v0.24.0.txt

.. _whatsnew_0240:

v0.24.0 (Month XX, 2018)
------------------------

.. warning::

   Starting January 1, 2019, pandas feature releases will support Python 3 only.
   See :ref:`install.dropping-27` for more.

.. _whatsnew_0240.enhancements:

New features
~~~~~~~~~~~~
- :func:`merge` now directly allows merge between objects of type ``DataFrame`` and named ``Series``, without the need to convert the ``Series`` object into a ``DataFrame`` beforehand (:issue:`21220`)


- ``ExcelWriter`` now accepts ``mode`` as a keyword argument, enabling append to existing workbooks when using the ``openpyxl`` engine (:issue:`3441`)

- :func:`DataFrame.to_parquet` now accepts ``index`` as an argument, allowing
the user to override the engine's default behavior to include or omit the
dataframe's indexes from the resulting Parquet file. (:issue:`20768`)
- :meth:`DataFrame.corr` and :meth:`Series.corr` now accept a callable for generic calculation methods of correlation, e.g. histogram intersection (:issue:`22684`)


.. _whatsnew_0240.enhancements.extension_array_operators:

``ExtensionArray`` operator support
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A ``Series`` based on an ``ExtensionArray`` now supports arithmetic and comparison
operators (:issue:`19577`). There are two approaches for providing operator support for an ``ExtensionArray``:

1. Define each of the operators on your ``ExtensionArray`` subclass.
2. Use an operator implementation from pandas that depends on operators that are already defined
   on the underlying elements (scalars) of the ``ExtensionArray``.

See the :ref:`ExtensionArray Operator Support
<extending.extension.operator>` documentation section for details on both
ways of adding operator support.

.. _whatsnew_0240.enhancements.intna:

Optional Integer NA Support
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Pandas has gained the ability to hold integer dtypes with missing values. This long requested feature is enabled through the use of :ref:`extension types <extending.extension-types>`.
Here is an example of the usage.

We can construct a ``Series`` with the specified dtype. The dtype string ``Int64`` is a pandas ``ExtensionDtype``. Specifying a list or array using the traditional missing value
marker of ``np.nan`` will infer to integer dtype. The display of the ``Series`` will also use the ``NaN`` to indicate missing values in string outputs. (:issue:`20700`, :issue:`20747`, :issue:`22441`, :issue:`21789`, :issue:`22346`)

.. ipython:: python

   s = pd.Series([1, 2, np.nan], dtype='Int64')
   s


Operations on these dtypes will propagate ``NaN`` as other pandas operations.

.. ipython:: python

   # arithmetic
   s + 1

   # comparison
   s == 1

   # indexing
   s.iloc[1:3]

   # operate with other dtypes
   s + s.iloc[1:3].astype('Int8')

   # coerce when needed
   s + 0.01

These dtypes can operate as part of of ``DataFrame``.

.. ipython:: python

   df = pd.DataFrame({'A': s, 'B': [1, 1, 3], 'C': list('aab')})
   df
   df.dtypes


These dtypes can be merged & reshaped & casted.

.. ipython:: python

   pd.concat([df[['A']], df[['B', 'C']]], axis=1).dtypes
   df['A'].astype(float)

Reduction and groupby operations such as 'sum' work.

.. ipython:: python

   df.sum()
   df.groupby('B').A.sum()

.. warning::

   The Integer NA support currently uses the captilized dtype version, e.g. ``Int8`` as compared to the traditional ``int8``. This may be changed at a future date.

.. _whatsnew_0240.enhancements.read_html:

``read_html`` Enhancements
^^^^^^^^^^^^^^^^^^^^^^^^^^

:func:`read_html` previously ignored ``colspan`` and ``rowspan`` attributes.
Now it understands them, treating them as sequences of cells with the same
value. (:issue:`17054`)

.. ipython:: python

    result = pd.read_html("""
      <table>
        <thead>
          <tr>
            <th>A</th><th>B</th><th>C</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td colspan="2">1</td><td>2</td>
          </tr>
        </tbody>
      </table>""")

Previous Behavior:

.. code-block:: ipython

    In [13]: result
    Out [13]:
    [   A  B   C
     0  1  2 NaN]

Current Behavior:

.. ipython:: python

    result


.. _whatsnew_0240.enhancements.interval:

Storing Interval and Period Data in Series and DataFrame
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Interval and Period data may now be stored in a ``Series`` or ``DataFrame``, in addition to an
:class:`IntervalIndex` and :class:`PeriodIndex` like previously (:issue:`19453`, :issue:`22862`).

.. ipython:: python

   ser = pd.Series(pd.interval_range(0, 5))
   ser
   ser.dtype

And for periods:

.. ipython:: python

   pser = pd.Series(pd.date_range("2000", freq="D", periods=5))
   pser
   pser.dtype

Previously, these would be cast to a NumPy array with object dtype. In general,
this should result in better performance when storing an array of intervals or periods
in a :class:`Series` or column of a :class:`DataFrame`.

Note that the ``.values`` of a ``Series`` containing one of these types is no longer a NumPy
array, but rather an ``ExtensionArray``:

.. ipython:: python

   ser.values
   pser.values

This is the same behavior as ``Series.values`` for categorical data. See
:ref:`whatsnew_0240.api_breaking.interval_values` for more.

.. _whatsnew_0240.enhancements.rename_axis:

Renaming names in a MultiIndex
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:func:`DataFrame.rename_axis` now supports ``index`` and ``columns`` arguments
and :func:`Series.rename_axis` supports ``index`` argument (:issue:`19978`)

This change allows a dictionary to be passed so that some of the names 
of a ``MultiIndex`` can be changed.

Example:

.. ipython:: python

    mi = pd.MultiIndex.from_product([list('AB'), list('CD'), list('EF')],
                                    names=['AB', 'CD', 'EF'])
    df = pd.DataFrame([i for i in range(len(mi))], index=mi, columns=['N'])
    df
    df.rename_axis(index={'CD': 'New'})

See the :ref:`advanced docs on renaming<advanced.index_names>` for more details.

.. _whatsnew_0240.enhancements.other:

Other Enhancements
^^^^^^^^^^^^^^^^^^
- :func:`to_datetime` now supports the ``%Z`` and ``%z`` directive when passed into ``format`` (:issue:`13486`)
- :func:`Series.mode` and :func:`DataFrame.mode` now support the ``dropna`` parameter which can be used to specify whether ``NaN``/``NaT`` values should be considered (:issue:`17534`)
- :func:`to_csv` now supports ``compression`` keyword when a file handle is passed. (:issue:`21227`)
- :meth:`Index.droplevel` is now implemented also for flat indexes, for compatibility with :class:`MultiIndex` (:issue:`21115`)
- :meth:`Series.droplevel` and :meth:`DataFrame.droplevel` are now implemented (:issue:`20342`)
- Added support for reading from/writing to Google Cloud Storage via the ``gcsfs`` library (:issue:`19454`, :issue:`23094`)
