issue:`14355`)

.. ipython:: python

   left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')

   left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                        'B': ['B0', 'B1', 'B2', 'B3'],
                        'key2': ['K0', 'K1', 'K0', 'K1']},
                       index=left_index)

   right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')

   right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
                         'D': ['D0', 'D1', 'D2', 'D3'],
                         'key2': ['K0', 'K0', 'K0', 'K1']},
                        index=right_index)

   left.merge(right, on=['key1', 'key2'])

.. _whatsnew_0230.enhancements.sort_by_columns_and_levels:

Sorting by a combination of columns and index levels
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Strings passed to :meth:`DataFrame.sort_values` as the ``by`` parameter may
now refer to either column names or index level names.  This enables sorting
``DataFrame`` instances by a combination of index levels and columns without
resetting indexes. See the :ref:`Sorting by Indexes and Values
<basics.sort_indexes_and_values>` documentation section.
(:issue:`14353`)

.. ipython:: python

   # Build MultiIndex
   idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 2),
                                    ('b', 2), ('b', 1), ('b', 1)])
   idx.names = ['first', 'second']

   # Build DataFrame
   df_multi = pd.DataFrame({'A': np.arange(6, 0, -1)},
                           index=idx)
   df_multi

   # Sort by 'second' (index) and 'A' (column)
   df_multi.sort_values(by=['second', 'A'])

.. _whatsnew_0230.enhancements.timedelta_mod

Timedelta mod method
^^^^^^^^^^^^^^^^^^^^

``mod`` (%) and ``divmod`` operations are now defined on ``Timedelta`` objects when operating with either timedelta-like or with numeric arguments. (:issue:`19365`)

.. ipython:: python

    td = pd.Timedelta(hours=37)
    td

Current Behavior

.. ipython:: python
    
    td % pd.Timedelta(hours=2)
    divmod(td, np.array([2, 3], dtype='timedelta64[h]'))

.. _whatsnew_0230.enhancements.ran_inf:

``.rank()`` handles ``inf`` values when ``NaN`` are present
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In previous versions, ``.rank()`` would assign ``inf`` elements ``NaN`` as their ranks. Now ranks are calculated properly. (:issue:`6945`)

.. ipython:: python

    s = pd.Series([-np.inf, 0, 1, np.nan, np.inf])
    s

Previous Behavior:

.. code-block:: ipython

    In [11]: s.rank()
    Out[11]:
    0    1.0
    1    2.0
    2    3.0
    3    NaN
    4    NaN
    dtype: float64

Current Behavior:

.. ipython:: python

    s.rank()

Furthermore, previously if you rank ``inf`` or ``-inf`` values together with ``NaN`` values, the calculation won't distinguish ``NaN`` from infinity when using 'top' or 'bottom' argument.

.. ipython:: python

    s = pd.Series([np.nan, np.nan, -np.inf, -np.inf])
    s

Previous Behavior:

.. code-block:: ipython

    In [15]: s.rank(na_option='top')
    Out[15]:
    0    2.5
    1    2.5
    2    2.5
    3    2.5
    dtype: float64

Current Behavior:

.. ipython:: python

    s.rank(na_option='top')

.. _whatsnew_0230.enhancements.round-trippable_json:

JSON read/write round-trippable with ``orient='table'``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A ``DataFrame`` can now be written to and subsequently read back via JSON while preserving metadata through usage of the ``orient='table'`` argument (see :issue:`18912` and :issue:`9146`). Previously, none of the available ``orient`` values guaranteed the preservation of dtypes and index names, amongst other metadata.

.. ipython:: python

   df = pd.DataFrame({'foo': [1, 2, 3, 4],
		      'bar': ['a', 'b', 'c', 'd'],
		      'baz': pd.date_range('2018-01-01', freq='d', periods=4),
		      'qux': pd.Categorical(['a', 'b', 'c', 'c'])
		      }, index=pd.Index(range(4), name='idx'))
   df
   df.dtypes
   df.to_json('test.json', orient='table')
   new_df = pd.read_json('test.json', orient='table')
   new_df
   new_df.dtypes

Please note that the string `index` is not supported with the round trip format, as it is used by default in ``write_json`` to indicate a missing index name.

.. ipython:: python

   df.index.name = 'index'
   df.to_json('test.json', orient='table')
   new_df = pd.read_json('test.json', orient='table')
   new_df
   print(new_df.index.name)

.. _whatsnew_0230.enhancements.index_division_by_zero:

Index Division By Zero Fills Correctly
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Division operations on ``Index`` and subclasses will now fill division of positive numbers by zero with ``np.inf``, division of negative numbers by zero with ``-np.inf`` and `0 / 0` with ``np.nan``.  This matches existing ``Series`` behavior. (:issue:`19322`, :issue:`19347`)

Previous Behavior:

.. code-block:: ipython

    In [6]: index = pd.Int64Index([-1, 0, 1])

    In [7]: index / 0
    Out[7]: Int64Index([0, 0, 0], dtype='int64')

    # Previous behavior yielded different results depending on the type of zero in the divisor
    In [8]: index / 0.0
    Out[8]: Float64Index([-inf, nan, inf], dtype='float64')

    In [9]: index = pd.UInt64Index([0, 1])

    In [10]: index / np.array([0, 0], dtype=np.uint64)
    Out[10]: UInt64Index([0, 0], dtype='uint64')

    In [11]: pd.RangeIndex(1, 5) / 0
    ZeroDivisionError: integer division or modulo by zero

Current Behavior:

.. ipython:: python

    index = pd.Int64Index([-1, 0, 1])
    # division by zero gives -infinity where negative, +infinity where positive, and NaN for 0 / 0
    index / 0

    # The result of division by zero should not depend on whether the zero is int or float
    index / 0.0

    index = pd.UInt64Index([0, 1])
    index / np.array([0, 0], dtype=np.uint64)

    pd.RangeIndex(1, 5) / 0

.. _whatsnew_0230.enhancements.assign_dependent:

``.assign()`` accepts dependent arguments
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :func:`DataFrame.assign` now accepts dependent keyword arguments for python version later than 3.6 (see also `PEP 468
<https://www.python.org/dev/peps/pep-0468/>`_). Later keyword arguments may now refer to earlier ones if the argument is a callable. See the
:ref:`documentation here <dsintro.chained_assignment>` (:issue:`14207`)