- :func:`to_gbq` and :func:`read_gbq` signature and documentation updated to
  reflect changes from the `Pandas-GBQ library version 0.6.0
  <https://pandas-gbq.readthedocs.io/en/latest/changelog.html#changelog-0-6-0>`__.
  (:issue:`21627`, :issue:`22557`)
- New method :meth:`HDFStore.walk` will recursively walk the group hierarchy of an HDF5 file (:issue:`10932`)
- :func:`read_html` copies cell data across ``colspan`` and ``rowspan``, and it treats all-``th`` table rows as headers if ``header`` kwarg is not given and there is no ``thead`` (:issue:`17054`)
- :meth:`Series.nlargest`, :meth:`Series.nsmallest`, :meth:`DataFrame.nlargest`, and :meth:`DataFrame.nsmallest` now accept the value ``"all"`` for the ``keep`` argument. This keeps all ties for the nth largest/smallest value (:issue:`16818`)
- :class:`IntervalIndex` has gained the :meth:`~IntervalIndex.set_closed` method to change the existing ``closed`` value (:issue:`21670`)
- :func:`~DataFrame.to_csv`, :func:`~Series.to_csv`, :func:`~DataFrame.to_json`, and :func:`~Series.to_json` now support ``compression='infer'`` to infer compression based on filename extension (:issue:`15008`).
  The default compression for ``to_csv``, ``to_json``, and ``to_pickle`` methods has been updated to ``'infer'`` (:issue:`22004`).
- :func:`to_timedelta` now supports iso-formated timedelta strings (:issue:`21877`)
- Comparing :class:`Timedelta` with unknown types now return ``NotImplemented`` instead of ``False`` (:issue:`20829`)
- :class:`Series` and :class:`DataFrame` now support :class:`Iterable` in constructor (:issue:`2193`)
- :class:`DatetimeIndex` gained :attr:`DatetimeIndex.timetz` attribute. Returns local time with timezone information. (:issue:`21358`)
- :meth:`round`, :meth:`ceil`, and meth:`floor` for :class:`DatetimeIndex` and :class:`Timestamp` now support an ``ambiguous`` argument for handling datetimes that are rounded to ambiguous times (:issue:`18946`)
- :class:`Resampler` now is iterable like :class:`GroupBy` (:issue:`15314`).
- :meth:`Series.resample` and :meth:`DataFrame.resample` have gained the :meth:`Resampler.quantile` (:issue:`15023`).
- :meth:`pandas.core.dtypes.is_list_like` has gained a keyword ``allow_sets`` which is ``True`` by default; if ``False``,
  all instances of ``set`` will not be considered "list-like" anymore (:issue:`23061`)
- :meth:`Index.to_frame` now supports overriding column name(s) (:issue:`22580`).
- New attribute :attr:`__git_version__` will return git commit sha of current build (:issue:`21295`).
- Compatibility with Matplotlib 3.0 (:issue:`22790`).
- Added :meth:`Interval.overlaps`, :meth:`IntervalArray.overlaps`, and :meth:`IntervalIndex.overlaps` for determining overlaps between interval-like objects (:issue:`21998`)
- :meth:`Timestamp.tz_localize`, :meth:`DatetimeIndex.tz_localize`, and :meth:`Series.tz_localize` have gained the ``nonexistent`` argument for alternative handling of nonexistent times. See :ref:`timeseries.timezone_nonexsistent` (:issue:`8917`)

.. _whatsnew_0240.api_breaking:

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

- A newly constructed empty :class:`DataFrame` with integer as the ``dtype`` will now only be cast to ``float64`` if ``index`` is specified (:issue:`22858`)
- :meth:`Series.str.cat` will now raise if `others` is a `set` (:issue:`23009`)

.. _whatsnew_0240.api_breaking.deps:

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

We have updated our minimum supported versions of dependencies (:issue:`21242`).
If installed, we now require:

+-----------------+-----------------+----------+
| Package         | Minimum Version | Required |
+=================+=================+==========+
| numpy           | 1.12.0          |    X     |
+-----------------+-----------------+----------+
| bottleneck      | 1.2.0           |          |
+-----------------+-----------------+----------+
| matplotlib      | 2.0.0           |          |
+-----------------+-----------------+----------+
| numexpr         | 2.6.1           |          |
+-----------------+-----------------+----------+
| pytables        | 3.4.2           |          |
+-----------------+-----------------+----------+
| scipy           | 0.18.1          |          |
+-----------------+-----------------+----------+

.. _whatsnew_0240.api_breaking.csv_line_terminator:

`os.linesep` is used for ``line_terminator`` of ``DataFrame.to_csv``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:func:`DataFrame.to_csv` now uses :func:`os.linesep` rather than ``'\n'``
 for the default line terminator (:issue:`20353`).
This change only affects when running on Windows, where ``'\r\n'`` was used for line terminator
even when ``'\n'`` was passed in ``line_terminator``.

Previous Behavior on Windows:

.. code-block:: ipython

In [1]: data = pd.DataFrame({
   ...:     "string_with_lf": ["a\nbc"],
   ...:     "string_with_crlf": ["a\r\nbc"]
   ...: })

In [2]: # When passing file PATH to to_csv, line_terminator does not work, and csv is saved with '\r\n'.
   ...: # Also, this converts all '\n's in the data to '\r\n'.
   ...: data.to_csv("test.csv", index=False, line_terminator='\n')

In [3]: with open("test.csv", mode='rb') as f:
   ...:     print(f.read())
b'string_with_lf,string_with_crlf\r\n"a\r\nbc","a\r\r\nbc"\r\n'

In [4]: # When passing file OBJECT with newline option to to_csv, line_terminator works.
   ...: with open("test2.csv", mode='w', newline='\n') as f:
   ...:     data.to_csv(f, index=False, line_terminator='\n')

In [5]: with open("test2.csv", mode='rb') as f:
   ...:     print(f.read())
b'string_with_lf,string_with_crlf\n"a\nbc","a\r\nbc"\n'


New Behavior on Windows:

- By passing ``line_terminator`` explicitly, line terminator is set to that character.
- The value of ``line_terminator`` only affects the line terminator of CSV,
  so it does not change the value inside the data.

.. code-block:: ipython

In [1]: data = pd.DataFrame({
   ...:     "string_with_lf": ["a\nbc"],
   ...:     "string_with_crlf": ["a\r\nbc"]
   ...: })

In [2]: data.to_csv("test.csv", index=False, line_terminator='\n')

In [3]: with open("test.csv", mode='rb') as f:
   ...:     print(f.read())
b'string_with_lf,string_with_crlf\n"a\nbc","a\r\nbc"\n'


- On Windows, the value of ``os.linesep`` is ``'\r\n'``,
  so if ``line_terminator`` is not set, ``'\r\n'`` is used for line terminator.
- Again, it does not affect the value inside the data.

.. code-block:: ipython

In [1]: data = pd.DataFrame({
   ...: "string_with_lf": ["a\nbc"],
   ...: "string_with_crlf": ["a\r\nbc"]
   ...: })

In [2]: data.to_csv("test.csv", index=False)

In [3]: with open("test.csv", mode='rb') as f:
   ...:     print(f.read())
b'string_with_lf,string_with_crlf\r\n"a\nbc","a\r\nbc"\r\n'


- For files objects, specifying ``newline`` is not sufficient to set the line terminator.
  You must pass in the ``line_terminator`` explicitly, even in this case.

.. code-block:: ipython

In [1]: data = pd.DataFrame({
   ...: "string_with_lf": ["a\nbc"],
   ...: "string_with_crlf": ["a\r\nbc"]
   ...: })

In [2]: with open("test2.csv", mode='w', newline='\n') as f:
   ...:     data.to_csv(f, index=False)

In [3]: with open("test2.csv", mode='rb') as f:
   ...:     print(f.read())
b'string_with_lf,string_with_crlf\r\n"a\nbc","a\r\nbc"\r\n'

.. _whatsnew_0240.api_breaking.interval_values:

``IntervalIndex.values`` is now an ``IntervalArray``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :attr:`~Interval.values` attribute of an :class:`IntervalIndex` now returns an
``IntervalArray``, rather than a NumPy array of :class:`Interval` objects (:issue:`19453`).

Previous Behavior:

.. code-block:: ipython

   In [1]: idx = pd.interval_range(0, 4)

   In [2]: idx.values
   Out[2]:
   array([Interval(0, 1, closed='right'), Interval(1, 2, closed='right'),
          Interval(2, 3, closed='right'), Interval(3, 4, closed='right')],
         dtype=object)

New Behavior:

.. ipython:: python

   idx = pd.interval_range(0, 4)
   idx.values

This mirrors ``CategoricalIndex.values``, which returns a ``Categorical``.

For situations where you need an ``ndarray`` of ``Interval`` objects, use
:meth:`numpy.asarray`.

.. ipython:: python

   np.asarray(idx)
   idx.values.astype(object)

.. _whatsnew_0240.api.timezone_offset_parsing:

Parsing Datetime Strings with Timezone Offsets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Previously, parsing datetime strings with UTC offsets with :func:`to_datetime`
or :class:`DatetimeIndex` would automatically convert the datetime to UTC
without timezone localization. This is inconsistent from parsing the same
datetime string with :class:`Timestamp` which would preserve the UTC
offset in the ``tz`` attribute. Now, :func:`to_datetime` preserves the UTC
offset in the ``tz`` attribute when all the datetime strings have the same
UTC offset (:issue:`17697`, :issue:`11736`, :issue:`22457`)

*Previous Behavior*:

.. code-block:: ipython

    In [2]: pd.to_datetime("2015-11-18 15:30:00+05:30")
    Out[2]: Timestamp('2015-11-18 10:00:00')

    In [3]: pd.Timestamp("2015-11-18 15:30:00+05:30")
    Out[3]: Timestamp('2015-11-18 15:30:00+0530', tz='pytz.FixedOffset(330)')

    # Different UTC offsets would automatically convert the datetimes to UTC (without a UTC timezone)
    In [4]: pd.to_datetime(["2015-11-18 15:30:00+05:30", "2015-11-18 16:30:00+06:30"])
    Out[4]: DatetimeIndex(['2015-11-18 10:00:00', '2015-11-18 10:00:00'], dtype='datetime64[ns]', freq=None)

*Current Behavior*:

.. ipython:: python

    pd.to_datetime("2015-11-18 15:30:00+05:30")
    pd.Timestamp("2015-11-18 15:30:00+05:30")

Parsing datetime strings with the same UTC offset will preserve the UTC offset in the ``tz``

.. ipython:: python

    pd.to_datetime(["2015-11-18 15:30:00+05:30"] * 2)

Parsing datetime strings with different UTC offsets will now create an Index of
``datetime.datetime`` objects with different UTC offsets

.. ipython:: python

    idx = pd.to_datetime(["2015-11-18 15:30:00+05:30", "2015-11-18 16:30:00+06:30"])
    idx
    idx[0]
    idx[1]

Passing ``utc=True`` will mimic the previous behavior but will correctly indicate
that the dates have been converted to UTC

.. ipython:: python

    pd.to_datetime(["2015-11-18 15:30:00+05:30", "2015-11-18 16:30:00+06:30"], utc=True)

.. _whatsnew_0240.api_breaking.calendarday:

CalendarDay Offset
^^^^^^^^^^^^^^^^^^

:class:`Day` and associated frequency alias ``'D'`` were documented to represent
a calendar day; however, arithmetic and operations with :class:`Day` sometimes
respected absolute time instead (i.e. ``Day(n)`` and acted identically to ``Timedelta(days=n)``).

*Previous Behavior*:

.. code-block:: ipython


    In [2]: ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')

    # Respects calendar arithmetic
    In [3]: pd.date_range(start=ts, freq='D', periods=3)
    Out[3]:
    DatetimeIndex(['2016-10-30 00:00:00+03:00', '2016-10-31 00:00:00+02:00',
                   '2016-11-01 00:00:00+02:00'],
                  dtype='datetime64[ns, Europe/Helsinki]', freq='D')

    # Respects absolute arithmetic
    In [4]: ts + pd.tseries.frequencies.to_offset('D')
    Out[4]: Timestamp('2016-10-30 23:00:00+0200', tz='Europe/Helsinki')

:class:`CalendarDay` and associated frequency alias ``'CD'`` are now available
and respect calendar day arithmetic while :class:`Day` and frequency alias ``'D'``
will now respect absolute time (:issue:`22274`, :issue:`20596`, :issue:`16980`, :issue:`8774`)
See the :ref:`documentation here <timeseries.dayvscalendarday>` for more information.

Addition with :class:`CalendarDay` across a daylight savings time transition:

.. ipython:: python

   ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
   ts + pd.offsets.Day(1)
   ts + pd.offsets.CalendarDay(1)

.. _whatsnew_0240.api_breaking.period_end_time:

Time values in ``dt.end_time`` and ``to_timestamp(how='end')``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The time values in :class:`Period` and :class:`PeriodIndex` objects are now set
to '23:59:59.999999999' when calling :attr:`Series.dt.end_time`, :attr:`Period.end_time`,
:attr:`PeriodIndex.end_time`, :func:`Period.to_timestamp()` with ``how='end'``,
or :func:`PeriodIndex.to_timestamp()` with ``how='end'`` (:issue:`17157`)

Previous Behavior:

.. code-block:: ipython

   In [2]: p = pd.Period('2017-01-01', 'D')
   In [3]: pi = pd.PeriodIndex([p])

   In [4]: pd.Series(pi).dt.end_time[0]
   Out[4]: Timestamp(2017-01-01 00:00:00)

   In [5]: p.end_time
   Out[5]: Timestamp(2017-01-01 23:59:59.999999999)

Current Behavior:

Calling :attr:`Series.dt.end_time` will now result in a time of '23:59:59.999999999' as
is the case with :attr:`Period.end_time`, for example

.. ipython:: python

   p = pd.Period('2017-01-01', 'D')
   pi = pd.PeriodIndex([p])

   pd.Series(pi).dt.end_time[0]

   p.end_time

.. _whatsnew_0240.api_breaking.sparse_values:

Sparse Data Structure Refactor
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``SparseArray``, the array backing ``SparseSeries`` and the columns in a ``SparseDataFrame``,
is now an extension array (:issue:`21978`, :issue:`19056`, :issue:`22835`).
To conform to this interface and for consistency with the rest of pandas, some API breaking
changes were made:

- ``SparseArray`` is no longer a subclass of :class:`numpy.ndarray`. To convert a SparseArray to a NumPy array, use :meth:`numpy.asarray`.
- ``SparseArray.dtype`` and ``SparseSeries.dtype`` are now instances of :class:`SparseDtype`, rather than ``np.dtype``. Access the underlying dtype with ``SparseDtype.subtype``.
- :meth:`numpy.asarray(sparse_array)` now returns a dense array with all the values, not just the non-fill-value values (:issue:`14167`)
- ``SparseArray.take`` now matches the API of :meth:`pandas.api.extensions.ExtensionArray.take` (:issue:`19506`):