.. ipython:: python

    df = pd.DataFrame({'A': [1, 2, 3]})
    df
    df.assign(B=df.A, C=lambda x:x['A']+ x['B'])

.. warning::

  This may subtly change the behavior of your code when you're
  using ``.assign()`` to update an existing column. Previously, callables
  referring to other variables being updated would get the "old" values

  Previous Behaviour:

  .. code-block:: ipython

      In [2]: df = pd.DataFrame({"A": [1, 2, 3]})

      In [3]: df.assign(A=lambda df: df.A + 1, C=lambda df: df.A * -1)
      Out[3]:
         A  C
      0  2 -1
      1  3 -2
      2  4 -3

  New Behaviour:

  .. ipython:: python

      df.assign(A=df.A+1, C= lambda df: df.A* -1)

.. _whatsnew_0230.enhancements.other:

Other Enhancements
^^^^^^^^^^^^^^^^^^

- Unary ``+`` now permitted for ``Series`` and ``DataFrame`` as  numeric operator (:issue:`16073`)
- Better support for :func:`Dataframe.style.to_excel` output with the ``xlsxwriter`` engine. (:issue:`16149`)
- :func:`pandas.tseries.frequencies.to_offset` now accepts leading '+' signs e.g. '+1h'. (:issue:`18171`)
- :func:`MultiIndex.unique` now supports the ``level=`` argument, to get unique values from a specific index level (:issue:`17896`)
- :class:`pandas.io.formats.style.Styler` now has method ``hide_index()`` to determine whether the index will be rendered in output (:issue:`14194`)
- :class:`pandas.io.formats.style.Styler` now has method ``hide_columns()`` to determine whether columns will be hidden in output (:issue:`14194`)
- Improved wording of ``ValueError`` raised in :func:`to_datetime` when ``unit=`` is passed with a non-convertible value (:issue:`14350`)
- :func:`Series.fillna` now accepts a Series or a dict as a ``value`` for a categorical dtype (:issue:`17033`)
- :func:`pandas.read_clipboard` updated to use qtpy, falling back to PyQt5 and then PyQt4, adding compatibility with Python3 and multiple python-qt bindings (:issue:`17722`)
- Improved wording of ``ValueError`` raised in :func:`read_csv` when the ``usecols`` argument cannot match all columns. (:issue:`17301`)
- :func:`DataFrame.corrwith` now silently drops non-numeric columns when passed a Series. Before, an exception was raised (:issue:`18570`).
- :class:`IntervalIndex` now supports time zone aware ``Interval`` objects (:issue:`18537`, :issue:`18538`)
- :func:`Series` / :func:`DataFrame` tab completion also returns identifiers in the first level of a :func:`MultiIndex`. (:issue:`16326`)
- :func:`read_excel()` has gained the ``nrows`` parameter (:issue:`16645`)
- :func:``DataFrame.to_json`` and ``Series.to_json`` now accept an ``index`` argument which allows the user to exclude the index from the JSON output (:issue:`17394`)
- ``IntervalIndex.to_tuples()`` has gained the ``na_tuple`` parameter to control whether NA is returned as a tuple of NA, or NA itself (:issue:`18756`)
- ``Categorical.rename_categories``, ``CategoricalIndex.rename_categories`` and :attr:`Series.cat.rename_categories`
  can now take a callable as their argument (:issue:`18862`)
- :class:`Interval` and :class:`IntervalIndex` have gained a ``length`` attribute (:issue:`18789`)
- ``Resampler`` objects now have a functioning :attr:`~pandas.core.resample.Resampler.pipe` method.
  Previously, calls to ``pipe`` were diverted to  the ``mean`` method (:issue:`17905`).
- :func:`~pandas.api.types.is_scalar` now returns ``True`` for ``DateOffset`` objects (:issue:`18943`).
- Added :func:`pandas.api.extensions.register_dataframe_accessor`,
  :func:`pandas.api.extensions.register_series_accessor`, and
  :func:`pandas.api.extensions.register_index_accessor`, accessor for libraries downstream of pandas
  to register custom accessors like ``.cat`` on pandas objects. See
  :ref:`Registering Custom Accessors <developer.register-accessors>` for more (:issue:`14781`).

- ``IntervalIndex.astype`` now supports conversions between subtypes when passed an ``IntervalDtype`` (:issue:`19197`)
- :class:`IntervalIndex` and its associated constructor methods (``from_arrays``, ``from_breaks``, ``from_tuples``) have gained a ``dtype`` parameter (:issue:`19262`)

.. _whatsnew_0230.api_breaking:

Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. _whatsnew_0230.api_breaking.deps:

Dependencies have increased minimum versions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

We have updated our minimum supported versions of dependencies (:issue:`15184`).
If installed, we now require:

   +-----------------+-----------------+----------+
   | Package         | Minimum Version | Required |
   +=================+=================+==========+
   | python-dateutil | 2.5.0           |    X     |
   +-----------------+-----------------+----------+
   | openpyxl        | 2.4.0           |          |
   +-----------------+-----------------+----------+

.. _whatsnew_0230.api_breaking.deprecate_panel:

Deprecate Panel
^^^^^^^^^^^^^^^

``Panel`` was deprecated in the 0.20.x release, showing as a ``DeprecationWarning``. Using ``Panel`` will now show a ``FutureWarning``. The recommended way to represent 3-D data are
with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas
provides a :meth:`~Panel.to_xarray` method to automate this conversion. For more details see :ref:`Deprecate Panel <dsintro.deprecate_panel>` documentation. (:issue:`13563`, :issue:`18324`).

.. ipython:: python
   :okwarning:

   p = tm.makePanel()
   p

Convert to a MultiIndex DataFrame

.. ipython:: python

   p.to_frame()

Convert to an xarray DataArray

.. ipython:: python
   :okwarning:

   p.to_xarray()

.. _whatsnew_0230.api_breaking.apply:

Changes to make output of ``DataFrame.apply`` consistent
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

:func:`DataFrame.apply` was inconsistent when applying an arbitrary user-defined-function that returned a list-like with ``axis=1``. Several bugs and inconsistencies
are resolved. If the applied function returns a Series, then pandas will return a DataFrame; otherwise a Series will be returned, this includes the case
where a list-like (e.g. ``tuple`` or ``list`` is returned) (:issue:`16353`, :issue:`17437`, :issue:`17970`, :issue:`17348`, :issue:`17892`, :issue:`18573`,
:issue:`17602`, :issue:`18775`, :issue:`18901`, :issue:`18919`).