  * The default value of ``allow_fill`` has changed from ``False`` to ``True``.
  * The ``out`` and ``mode`` parameters are now longer accepted (previously, this raised if they were specified).
  * Passing a scalar for ``indices`` is no longer allowed.

- The result of concatenating a mix of sparse and dense Series is a Series with sparse values, rather than a ``SparseSeries``.
- ``SparseDataFrame.combine`` and ``DataFrame.combine_first`` no longer supports combining a sparse column with a dense column while preserving the sparse subtype. The result will be an object-dtype SparseArray.
- Setting :attr:`SparseArray.fill_value` to a fill value with a different dtype is now allowed.

Some new warnings are issued for operations that require or are likely to materialize a large dense array:

- A :class:`errors.PerformanceWarning` is issued when using fillna with a ``method``, as a dense array is constructed to create the filled array. Filling with a ``value`` is the efficient way to fill a sparse array.
- A :class:`errors.PerformanceWarning` is now issued when concatenating sparse Series with differing fill values. The fill value from the first sparse array continues to be used.

In addition to these API breaking changes, many :ref:`performance improvements and bug fixes have been made <whatsnew_0240.bug_fixes.sparse>`.

Finally, a ``Series.sparse`` accessor was added to provide sparse-specific methods like :meth:`Series.sparse.from_coo`.

.. ipython:: python

   s = pd.Series([0, 0, 1, 1, 1], dtype='Sparse[int]')
   s.sparse.density

.. _whatsnew_0240.api_breaking.frame_to_dict_index_orient:

Raise ValueError in ``DataFrame.to_dict(orient='index')``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Bug in :func:`DataFrame.to_dict` raises ``ValueError`` when used with
``orient='index'`` and a non-unique index instead of losing data (:issue:`22801`)

.. ipython:: python
    :okexcept:

    df = pd.DataFrame({'a': [1, 2], 'b': [0.5, 0.75]}, index=['A', 'A'])
    df

    df.to_dict(orient='index')

.. _whatsnew_0240.api.datetimelike.normalize:

Tick DateOffset Normalize Restrictions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Creating a ``Tick`` object (:class:`Day`, :class:`Hour`, :class:`Minute`,
:class:`Second`, :class:`Milli`, :class:`Micro`, :class:`Nano`) with
``normalize=True`` is no longer supported.  This prevents unexpected behavior
where addition could fail to be monotone or associative.  (:issue:`21427`)

*Previous Behavior*:

.. code-block:: ipython


   In [2]: ts = pd.Timestamp('2018-06-11 18:01:14')

   In [3]: ts
   Out[3]: Timestamp('2018-06-11 18:01:14')

   In [4]: tic = pd.offsets.Hour(n=2, normalize=True)
      ...:

   In [5]: tic
   Out[5]: <2 * Hours>

   In [6]: ts + tic
   Out[6]: Timestamp('2018-06-11 00:00:00')

   In [7]: ts + tic + tic + tic == ts + (tic + tic + tic)
   Out[7]: False

*Current Behavior*:

.. ipython:: python

    ts = pd.Timestamp('2018-06-11 18:01:14')
    tic = pd.offsets.Hour(n=2)
    ts + tic + tic + tic == ts + (tic + tic + tic)


.. _whatsnew_0240.api.datetimelike:


.. _whatsnew_0240.api.period_subtraction:

Period Subtraction
^^^^^^^^^^^^^^^^^^

Subtraction of a ``Period`` from another ``Period`` will give a ``DateOffset``.
instead of an integer (:issue:`21314`)

.. ipython:: python

    june = pd.Period('June 2018')
    april = pd.Period('April 2018')
    june - april

Previous Behavior:

.. code-block:: ipython

    In [2]: june = pd.Period('June 2018')

    In [3]: april = pd.Period('April 2018')

    In [4]: june - april
    Out [4]: 2

Similarly, subtraction of a ``Period`` from a ``PeriodIndex`` will now return
an ``Index`` of ``DateOffset`` objects instead of an ``Int64Index``

.. ipython:: python

    pi = pd.period_range('June 2018', freq='M', periods=3)
    pi - pi[0]

Previous Behavior:

.. code-block:: ipython

    In [2]: pi = pd.period_range('June 2018', freq='M', periods=3)

    In [3]: pi - pi[0]
    Out[3]: Int64Index([0, 1, 2], dtype='int64')


.. _whatsnew_0240.api.timedelta64_subtract_nan:

Addition/Subtraction of ``NaN`` from :class:`DataFrame`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Adding or subtracting ``NaN`` from a :class:`DataFrame` column with
``timedelta64[ns]`` dtype will now raise a ``TypeError`` instead of returning
all-``NaT``.  This is for compatibility with ``TimedeltaIndex`` and
``Series`` behavior (:issue:`22163`)

.. ipython:: python
   :okexcept:

   df = pd.DataFrame([pd.Timedelta(days=1)])
   df - np.nan

Previous Behavior:

.. code-block:: ipython

    In [4]: df = pd.DataFrame([pd.Timedelta(days=1)])

    In [5]: df - np.nan
    Out[5]:
        0
    0 NaT

.. _whatsnew_0240.api.dataframe_cmp_broadcasting:

DataFrame Comparison Operations Broadcasting Changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Previously, the broadcasting behavior of :class:`DataFrame` comparison
operations (``==``, ``!=``, ...) was inconsistent with the behavior of
arithmetic operations (``+``, ``-``, ...).  The behavior of the comparison
operations has been changed to match the arithmetic operations in these cases.
(:issue:`22880`)

The affected cases are:

- operating against a 2-dimensional ``np.ndarray`` with either 1 row or 1 column will now broadcast the same way a ``np.ndarray`` would (:issue:`23000`).
- a list or tuple with length matching the number of rows in the :class:`DataFrame` will now raise ``ValueError`` instead of operating column-by-column (:issue:`22880`.
- a list or tuple with length matching the number of columns in the :class:`DataFrame` will now operate row-by-row instead of raising ``ValueError`` (:issue:`22880`).

Previous Behavior:

.. code-block:: ipython

   In [3]: arr = np.arange(6).reshape(3, 2)
   In [4]: df = pd.DataFrame(arr)

   In [5]: df == arr[[0], :]
      ...: # comparison previously broadcast where arithmetic would raise
   Out[5]:
          0      1
   0   True   True
   1  False  False
   2  False  False
   In [6]: df + arr[[0], :]
   ...
   ValueError: Unable to coerce to DataFrame, shape must be (3, 2): given (1, 2)

   In [7]: df == (1, 2)
      ...: # length matches number of columns;
      ...: # comparison previously raised where arithmetic would broadcast
   ...
   ValueError: Invalid broadcasting comparison [(1, 2)] with block values
   In [8]: df + (1, 2)
   Out[8]:
      0  1
   0  1  3
   1  3  5
   2  5  7

   In [9]: df == (1, 2, 3)
      ...: # length matches number of rows
      ...: # comparison previously broadcast where arithmetic would raise
   Out[9]:
          0      1
   0  False   True
   1   True  False
   2  False  False
   In [10]: df + (1, 2, 3)
   ...
   ValueError: Unable to coerce to Series, length must be 2: given 3

*Current Behavior*:

.. ipython:: python
   :okexcept:

   arr = np.arange(6).reshape(3, 2)
   df = pd.DataFrame(arr)

.. ipython:: python
   # Comparison operations and arithmetic operations both broadcast.
   df == arr[[0], :]
   df + arr[[0], :]

.. ipython:: python
   # Comparison operations and arithmetic operations both broadcast.
   df == (1, 2)
   df + (1, 2)

.. ipython:: python
   :okexcept:
   # Comparison operations and arithmetic opeartions both raise ValueError.
   df == (1, 2, 3)
   df + (1, 2, 3)


.. _whatsnew_0240.api.dataframe_arithmetic_broadcasting:

DataFrame Arithmetic Operations Broadcasting Changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
:class:`DataFrame` arithmetic operations when operating with 2-dimensional
``np.ndarray`` objects now broadcast in the same way as ``np.ndarray``s
broadcast.  (:issue:`23000`)

Previous Behavior:

.. code-block:: ipython

   In [3]: arr = np.arange(6).reshape(3, 2)
   In [4]: df = pd.DataFrame(arr)
   In [5]: df + arr[[0], :]   # 1 row, 2 columns
   ...
   ValueError: Unable to coerce to DataFrame, shape must be (3, 2): given (1, 2)
   In [6]: df + arr[:, [1]]   # 1 column, 3 rows
   ...
   ValueError: Unable to coerce to DataFrame, shape must be (3, 2): given (3, 1)

*Current Behavior*:

.. ipython:: python
   arr = np.arange(6).reshape(3, 2)
   df = pd.DataFrame(arr)
   df

.. ipython:: python
   df + arr[[0], :]   # 1 row, 2 columns
   df + arr[:, [1]]   # 1 column, 3 rows


.. _whatsnew_0240.api.extension:

ExtensionType Changes
^^^^^^^^^^^^^^^^^^^^^

**:class:`pandas.api.extensions.ExtensionDtype` Equality and Hashability**

Pandas now requires that extension dtypes be hashable. The base class implements
a default ``__eq__`` and ``__hash__``. If you have a parametrized dtype, you should
update the ``ExtensionDtype._metadata`` tuple to match the signature of your
``__init__`` method. See :class:`pandas.api.extensions.ExtensionDtype` for more (:issue:`22476`).

**Other changes**

- ``ExtensionArray`` has gained the abstract methods ``.dropna()`` (:issue:`21185`)
- ``ExtensionDtype`` has gained the ability to instantiate from string dtypes, e.g. ``decimal`` would instantiate a registered ``DecimalDtype``; furthermore
  the ``ExtensionDtype`` has gained the method ``construct_array_type`` (:issue:`21185`)
- An ``ExtensionArray`` with a boolean dtype now works correctly as a boolean indexer. :meth:`pandas.api.types.is_bool_dtype` now properly considers them boolean (:issue:`22326`)
- Added ``ExtensionDtype._is_numeric`` for controlling whether an extension dtype is considered numeric (:issue:`22290`).
- The ``ExtensionArray`` constructor, ``_from_sequence`` now take the keyword arg ``copy=False`` (:issue:`21185`)
- Bug in :meth:`Series.get` for ``Series`` using ``ExtensionArray`` and integer index (:issue:`21257`)
- :meth:`~Series.shift` now dispatches to :meth:`ExtensionArray.shift` (:issue:`22386`)
- :meth:`Series.combine()` works correctly with :class:`~pandas.api.extensions.ExtensionArray` inside of :class:`Series` (:issue:`20825`)
- :meth:`Series.combine()` with scalar argument now works for any function type (:issue:`21248`)
- :meth:`Series.astype` and :meth:`DataFrame.astype` now dispatch to :meth:`ExtensionArray.astype` (:issue:`21185:`).
- Slicing a single row of a ``DataFrame`` with multiple ExtensionArrays of the same type now preserves the dtype, rather than coercing to object (:issue:`22784`)
- Added :meth:`pandas.api.types.register_extension_dtype` to register an extension type with pandas (:issue:`22664`)
- Bug when concatenating multiple ``Series`` with different extension dtypes not casting to object dtype (:issue:`22994`)
- Series backed by an ``ExtensionArray`` now work with :func:`util.hash_pandas_object` (:issue:`23066`)
- Updated the ``.type`` attribute for ``PeriodDtype``, ``DatetimeTZDtype``, and ``IntervalDtype`` to be instances of the dtype (``Period``, ``Timestamp``, and ``Interval`` respectively) (:issue:`22938`)
- :func:`ExtensionArray.isna` is allowed to return an ``ExtensionArray`` (:issue:`22325`).
- Support for reduction operations such as ``sum``, ``mean`` via opt-in base class method override (:issue:`22762`)
- :meth:`Series.unstack` no longer converts extension arrays to object-dtype ndarrays. The output ``DataFrame`` will now have the same dtype as the input. This changes behavior for Categorical and Sparse data (:issue:`23077`).

.. _whatsnew_0240.api.incompatibilities:

Series and Index Data-Dtype Incompatibilities
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``Series`` and ``Index`` constructors now raise when the
data is incompatible with a passed ``dtype=`` (:issue:`15832`)

Previous Behavior:

.. code-block:: ipython

    In [4]: pd.Series([-1], dtype="uint64")
    Out [4]:
    0    18446744073709551615
    dtype: uint64

Current Behavior:

.. code-block:: ipython

    In [4]: pd.Series([-1], dtype="uint64")
    Out [4]:
    ...
    OverflowError: Trying to coerce negative values to unsigned integers

.. _whatsnew_0240.api.crosstab_dtypes

Crosstab Preserves Dtypes
^^^^^^^^^^^^^^^^^^^^^^^^^

:func:`crosstab` will preserve now dtypes in some cases that previously would
cast from integer dtype to floating dtype (:issue:`22019`)

Previous Behavior:

.. code-block:: ipython

    In [3]: df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
       ...:                    'c': [1, 1, np.nan, 1, 1]})
    In [4]: pd.crosstab(df.a, df.b, normalize='columns')
    Out[4]:
    b    3    4
    a
    1  0.5  0.0
    2  0.5  1.0

Current Behavior:

.. code-block:: ipython

    In [3]: df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
       ...:                    'c': [1, 1, np.nan, 1, 1]})
    In [4]: pd.crosstab(df.a, df.b, normalize='columns')

Datetimelike API Changes
^^^^^^^^^^^^^^^^^^^^^^^^

- For :class:`DatetimeIndex` and :class:`TimedeltaIndex` with non-``None`` ``freq`` attribute, addition or subtraction of integer-dtyped array or ``Index`` will return an object of the same class (:issue:`19959`)
- :class:`DateOffset` objects are now immutable. Attempting to alter one of these will now raise ``AttributeError`` (:issue:`21341`)
- :class:`PeriodIndex` subtraction of another ``PeriodIndex`` will now return an object-dtype :class:`Index` of :class:`DateOffset` objects instead of raising a ``TypeError`` (:issue:`20049`)
- :func:`cut` and :func:`qcut` now returns a :class:`DatetimeIndex` or :class:`TimedeltaIndex` bins when the input is datetime or timedelta dtype respectively and ``retbins=True`` (:issue:`19891`)
- :meth:`DatetimeIndex.to_period` and :meth:`Timestamp.to_period` will issue a warning when timezone information will be lost (:issue:`21333`)

.. _whatsnew_0240.api.other:

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

- :class:`DatetimeIndex` now accepts :class:`Int64Index` arguments as epoch timestamps (:issue:`20997`)
- Accessing a level of a ``MultiIndex`` with a duplicate name (e.g. in
  :meth:`~MultiIndex.get_level_values`) now raises a ``ValueError`` instead of
  a ``KeyError`` (:issue:`21678`).