.. ipython:: python

    df = pd.DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1, columns=['A', 'B', 'C'])
    df

Previous Behavior: if the returned shape happened to match the length of original columns, this would return a ``DataFrame``.
If the return shape did not match, a ``Series`` with lists was returned.

.. code-block:: python

   In [3]: df.apply(lambda x: [1, 2, 3], axis=1)
   Out[3]:
      A  B  C
   0  1  2  3
   1  1  2  3
   2  1  2  3
   3  1  2  3
   4  1  2  3
   5  1  2  3

   In [4]: df.apply(lambda x: [1, 2], axis=1)
   Out[4]:
   0    [1, 2]
   1    [1, 2]
   2    [1, 2]
   3    [1, 2]
   4    [1, 2]
   5    [1, 2]
   dtype: object


New Behavior: When the applied function returns a list-like, this will now *always* return a ``Series``.

.. ipython:: python

    df.apply(lambda x: [1, 2, 3], axis=1)
    df.apply(lambda x: [1, 2], axis=1)

To have expanded columns, you can use ``result_type='expand'``

.. ipython:: python

    df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')

To broadcast the result across the original columns (the old behaviour for
list-likes of the correct length), you can use ``result_type='broadcast'``.
The shape must match the original columns.

.. ipython:: python

    df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')

Returning a ``Series`` allows one to control the exact return structure and column names:

.. ipython:: python

    df.apply(lambda x: Series([1, 2, 3], index=['D', 'E', 'F']]), axis=1)


.. _whatsnew_0230.api_breaking.build_changes:

Build Changes
^^^^^^^^^^^^^

- Building pandas for development now requires ``cython >= 0.24`` (:issue:`18613`)
- Building from source now explicitly requires ``setuptools`` in ``setup.py`` (:issue:`18113`)
- Updated conda recipe to be in compliance with conda-build 3.0+ (:issue:`18002`)

.. _whatsnew_0230.api_breaking.extract:

Extraction of matching patterns from strings
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

By default, extracting matching patterns from strings with :func:`str.extract` used to return a
``Series`` if a single group was being extracted (a ``DataFrame`` if more than one group was
extracted``). As of Pandas 0.23.0 :func:`str.extract` always returns a ``DataFrame``, unless
``expand`` is set to ``False`` (:issue:`11386`).

Also, ``None`` was an accepted value for the ``expand`` parameter (which was equivalent to
``False``), but now raises a ``ValueError``.

Previous Behavior:

.. code-block:: ipython

    In [1]: s = pd.Series(['number 10', '12 eggs'])

    In [2]: extracted = s.str.extract('.*(\d\d).*')

    In [3]: extracted
    Out [3]:
    0    10
    1    12
    dtype: object

    In [4]: type(extracted)
    Out [4]:
    pandas.core.series.Series

New Behavior:

.. ipython:: python

    s = pd.Series(['number 10', '12 eggs'])
    extracted = s.str.extract('.*(\d\d).*')
    extracted
    type(extracted)

To restore previous behavior, simply set ``expand`` to ``False``:

.. ipython:: python

    s = pd.Series(['number 10', '12 eggs'])
    extracted = s.str.extract('.*(\d\d).*', expand=False)
    extracted
    type(extracted)

.. _whatsnew_0230.api_breaking.cdt_ordered:

Default value for the ``ordered`` parameter of ``CategoricalDtype``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The default value of the ``ordered`` parameter for :class:`~pandas.api.types.CategoricalDtype` has changed from ``False`` to ``None`` to allow updating of ``categories`` without impacting ``ordered``.  Behavior should remain consistent for downstream objects, such as :class:`Categorical` (:issue:`18790`)

In previous versions, the default value for the ``ordered`` parameter was ``False``.  This could potentially lead to the ``ordered`` parameter unintentionally being changed from ``True`` to ``False`` when users attempt to update ``categories`` if ``ordered`` is not explicitly specified, as it would silently default to ``False``.  The new behavior for ``ordered=None`` is to retain the existing value of ``ordered``.

New Behavior:

.. ipython:: python

    from pandas.api.types import CategoricalDtype
    cat = pd.Categorical(list('abcaba'), ordered=True, categories=list('cba'))
    cat
    cdt = CategoricalDtype(categories=list('cbad'))
    cat.astype(cdt)

Notice in the example above that the converted ``Categorical`` has retained ``ordered=True``.  Had the default value for ``ordered`` remained as ``False``, the converted ``Categorical`` would have become unordered, despite ``ordered=False`` never being explicitly specified.  To change the value of ``ordered``, explicitly pass it to the new dtype, e.g. ``CategoricalDtype(categories=list('cbad'), ordered=False)``.

Note that the unintenional conversion of ``ordered`` discussed above did not arise in previous versions due to separate bugs that prevented ``astype`` from doing any type of category to category conversion (:issue:`10696`, :issue:`18593`).  These bugs have been fixed in this release, and motivated changing the default value of ``ordered``.

.. _whatsnew_0230.api:

Other API Changes
^^^^^^^^^^^^^^^^^

- :func:`Series.astype` and :func:`Index.astype` with an incompatible dtype will now raise a ``TypeError`` rather than a ``ValueError`` (:issue:`18231`)
- ``Series`` construction with an ``object`` dtyped tz-aware datetime and ``dtype=object`` specified, will now return an ``object`` dtyped ``Series``, previously this would infer the datetime dtype (:issue:`18231`)
- A :class:`Series` of ``dtype=category`` constructed from an empty ``dict`` will now have categories of ``dtype=object`` rather than ``dtype=float64``, consistently with the case in which an empty list is passed (:issue:`18515`)
- ``NaT`` division with :class:`datetime.timedelta` will now return ``NaN`` instead of raising (:issue:`17876`)
- All-NaN levels in a ``MultiIndex`` are now assigned ``float`` rather than ``object`` dtype, promoting consistency with ``Index`` (:issue:`17929`).
- Levels names of a ``MultiIndex`` (when not None) are now required to be unique: trying to create a ``MultiIndex`` with repeated names will raise a ``ValueError`` (:issue:`18872`)
- :class:`Timestamp` will no longer silently ignore unused or invalid ``tz`` or ``tzinfo`` keyword arguments (:issue:`17690`)
- :class:`Timestamp` will no longer silently ignore invalid ``freq`` arguments (:issue:`5168`)
- :class:`CacheableOffset` and :class:`WeekDay` are no longer available in the ``pandas.tseries.offsets`` module (:issue:`17830`)
- ``pandas.tseries.frequencies.get_freq_group()`` and ``pandas.tseries.frequencies.DAYS`` are removed from the public API (:issue:`18034`)
- :func:`Series.truncate` and :func:`DataFrame.truncate` will raise a ``ValueError`` if the index is not sorted instead of an unhelpful ``KeyError`` (:issue:`17935`)
- :func:`Index.map` can now accept ``Series`` and dictionary input objects (:issue:`12756`, :issue:`18482`, :issue:`18509`).
- :func:`Dataframe.unstack` will now default to filling with ``np.nan`` for ``object`` columns. (:issue:`12815`)
- :class:`IntervalIndex` constructor will raise if the ``closed`` parameter conflicts with how the input data is inferred to be closed (:issue:`18421`)
- Inserting missing values into indexes will work for all types of indexes and automatically insert the correct type of missing value (``NaN``, ``NaT``, etc.) regardless of the type passed in (:issue:`18295`)
- Restricted ``DateOffset`` keyword arguments. Previously, ``DateOffset`` subclasses allowed arbitrary keyword arguments which could lead to unexpected behavior. Now, only valid arguments will be accepted. (:issue:`17176`, :issue:`18226`).
- :func:`DataFrame.from_items` provides a more informative error message when passed scalar values (:issue:`17312`)
- When created with duplicate labels, ``MultiIndex`` now raises a ``ValueError``. (:issue:`17464`)
- :func:`Series.fillna` now raises a ``TypeError`` instead of a ``ValueError`` when passed a list, tuple or DataFrame as a ``value`` (:issue:`18293`)
- :func:`pandas.DataFrame.merge` no longer casts a ``float`` column to ``object`` when merging on ``int`` and ``float`` columns (:issue:`16572`)
- The default NA value for :class:`UInt64Index` has changed from 0 to ``NaN``, which impacts methods that mask with NA, such as ``UInt64Index.where()`` (:issue:`18398`)
- Refactored ``setup.py`` to use ``find_packages`` instead of explicitly listing out all subpackages (:issue:`18535`)
- Rearranged the order of keyword arguments in :func:`read_excel()` to align with :func:`read_csv()` (:issue:`16672`)
- :func:`pandas.merge` now raises a ``ValueError`` when trying to merge on incompatible data types (:issue:`9780`)
- :func:`wide_to_long` previously kept numeric-like suffixes as ``object`` dtype. Now they are cast to numeric if possible (:issue:`17627`)
- In :func:`read_excel`, the ``comment`` argument is now exposed as a named parameter (:issue:`18735`)
- Rearranged the order of keyword arguments in :func:`read_excel()` to align with :func:`read_csv()` (:issue:`16672`)
- The options ``html.border`` and ``mode.use_inf_as_null`` were deprecated in prior versions, these will now show ``FutureWarning`` rather than a ``DeprecationWarning`` (:issue:`19003`)
- Subtracting ``NaT`` from a :class:`Series` with ``dtype='datetime64[ns]'`` returns a ``Series`` with ``dtype='timedelta64[ns]'`` instead of ``dtype='datetime64[ns]'``(:issue:`18808`)
- Operations between a :class:`Series` with dtype ``dtype='datetime64[ns]'`` and a :class:`PeriodIndex` will correctly raises ``TypeError`` (:issue:`18850`)
- Subtraction of :class:`Series` with timezone-aware ``dtype='datetime64[ns]'`` with mis-matched timezones will raise ``TypeError`` instead of ``ValueError`` (issue:`18817`)
- :class:`IntervalIndex` and ``IntervalDtype`` no longer support categorical, object, and string subtypes (:issue:`19016`)
- The default ``Timedelta`` constructor now accepts an ``ISO 8601 Duration`` string as an argument (:issue:`19040`)
- ``IntervalDtype`` now returns ``True`` when compared against ``'interval'`` regardless of subtype, and ``IntervalDtype.name`` now returns ``'interval'`` regardless of subtype (:issue:`18980`)
- ``KeyError`` now raises instead of ``ValueError`` in :meth:`~DataFrame.drop`, :meth:`~Panel.drop`, :meth:`~Series.drop`, :meth:`~Index.drop` when dropping a non-existent element in an axis with duplicates (:issue:`19186`)
- :func:`Series.to_csv` now accepts a ``compression`` argument that works in the same way as the ``compression`` argument in :func:`DataFrame.to_csv` (:issue:`18958`)
- Addition or subtraction of ``NaT`` from :class:`TimedeltaIndex` will return ``TimedeltaIndex`` instead of ``DatetimeIndex`` (:issue:`19124`)
- :func:`DatetimeIndex.shift` and :func:`TimedeltaIndex.shift` will now raise ``NullFrequencyError`` (which subclasses ``ValueError``, which was raised in older versions) when the index object frequency is ``None`` (:issue:`19147`)
- Addition and subtraction of ``NaN`` from a :class:`Series` with ``dtype='timedelta64[ns]'`` will raise a ``TypeError` instead of treating the ``NaN`` as ``NaT`` (:issue:`19274`)
- Set operations (union, difference...) on :class:`IntervalIndex` with incompatible index types will now raise a ``TypeError`` rather than a ``ValueError`` (:issue:`19329`)
- :class:`DateOffset` objects render more simply, e.g. "<DateOffset: days=1>" instead of "<DateOffset: kwds={'days': 1}>" (:issue:`19403`)
- :func:`pandas.merge` provides a more informative error message when trying to merge on timezone-aware and timezone-naive columns (:issue:`15800`)
- :func:`Timedelta.__mod__`, :func:`Timedelta.__divmod__` now accept timedelta-like and numeric arguments instead of raising ``TypeError`` (:issue:`19365`)

.. _whatsnew_0230.deprecations:

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

- ``Series.from_array`` and ``SparseSeries.from_array`` are deprecated. Use the normal constructor ``Series(..)`` and ``SparseSeries(..)`` instead (:issue:`18213`).
- ``DataFrame.as_matrix`` is deprecated. Use ``DataFrame.values`` instead (:issue:`18458`).
- ``Series.asobject``, ``DatetimeIndex.asobject``, ``PeriodIndex.asobject`` and ``TimeDeltaIndex.asobject`` have been deprecated. Use ``.astype(object)`` instead (:issue:`18572`)
- Grouping by a tuple of keys now emits a ``FutureWarning`` and is deprecated.
  In the future, a tuple passed to ``'by'`` will always refer to a single key
  that is the actual tuple, instead of treating the tuple as multiple keys. To
  retain the previous behavior, use a list instead of a tuple (:issue:`18314`)
- ``Series.valid`` is deprecated. Use :meth:`Series.dropna` instead (:issue:`18800`).
- :func:`read_excel` has deprecated the ``skip_footer`` parameter. Use ``skipfooter`` instead (:issue:`18836`)
- The ``is_copy`` attribute is deprecated and will be removed in a future version (:issue:`18801`).
- ``IntervalIndex.from_intervals`` is deprecated in favor of the :class:`IntervalIndex` constructor (:issue:`19263`)
- :func:``DataFrame.from_items`` is deprecated. Use :func:``DataFrame.from_dict()`` instead, or :func:``DataFrame.from_dict(OrderedDict())`` if you wish to preserve the key order (:issue:`17320`)
- The ``broadcast`` parameter of ``.apply()`` is deprecated in favor of ``result_type='broadcast'`` (:issue:`18577`)
- The ``reduce`` parameter of ``.apply()`` is deprecated in favor of ``result_type='reduce'`` (:issue:`18577`)

.. _whatsnew_0230.prior_deprecations:

Removal of prior version deprecations/changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- Warnings against the obsolete usage ``Categorical(codes, categories)``, which were emitted for instance when the first two arguments to ``Categorical()`` had different dtypes, and recommended the use of ``Categorical.from_codes``, have now been removed (:issue:`8074`)
- The ``levels`` and ``labels`` attributes of a ``MultiIndex`` can no longer be set directly (:issue:`4039`).
- ``pd.tseries.util.pivot_annual`` has been removed (deprecated since v0.19). Use ``pivot_table`` instead (:issue:`18370`)
- ``pd.tseries.util.isleapyear`` has been removed (deprecated since v0.19). Use ``.is_leap_year`` property in Datetime-likes instead (:issue:`18370`)
- ``pd.ordered_merge`` has been removed (deprecated since v0.19). Use ``pd.merge_ordered`` instead (:issue:`18459`)
- The ``SparseList`` class has been removed (:issue:`14007`)
- The ``pandas.io.wb`` and ``pandas.io.data`` stub modules have been removed (:issue:`13735`)
- ``Categorical.from_array`` has been removed (:issue:`13854`)
- The ``freq`` and ``how`` parameters have been removed from the ``rolling``/``expanding``/``ewm`` methods of DataFrame
  and Series (deprecated since v0.18). Instead, resample before calling the methods. (:issue:18601 & :issue:18668)
- ``DatetimeIndex.to_datetime``, ``Timestamp.to_datetime``, ``PeriodIndex.to_datetime``, and ``Index.to_datetime`` have been removed (:issue:`8254`, :issue:`14096`, :issue:`14113`)
- :func:`read_csv` has dropped the ``skip_footer`` parameter (:issue:`13386`)
- :func:`read_csv` has dropped the ``as_recarray`` parameter (:issue:`13373`)
- :func:`read_csv` has dropped the ``buffer_lines`` parameter (:issue:`13360`)
- :func:`read_csv` has dropped the ``compact_ints`` and ``use_unsigned`` parameters (:issue:`13323`)
- The ``Timestamp`` class has dropped the ``offset`` attribute in favor of ``freq`` (:issue:`13593`)
- The ``Series``, ``Categorical``, and ``Index`` classes have dropped the ``reshape`` method (:issue:`13012`)
- ``pandas.tseries.frequencies.get_standard_freq`` has been removed in favor of ``pandas.tseries.frequencies.to_offset(freq).rule_code`` (:issue:`13874`)
- The ``freqstr`` keyword has been removed from ``pandas.tseries.frequencies.to_offset`` in favor of ``freq`` (:issue:`13874`)
- The ``Panel4D`` and ``PanelND`` classes have been removed (:issue:`13776`)
- The ``Panel``class has dropped the ``to_long``and ``toLong`` methods (:issue:`19077`)
- The options ``display.line_with`` and ``display.height`` are removed in favor of ``display.width`` and ``display.max_rows`` respectively (:issue:`4391`, :issue:`19107`)
- The ``labels`` attribute of the ``Categorical`` class has been removed in favor of :attribute:`Categorical.codes` (:issue:`7768`)
- The ``flavor`` parameter have been removed from func:`to_sql` method (:issue:`13611`)
- The modules `pandas.tools.hashing` and `pandas.util.hashing` have been removed (:issue:`16223`)
- The top-level functions ``pd.rolling_*``, ``pd.expanding_*`` and ``pd.ewm*`` have been removed (Deprecated since v0.18).
  Instead, use the DataFrame/Series methods :attr:`~DataFrame.rolling`, :attr:`~DataFrame.expanding` and :attr:`~DataFrame.ewm` (:issue:`18723`)

.. _whatsnew_0230.performance:

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

- Indexers on ``Series`` or ``DataFrame`` no longer create a reference cycle (:issue:`17956`)
- Added a keyword argument, ``cache``, to :func:`to_datetime` that improved the performance of converting duplicate datetime arguments (:issue:`11665`)
- :class`DateOffset` arithmetic performance is improved (:issue:`18218`)
- Converting a ``Series`` of ``Timedelta`` objects to days, seconds, etc... sped up through vectorization of underlying methods (:issue:`18092`)
- Improved performance of ``.map()`` with a ``Series/dict`` input (:issue:`15081`)
- The overridden ``Timedelta`` properties of days, seconds and microseconds have been removed, leveraging their built-in Python versions instead (:issue:`18242`)
- ``Series`` construction will reduce the number of copies made of the input data in certain cases (:issue:`17449`)
- Improved performance of :func:`Series.dt.date` and :func:`DatetimeIndex.date` (:issue:`18058`)
- Improved performance of :func:`Series.dt.time` and :func:`DatetimeIndex.time` (:issue:`18461`)
- Improved performance of :func:`IntervalIndex.symmetric_difference()` (:issue:`18475`)
- Improved performance of ``DatetimeIndex`` and ``Series`` arithmetic operations with Business-Month and Business-Quarter frequencies (:issue:`18489`)
- :func:`Series` / :func:`DataFrame` tab completion limits to 100 values, for better performance. (:issue:`18587`)
- Improved performance of :func:`DataFrame.median` with ``axis=1`` when bottleneck is not installed (:issue:`16468`)
- Improved performance of :func:`MultiIndex.get_loc` for large indexes, at the cost of a reduction in performance for small ones (:issue:`18519`)
- Improved performance of pairwise ``.rolling()`` and ``.expanding()`` with ``.cov()`` and ``.corr()`` operations (:issue:`17917`)
- Improved performance of :func:`DataFrameGroupBy.rank` (:issue:`15779`)