- Invalid construction of ``IntervalDtype`` will now always raise a ``TypeError`` rather than a ``ValueError`` if the subdtype is invalid (:issue:`21185`)
- Trying to reindex a ``DataFrame`` with a non unique ``MultiIndex`` now raises a ``ValueError`` instead of an ``Exception`` (:issue:`21770`)
- :meth:`PeriodIndex.tz_convert` and :meth:`PeriodIndex.tz_localize` have been removed (:issue:`21781`)
- :class:`Index` subtraction will attempt to operate element-wise instead of raising ``TypeError`` (:issue:`19369`)
- :class:`pandas.io.formats.style.Styler` supports a ``number-format`` property when using :meth:`~pandas.io.formats.style.Styler.to_excel` (:issue:`22015`)
- :meth:`DataFrame.corr` and :meth:`Series.corr` now raise a ``ValueError`` along with a helpful error message instead of a ``KeyError`` when supplied with an invalid method (:issue:`22298`)
- :meth:`shift` will now always return a copy, instead of the previous behaviour of returning self when shifting by 0 (:issue:`22397`)
- :meth:`DataFrame.set_index` now allows all one-dimensional list-likes, raises a ``TypeError`` for incorrect types,
  has an improved ``KeyError`` message, and will not fail on duplicate column names with ``drop=True``. (:issue:`22484`)
- Slicing a single row of a DataFrame with multiple ExtensionArrays of the same type now preserves the dtype, rather than coercing to object (:issue:`22784`)
- :class:`DateOffset` attribute `_cacheable` and method `_should_cache` have been removed (:issue:`23118`)

.. _whatsnew_0240.deprecations:

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

- :meth:`DataFrame.to_stata`, :meth:`read_stata`, :class:`StataReader` and :class:`StataWriter` have deprecated the ``encoding`` argument. The encoding of a Stata dta file is determined by the file type and cannot be changed (:issue:`21244`)
- :meth:`MultiIndex.to_hierarchical` is deprecated and will be removed in a future version (:issue:`21613`)
- :meth:`Series.ptp` is deprecated. Use ``numpy.ptp`` instead (:issue:`21614`)
- :meth:`Series.compress` is deprecated. Use ``Series[condition]`` instead (:issue:`18262`)
- The signature of :meth:`Series.to_csv` has been uniformed to that of :meth:`DataFrame.to_csv`: the name of the first argument is now ``path_or_buf``, the order of subsequent arguments has changed, the ``header`` argument now defaults to ``True``. (:issue:`19715`)
- :meth:`Categorical.from_codes` has deprecated providing float values for the ``codes`` argument. (:issue:`21767`)
- :func:`pandas.read_table` is deprecated. Instead, use :func:`pandas.read_csv` passing ``sep='\t'`` if necessary (:issue:`21948`)
- :meth:`Series.str.cat` has deprecated using arbitrary list-likes *within* list-likes. A list-like container may still contain
  many ``Series``, ``Index`` or 1-dimensional ``np.ndarray``, or alternatively, only scalar values. (:issue:`21950`)
- :meth:`FrozenNDArray.searchsorted` has deprecated the ``v`` parameter in favor of ``value`` (:issue:`14645`)
- :func:`DatetimeIndex.shift` and :func:`PeriodIndex.shift` now accept ``periods`` argument instead of ``n`` for consistency with :func:`Index.shift` and :func:`Series.shift`. Using ``n`` throws a deprecation warning (:issue:`22458`, :issue:`22912`)
- The ``fastpath`` keyword of the different Index constructors is deprecated (:issue:`23110`).
- :meth:`Timestamp.tz_localize`, :meth:`DatetimeIndex.tz_localize`, and :meth:`Series.tz_localize` have deprecated the ``errors`` argument in favor of the ``nonexistent`` argument (:issue:`8917`)

.. _whatsnew_0240.prior_deprecations:

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

- The ``LongPanel`` and ``WidePanel`` classes have been removed (:issue:`10892`)
- :meth:`Series.repeat` has renamed the ``reps`` argument to ``repeats`` (:issue:`14645`)
- Several private functions were removed from the (non-public) module ``pandas.core.common`` (:issue:`22001`)
- Removal of the previously deprecated module ``pandas.core.datetools`` (:issue:`14105`, :issue:`14094`)
- Strings passed into :meth:`DataFrame.groupby` that refer to both column and index levels will raise a ``ValueError`` (:issue:`14432`)
- :meth:`Index.repeat` and :meth:`MultiIndex.repeat` have renamed the ``n`` argument to ``repeats`` (:issue:`14645`)
- Removal of the previously deprecated ``as_indexer`` keyword completely from ``str.match()`` (:issue:`22356`, :issue:`6581`)
- Removed the ``pandas.formats.style`` shim for :class:`pandas.io.formats.style.Styler` (:issue:`16059`)
- :meth:`Categorical.searchsorted` and :meth:`Series.searchsorted` have renamed the ``v`` argument to ``value`` (:issue:`14645`)
- :meth:`TimedeltaIndex.searchsorted`, :meth:`DatetimeIndex.searchsorted`, and :meth:`PeriodIndex.searchsorted` have renamed the ``key`` argument to ``value`` (:issue:`14645`)
- Removal of the previously deprecated module ``pandas.json`` (:issue:`19944`)
- :meth:`SparseArray.get_values` and :meth:`SparseArray.to_dense` have dropped the ``fill`` parameter (:issue:`14686`)
- :meth:`SparseSeries.to_dense` has dropped the ``sparse_only`` parameter (:issue:`14686`)

.. _whatsnew_0240.performance:

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

- Slicing Series and Dataframes with an monotonically increasing :class:`CategoricalIndex`
  is now very fast and has speed comparable to slicing with an ``Int64Index``.
  The speed increase is both when indexing by label (using .loc) and position(.iloc) (:issue:`20395`)
  Slicing a monotonically increasing :class:`CategoricalIndex` itself (i.e. ``ci[1000:2000]``)
  shows similar speed improvements as above (:issue:`21659`)
- Improved performance of :func:`Series.describe` in case of numeric dtpyes (:issue:`21274`)
- Improved performance of :func:`pandas.core.groupby.GroupBy.rank` when dealing with tied rankings (:issue:`21237`)
- Improved performance of :func:`DataFrame.set_index` with columns consisting of :class:`Period` objects (:issue:`21582`, :issue:`21606`)
- Improved performance of membership checks in :class:`Categorical` and :class:`CategoricalIndex`
  (i.e. ``x in cat``-style checks are much faster). :meth:`CategoricalIndex.contains`
  is likewise much faster (:issue:`21369`, :issue:`21508`)
- Improved performance of :meth:`HDFStore.groups` (and dependent functions like
  :meth:`~HDFStore.keys`.  (i.e. ``x in store`` checks are much faster)
  (:issue:`21372`)
- Improved the performance of :func:`pandas.get_dummies` with ``sparse=True`` (:issue:`21997`)
- Improved performance of :func:`IndexEngine.get_indexer_non_unique` for sorted, non-unique indexes (:issue:`9466`)
- Improved performance of :func:`PeriodIndex.unique` (:issue:`23083`)


.. _whatsnew_0240.docs:

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

- Added sphinx spelling extension, updated documentation on how to use the spell check (:issue:`21079`)
-
-

.. _whatsnew_0240.bug_fixes:

Bug Fixes
~~~~~~~~~