.. _whatsnew_0230.docs:

Documentation Changes
~~~~~~~~~~~~~~~~~~~~~

- Changed spelling of "numpy" to "NumPy", and "python" to "Python". (:issue:`19017`)
- Consistency when introducing code samples, using either colon or period.
  Rewrote some sentences for greater clarity, added more dynamic references
  to functions, methods and classes.
  (:issue:`18941`, :issue:`18948`, :issue:`18973`, :issue:`19017`)
-

.. _whatsnew_0230.bug_fixes:

Bug Fixes
~~~~~~~~~

Categorical
^^^^^^^^^^^

.. warning::

   A class of bugs were introduced in pandas 0.21 with ``CategoricalDtype`` that
   affects the correctness of operations like ``merge``, ``concat``, and
   indexing when comparing multiple unordered ``Categorical`` arrays that have
   the same categories, but in a different order. We highly recommend upgrading
   or manually aligning your categories before doing these operations.

- Bug in ``Categorical.equals`` returning the wrong result when comparing two
  unordered ``Categorical`` arrays with the same categories, but in a different
  order (:issue:`16603`)
- Bug in :func:`pandas.api.types.union_categoricals` returning the wrong result
  when for unordered categoricals with the categories in a different order.
  This affected :func:`pandas.concat` with Categorical data (:issue:`19096`).
- Bug in :func:`pandas.merge` returning the wrong result when joining on an
  unordered ``Categorical`` that had the same categories but in a different
  order (:issue:`19551`)
- Bug in :meth:`CategoricalIndex.get_indexer` returning the wrong result when
  ``target`` was an unordered ``Categorical`` that had the same categories as
  ``self`` but in a different order (:issue:`19551`)
- Bug in :meth:`Index.astype` with a categorical dtype where the resultant index is not converted to a :class:`CategoricalIndex` for all types of index (:issue:`18630`)
- Bug in :meth:`Series.astype` and ``Categorical.astype()`` where an existing categorical data does not get updated (:issue:`10696`, :issue:`18593`)
- Bug in :class:`Index` constructor with ``dtype=CategoricalDtype(...)`` where ``categories`` and ``ordered`` are not maintained (issue:`19032`)

Datetimelike
^^^^^^^^^^^^

- Bug in :func:`Series.__sub__` subtracting a non-nanosecond ``np.datetime64`` object from a ``Series`` gave incorrect results (:issue:`7996`)
- Bug in :class:`DatetimeIndex`, :class:`TimedeltaIndex` addition and subtraction of zero-dimensional integer arrays gave incorrect results (:issue:`19012`)
- Bug in :func:`Series.__add__` adding Series with dtype ``timedelta64[ns]`` to a timezone-aware ``DatetimeIndex`` incorrectly dropped timezone information (:issue:`13905`)
- Bug in :func:`Timedelta.__floordiv__` and :func:`Timedelta.__rfloordiv__` dividing by many incompatible numpy objects was incorrectly allowed (:issue:`18846`)
- Adding a ``Period`` object to a ``datetime`` or ``Timestamp`` object will now correctly raise a ``TypeError`` (:issue:`17983`)
- Bug in :class:`Timestamp` where comparison with an array of ``Timestamp`` objects would result in a ``RecursionError`` (:issue:`15183`)
- Bug in :class:`DatetimeIndex` and :class:`TimedeltaIndex` where adding or subtracting an array-like of ``DateOffset`` objects either raised (``np.array``, ``pd.Index``) or broadcast incorrectly (``pd.Series``) (:issue:`18849`)
- Bug in :class:`Series` floor-division where operating on a scalar ``timedelta`` raises an exception (:issue:`18846`)
- Bug in :class:`Series`` with ``dtype='timedelta64[ns]`` where addition or subtraction of ``TimedeltaIndex`` had results cast to ``dtype='int64'`` (:issue:`17250`)
- Bug in :class:`TimedeltaIndex` where division by a ``Series`` would return a ``TimedeltaIndex`` instead of a ``Series`` (issue:`19042`)
- Bug in :class:`Series` with ``dtype='timedelta64[ns]`` where addition or subtraction of ``TimedeltaIndex`` could return a ``Series`` with an incorrect name (:issue:`19043`)
- Bug in :class:`DatetimeIndex` where the repr was not showing high-precision time values at the end of a day (e.g., 23:59:59.999999999) (:issue:`19030`)
- Bug where dividing a scalar timedelta-like object with :class:`TimedeltaIndex` performed the reciprocal operation (:issue:`19125`)
- Bug in ``.astype()`` to non-ns timedelta units would hold the incorrect dtype (:issue:`19176`, :issue:`19223`, :issue:`12425`)
- Bug in subtracting :class:`Series` from ``NaT`` incorrectly returning ``NaT`` (:issue:`19158`)
- Bug in :func:`Series.truncate` which raises ``TypeError`` with a monotonic ``PeriodIndex`` (:issue:`17717`)
- Bug in :func:`~DataFrame.pct_change` using ``periods`` and ``freq`` returned different length outputs (:issue:`7292`)
- Bug in comparison of :class:`DatetimeIndex` against ``None`` or ``datetime.date`` objects raising ``TypeError`` for ``==`` and ``!=`` comparisons instead of all-``False`` and all-``True``, respectively (:issue:`19301`)
- Bug in :class:`Timestamp` and :func:`to_datetime` where a string representing a barely out-of-bounds timestamp would be incorrectly rounded down instead of raising ``OutOfBoundsDatetime`` (:issue:`19382`)
- Bug in :func:`Timestamp.floor` :func:`DatetimeIndex.floor` where time stamps far in the future and past were not rounded correctly (:issue:`19206`)
- Bug in :func:`to_datetime` where passing an out-of-bounds datetime with ``errors='coerce'`` and ``utc=True`` would raise ``OutOfBoundsDatetime`` instead of parsing to ``NaT`` (:issue:`19612`)
-


Timezones
^^^^^^^^^

- Bug in creating a ``Series`` from an array that contains both tz-naive and tz-aware values will result in a ``Series`` whose dtype is tz-aware instead of object (:issue:`16406`)
- Bug in comparison of timezone-aware :class:`DatetimeIndex` against ``NaT`` incorrectly raising ``TypeError`` (:issue:`19276`)
- Bug in :meth:`DatetimeIndex.astype` when converting between timezone aware dtypes, and converting from timezone aware to naive (:issue:`18951`)
- Bug in comparing :class:`DatetimeIndex`, which failed to raise ``TypeError`` when attempting to compare timezone-aware and timezone-naive datetimelike objects (:issue:`18162`)
- Bug in localization of a naive, datetime string in a ``Series`` constructor with a ``datetime64[ns, tz]`` dtype (:issue:`174151`)
- :func:`Timestamp.replace` will now handle Daylight Savings transitions gracefully (:issue:`18319`)
- Bug in tz-aware :class:`DatetimeIndex` where addition/subtraction with a :class:`TimedeltaIndex` or array with ``dtype='timedelta64[ns]'`` was incorrect (:issue:`17558`)
- Bug in :func:`DatetimeIndex.insert` where inserting ``NaT`` into a timezone-aware index incorrectly raised (:issue:`16357`)
- Bug in the :class:`DataFrame` constructor, where tz-aware Datetimeindex and a given column name will result in an empty ``DataFrame`` (:issue:`19157`)
- Bug in :func:`Timestamp.tz_localize` where localizing a timestamp near the minimum or maximum valid values could overflow and return a timestamp with an incorrect nanosecond value (:issue:`12677`)

Offsets
^^^^^^^

- Bug in :class:`WeekOfMonth` and class:`Week` where addition and subtraction did not roll correctly (:issue:`18510`,:issue:`18672`,:issue:`18864`)
- Bug in :class:`WeekOfMonth` and :class:`LastWeekOfMonth` where default keyword arguments for constructor raised ``ValueError`` (:issue:`19142`)
- Bug in :class:`FY5253Quarter`, :class:`LastWeekOfMonth` where rollback and rollforward behavior was inconsistent with addition and subtraction behavior (:issue:`18854`)
- Bug in :class:`FY5253` where ``datetime`` addition and subtraction incremented incorrectly for dates on the year-end but not normalized to midnight (:issue:`18854`)
- Bug in :class:`FY5253` where date offsets could incorrectly raise an ``AssertionError`` in arithmetic operatons (:issue:`14774`)


Numeric
^^^^^^^
- Bug in :class:`Series` constructor with an int or float list where specifying ``dtype=str``, ``dtype='str'`` or ``dtype='U'`` failed to convert the data elements to strings (:issue:`16605`)
- Bug in :class:`Index` multiplication and division methods where operating with a ``Series`` would return an ``Index`` object instead of a ``Series`` object (:issue:`19042`)
- Bug in the :class:`DataFrame` constructor in which data containing very large positive or very large negative numbers was causing ``OverflowError`` (:issue:`18584`)
- Bug in :class:`Index` constructor with ``dtype='uint64'`` where int-like floats were not coerced to :class:`UInt64Index` (:issue:`18400`)
- Bug in :class:`DataFrame` flex arithmetic (e.g. `df.add(other, fill_value=foo)`) with a `fill_value` other than ``None`` failed to raise ``NotImplementedError`` in corner cases where either the frame or ``other`` has length zero (:issue:`19522`)


Indexing
^^^^^^^^

- Bug in :class:`Index` construction from list of mixed type tuples (:issue:`18505`)
- Bug in :func:`Index.drop` when passing a list of both tuples and non-tuples (:issue:`18304`)
- Bug in :meth:`~DataFrame.drop`, :meth:`~Panel.drop`, :meth:`~Series.drop`, :meth:`~Index.drop` where no ``KeyError`` is raised when dropping a non-existent element from an axis that contains duplicates (:issue:`19186`)
- Bug in indexing a datetimelike ``Index`` that raised ``ValueError`` instead of ``IndexError`` (:issue:`18386`).
- :func:`Index.to_series` now accepts ``index`` and ``name`` kwargs (:issue:`18699`)
- :func:`DatetimeIndex.to_series` now accepts ``index`` and ``name`` kwargs (:issue:`18699`)
- Bug in indexing non-scalar value from ``Series`` having non-unique ``Index`` will return value flattened (:issue:`17610`)
- Bug in ``__setitem__`` when indexing a :class:`DataFrame` with a 2-d boolean ndarray (:issue:`18582`)
- Bug in ``str.extractall`` when there were no matches empty :class:`Index` was returned instead of appropriate :class:`MultiIndex` (:issue:`19034`)
- Bug in :class:`IntervalIndex` where empty and purely NA data was constructed inconsistently depending on the construction method (:issue:`18421`)
- Bug in :func:`IntervalIndex.symmetric_difference` where the symmetric difference with a non-``IntervalIndex`` did not raise (:issue:`18475`)
- Bug in :class:`IntervalIndex` where set operations that returned an empty ``IntervalIndex`` had the wrong dtype (:issue:`19101`)

MultiIndex
^^^^^^^^^^

- Bug in :func:`MultiIndex.__contains__` where non-tuple keys would return ``True`` even if they had been dropped (:issue:`19027`)
- Bug in :func:`MultiIndex.set_labels` which would cause casting (and potentially clipping) of the new labels if the ``level`` argument is not 0 or a list like [0, 1, ... ]  (:issue:`19057`)
- Bug in :func:`MultiIndex.get_level_values` which would return an invalid index on level of ints with missing values (:issue:`17924`)
- Bug in :func:`MultiIndex.remove_unused_levels` which would fill nan values (:issue:`18417`)
- Bug in :func:`MultiIndex.from_tuples`` which would fail to take zipped tuples in python3 (:issue:`18434`)
- Bug in :func:`MultiIndex.get_loc`` which would fail to automatically cast values between float and int (:issue:`18818`, :issue:`15994`)
- Bug in :func:`MultiIndex.get_loc`` which would cast boolean to integer labels (:issue:`19086`)
- Bug in :func:`MultiIndex.get_loc`` which would fail to locate keys containing ``NaN`` (:issue:`18485`)
- Bug in :func:`MultiIndex.get_loc`` in large :class:`MultiIndex`, would fail when levels had different dtypes (:issue:`18520`)