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

- Bug in :meth:`Categorical.from_codes` where ``NaN`` values in ``codes`` were silently converted to ``0`` (:issue:`21767`). In the future this will raise a ``ValueError``. Also changes the behavior of ``.from_codes([1.1, 2.0])``.
- Bug in :meth:`Categorical.sort_values` where ``NaN`` values were always positioned in front regardless of ``na_position`` value. (:issue:`22556`).
- Bug when indexing with a boolean-valued ``Categorical``. Now a boolean-valued ``Categorical`` is treated as a boolean mask (:issue:`22665`)
- Constructing a :class:`CategoricalIndex` with empty values and boolean categories was raising a ``ValueError`` after a change to dtype coercion (:issue:`22702`).
- Bug in :meth:`Categorical.take` with a user-provided ``fill_value`` not encoding the ``fill_value``, which could result in a ``ValueError``, incorrect results, or a segmentation fault (:issue:`23296`).

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

- Fixed bug where two :class:`DateOffset` objects with different ``normalize`` attributes could evaluate as equal (:issue:`21404`)
- Fixed bug where :meth:`Timestamp.resolution` incorrectly returned 1-microsecond ``timedelta`` instead of 1-nanosecond :class:`Timedelta` (:issue:`21336`, :issue:`21365`)
- Bug in :func:`to_datetime` that did not consistently return an :class:`Index` when ``box=True`` was specified (:issue:`21864`)
- Bug in :class:`DatetimeIndex` comparisons where string comparisons incorrectly raises ``TypeError`` (:issue:`22074`)
- Bug in :class:`DatetimeIndex` comparisons when comparing against ``timedelta64[ns]`` dtyped arrays; in some cases ``TypeError`` was incorrectly raised, in others it incorrectly failed to raise (:issue:`22074`)
- Bug in :class:`DatetimeIndex` comparisons when comparing against object-dtyped arrays (:issue:`22074`)
- Bug in :class:`DataFrame` with ``datetime64[ns]`` dtype addition and subtraction with ``Timedelta``-like objects (:issue:`22005`, :issue:`22163`)
- Bug in :class:`DataFrame` with ``datetime64[ns]`` dtype addition and subtraction with ``DateOffset`` objects returning an ``object`` dtype instead of ``datetime64[ns]`` dtype (:issue:`21610`, :issue:`22163`)
- Bug in :class:`DataFrame` with ``datetime64[ns]`` dtype comparing against ``NaT`` incorrectly (:issue:`22242`, :issue:`22163`)
- Bug in :class:`DataFrame` with ``datetime64[ns]`` dtype subtracting ``Timestamp``-like object incorrectly returned ``datetime64[ns]`` dtype instead of ``timedelta64[ns]`` dtype (:issue:`8554`, :issue:`22163`)
- Bug in :class:`DataFrame` with ``datetime64[ns]`` dtype subtracting ``np.datetime64`` object with non-nanosecond unit failing to convert to nanoseconds (:issue:`18874`, :issue:`22163`)
- Bug in :class:`DataFrame` comparisons against ``Timestamp``-like objects failing to raise ``TypeError`` for inequality checks with mismatched types (:issue:`8932`, :issue:`22163`)
- Bug in :class:`DataFrame` with mixed dtypes including ``datetime64[ns]`` incorrectly raising ``TypeError`` on equality comparisons (:issue:`13128`, :issue:`22163`)
- Bug in :meth:`DataFrame.eq` comparison against ``NaT`` incorrectly returning ``True`` or ``NaN`` (:issue:`15697`, :issue:`22163`)
- Bug in :class:`DatetimeIndex` subtraction that incorrectly failed to raise ``OverflowError`` (:issue:`22492`, :issue:`22508`)
- Bug in :class:`DatetimeIndex` incorrectly allowing indexing with ``Timedelta`` object (:issue:`20464`)
- Bug in :class:`DatetimeIndex` where frequency was being set if original frequency was ``None`` (:issue:`22150`)
- Bug in rounding methods of :class:`DatetimeIndex` (:meth:`~DatetimeIndex.round`, :meth:`~DatetimeIndex.ceil`, :meth:`~DatetimeIndex.floor`) and :class:`Timestamp` (:meth:`~Timestamp.round`, :meth:`~Timestamp.ceil`, :meth:`~Timestamp.floor`) could give rise to loss of precision (:issue:`22591`)
- Bug in :func:`to_datetime` with an :class:`Index` argument that would drop the ``name`` from the result (:issue:`21697`)
- Bug in :class:`PeriodIndex` where adding or subtracting a :class:`timedelta` or :class:`Tick` object produced incorrect results (:issue:`22988`)
- Bug in :func:`date_range` when decrementing a start date to a past end date by a negative frequency (:issue:`23270`)
- Bug in :func:`DataFrame.combine` with datetimelike values raising a TypeError (:issue:`23079`)