I/O
^^^

- :func:`read_html` now rewinds seekable IO objects after parse failure, before attempting to parse with a new parser. If a parser errors and the object is non-seekable, an informative error is raised suggesting the use of a different parser (:issue:`17975`)
- :meth:`DataFrame.to_html` now has an option to add an id to the leading `<table>` tag (:issue:`8496`)
- Bug in :func:`read_msgpack` with a non existent file is passed in Python 2 (:issue:`15296`)
- Bug in :func:`read_csv` where a ``MultiIndex`` with duplicate columns was not being mangled appropriately (:issue:`18062`)
- Bug in :func:`read_csv` where missing values were not being handled properly when ``keep_default_na=False`` with dictionary ``na_values`` (:issue:`19227`)
- Bug in :func:`read_sas` where a file with 0 variables gave an ``AttributeError`` incorrectly. Now it gives an ``EmptyDataError`` (:issue:`18184`)
- Bug in :func:`DataFrame.to_latex()` where pairs of braces meant to serve as invisible placeholders were escaped (:issue:`18667`)
- Bug in :func:`read_json` where large numeric values were causing an ``OverflowError`` (:issue:`18842`)
- Bug in :func:`DataFrame.to_parquet` where an exception was raised if the write destination is S3 (:issue:`19134`)
- :class:`Interval` now supported in :func:`DataFrame.to_excel` for all Excel file types (:issue:`19242`)
- :class:`Timedelta` now supported in :func:`DataFrame.to_excel` for xls file type (:issue:`19242`, :issue:`9155`)
- Bug in :meth:`pandas.io.stata.StataReader.value_labels` raising an ``AttributeError`` when called on very old files. Now returns an empty dict (:issue:`19417`)

Plotting
^^^^^^^^

- :func: `DataFrame.plot` now raises a ``ValueError`` when the ``x`` or ``y`` argument is improperly formed (:issue:`18671`)
- Bug in formatting tick labels with ``datetime.time()`` and fractional seconds (:issue:`18478`).
- :meth:`Series.plot.kde` has exposed the args ``ind`` and ``bw_method`` in the docstring (:issue:`18461`). The argument ``ind`` may now also be an integer (number of sample points).
-

Groupby/Resample/Rolling
^^^^^^^^^^^^^^^^^^^^^^^^

- Bug when grouping by a single column and aggregating with a class like ``list`` or ``tuple`` (:issue:`18079`)
- Fixed regression in :func:`DataFrame.groupby` which would not emit an error when called with a tuple key not in the index (:issue:`18798`)
- Bug in :func:`DataFrame.resample` which silently ignored unsupported (or mistyped) options for ``label``, ``closed`` and ``convention`` (:issue:`19303`)
- Bug in :func:`DataFrame.groupby` where tuples were interpreted as lists of keys rather than as keys (:issue:`17979`, :issue:`18249`)
- Bug in :func:`DataFrame.groupby` where aggregation by ``first``/``last``/``min``/``max`` was causing timestamps to lose precision (:issue:`19526`)
- Bug in :func:`DataFrame.transform` where particular aggregation functions were being incorrectly cast to match the dtype(s) of the grouped data (:issue:`19200`)
- Bug in :func:`DataFrame.groupby` passing the `on=` kwarg, and subsequently using ``.apply()`` (:issue:`17813`)
- Bug in :func:`DataFrame.resample().aggregate` not raising a ``KeyError`` when aggregating a non-existent column (:issue:`16766`, :issue:`19566`)
- Fixed a performance regression for ``GroupBy.nth`` and ``GroupBy.last`` with some object columns (:issue:`19283`)

Sparse
^^^^^^

- Bug in which creating a ``SparseDataFrame`` from a dense ``Series`` or an unsupported type raised an uncontrolled exception (:issue:`19374`)
- Bug in :class:`SparseDataFrame.to_csv` causing exception (:issue:`19384`)
- Bug in :class:`SparseSeries.memory_usage` which caused segfault by accessing non sparse elements (:issue:`19368`)
- Bug in constructing a ``SparseArray``: if ``data`` is a scalar and ``index`` is defined it will coerce to ``float64`` regardless of scalar's dtype. (:issue:`19163`)

Reshaping
^^^^^^^^^

- Bug in :func:`DataFrame.stack` which fails trying to sort mixed type levels under Python 3 (:issue:`18310`)
- Bug in :func:`DataFrame.unstack` which casts int to float if ``columns`` is a ``MultiIndex`` with unused levels (:issue:`17845`)
- Bug in :func:`DataFrame.unstack` which raises an error if ``index`` is a ``MultiIndex`` with unused labels on the unstacked level (:issue:`18562`)
- Fixed construction of a :class:`Series` from a ``dict`` containing ``NaN`` as key (:issue:`18480`)
- Bug in :func:`Series.rank` where ``Series`` containing ``NaT`` modifies the ``Series`` inplace (:issue:`18521`)
- Bug in :func:`cut` which fails when using readonly arrays (:issue:`18773`)
- Bug in :func:`Dataframe.pivot_table` which fails when the ``aggfunc`` arg is of type string.  The behavior is now consistent with other methods like ``agg`` and ``apply`` (:issue:`18713`)
- Bug in :func:`DataFrame.merge` in which merging using ``Index`` objects as vectors raised an Exception (:issue:`19038`)
- Bug in :func:`DataFrame.stack`, :func:`DataFrame.unstack`, :func:`Series.unstack` which were not returning subclasses (:issue:`15563`)
- Bug in timezone comparisons, manifesting as a conversion of the index to UTC in ``.concat()`` (:issue:`18523`)
- Bug in :func:`concat` when concatting sparse and dense series it returns only a ``SparseDataFrame``. Should be a ``DataFrame``. (:issue:`18914`, :issue:`18686`, and :issue:`16874`)
- Improved error message for :func:`DataFrame.merge` when there is no common merge key (:issue:`19427`)
- Bug in :func:`DataFrame.join` which does an *outer* instead of a *left* join when being called with multiple DataFrames and some have non-unique indices (:issue:`19624`)

Other
^^^^^

- Improved error message when attempting to use a Python keyword as an identifier in a ``numexpr`` backed query (:issue:`18221`)