Timedelta
^^^^^^^^^
- Bug in :class:`DataFrame` with ``timedelta64[ns]`` dtype division by ``Timedelta``-like scalar incorrectly returning ``timedelta64[ns]`` dtype instead of ``float64`` dtype (:issue:`20088`, :issue:`22163`)
- Bug in adding a :class:`Index` with object dtype to a :class:`Series` with ``timedelta64[ns]`` dtype incorrectly raising (:issue:`22390`)
- Bug in multiplying a :class:`Series` with numeric dtype against a ``timedelta`` object (:issue:`22390`)
- Bug in :class:`Series` with numeric dtype when adding or subtracting an an array or ``Series`` with ``timedelta64`` dtype (:issue:`22390`)
- Bug in :class:`Index` with numeric dtype when multiplying or dividing an array with dtype ``timedelta64`` (:issue:`22390`)
- Bug in :class:`TimedeltaIndex` incorrectly allowing indexing with ``Timestamp`` object (:issue:`20464`)
- Fixed bug where subtracting :class:`Timedelta` from an object-dtyped array would raise ``TypeError`` (:issue:`21980`)
- Fixed bug in adding a :class:`DataFrame` with all-`timedelta64[ns]` dtypes to a :class:`DataFrame` with all-integer dtypes returning incorrect results instead of raising ``TypeError`` (:issue:`22696`)


Timezones
^^^^^^^^^

- Bug in :meth:`DatetimeIndex.shift` where an ``AssertionError`` would raise when shifting across DST (:issue:`8616`)
- Bug in :class:`Timestamp` constructor where passing an invalid timezone offset designator (``Z``) would not raise a ``ValueError`` (:issue:`8910`)
- Bug in :meth:`Timestamp.replace` where replacing at a DST boundary would retain an incorrect offset (:issue:`7825`)
- Bug in :meth:`Series.replace` with ``datetime64[ns, tz]`` data when replacing ``NaT`` (:issue:`11792`)
- Bug in :class:`Timestamp` when passing different string date formats with a timezone offset would produce different timezone offsets (:issue:`12064`)
- Bug when comparing a tz-naive :class:`Timestamp` to a tz-aware :class:`DatetimeIndex` which would coerce the :class:`DatetimeIndex` to tz-naive (:issue:`12601`)
- Bug in :meth:`Series.truncate` with a tz-aware :class:`DatetimeIndex` which would cause a core dump (:issue:`9243`)
- Bug in :class:`Series` constructor which would coerce tz-aware and tz-naive :class:`Timestamp` to tz-aware (:issue:`13051`)
- Bug in :class:`Index` with ``datetime64[ns, tz]`` dtype that did not localize integer data correctly (:issue:`20964`)
- Bug in :class:`DatetimeIndex` where constructing with an integer and tz would not localize correctly (:issue:`12619`)
- Fixed bug where :meth:`DataFrame.describe` and :meth:`Series.describe` on tz-aware datetimes did not show `first` and `last` result (:issue:`21328`)
- Bug in :class:`DatetimeIndex` comparisons failing to raise ``TypeError`` when comparing timezone-aware ``DatetimeIndex`` against ``np.datetime64`` (:issue:`22074`)
- Bug in ``DataFrame`` assignment with a timezone-aware scalar (:issue:`19843`)
- Bug in :func:`Dataframe.asof` that raised a ``TypeError`` when attempting to compare tz-naive and tz-aware timestamps (:issue:`21194`)
- Bug when constructing a :class:`DatetimeIndex` with :class:`Timestamp`s constructed with the ``replace`` method across DST (:issue:`18785`)
- Bug when setting a new value with :meth:`DataFrame.loc` with a :class:`DatetimeIndex` with a DST transition (:issue:`18308`, :issue:`20724`)
- Bug in :meth:`DatetimeIndex.unique` that did not re-localize tz-aware dates correctly (:issue:`21737`)
- Bug when indexing a :class:`Series` with a DST transition (:issue:`21846`)
- Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` where an ``AmbiguousTimeError`` or ``NonExistentTimeError`` would raise if a timezone aware timeseries ended on a DST transition (:issue:`19375`, :issue:`10117`)

Offsets
^^^^^^^

- Bug in :class:`FY5253` where date offsets could incorrectly raise an ``AssertionError`` in arithmetic operatons (:issue:`14774`)
- Bug in :class:`DateOffset` where keyword arguments ``week`` and ``milliseconds`` were accepted and ignored.  Passing these will now raise ``ValueError`` (:issue:`19398`)
-

Numeric
^^^^^^^

- Bug in :class:`Series` ``__rmatmul__`` doesn't support matrix vector multiplication (:issue:`21530`)
- Bug in :func:`factorize` fails with read-only array (:issue:`12813`)
- Fixed bug in :func:`unique` handled signed zeros inconsistently: for some inputs 0.0 and -0.0 were treated as equal and for some inputs as different. Now they are treated as equal for all inputs (:issue:`21866`)
- Bug in :meth:`DataFrame.agg`, :meth:`DataFrame.transform` and :meth:`DataFrame.apply` where,
  when supplied with a list of functions and ``axis=1`` (e.g. ``df.apply(['sum', 'mean'], axis=1)``),
  a ``TypeError`` was wrongly raised. For all three methods such calculation are now done correctly. (:issue:`16679`).
- Bug in :class:`Series` comparison against datetime-like scalars and arrays (:issue:`22074`)
- Bug in :class:`DataFrame` multiplication between boolean dtype and integer returning ``object`` dtype instead of integer dtype (:issue:`22047`, :issue:`22163`)
- Bug in :meth:`DataFrame.apply` where, when supplied with a string argument and additional positional or keyword arguments (e.g. ``df.apply('sum', min_count=1)``), a ``TypeError`` was wrongly raised (:issue:`22376`)
- Bug in :meth:`DataFrame.astype` to extension dtype may raise ``AttributeError`` (:issue:`22578`)
- Bug in :class:`DataFrame` with ``timedelta64[ns]`` dtype arithmetic operations with ``ndarray`` with integer dtype incorrectly treating the narray as ``timedelta64[ns]`` dtype (:issue:`23114`)
- Bug in :meth:`Series.rpow` with object dtype ``NaN`` for ``1 ** NA`` instead of ``1`` (:issue:`22922`).

Strings
^^^^^^^

-
-
-

Interval
^^^^^^^^

- Bug in the :class:`IntervalIndex` constructor where the ``closed`` parameter did not always override the inferred ``closed`` (:issue:`19370`)
- Bug in the ``IntervalIndex`` repr where a trailing comma was missing after the list of intervals (:issue:`20611`)
- Bug in :class:`Interval` where scalar arithmetic operations did not retain the ``closed`` value (:issue:`22313`)
- Bug in :class:`IntervalIndex` where indexing with datetime-like values raised a ``KeyError`` (:issue:`20636`)

Indexing
^^^^^^^^

- The traceback from a ``KeyError`` when asking ``.loc`` for a single missing label is now shorter and more clear (:issue:`21557`)
- When ``.ix`` is asked for a missing integer label in a :class:`MultiIndex` with a first level of integer type, it now raises a ``KeyError``, consistently with the case of a flat :class:`Int64Index`, rather than falling back to positional indexing (:issue:`21593`)
- Bug in :meth:`DatetimeIndex.reindex` when reindexing a tz-naive and tz-aware :class:`DatetimeIndex` (:issue:`8306`)
- Bug in :class:`DataFrame` when setting values with ``.loc`` and a timezone aware :class:`DatetimeIndex` (:issue:`11365`)
- ``DataFrame.__getitem__`` now accepts dictionaries and dictionary keys as list-likes of labels, consistently with ``Series.__getitem__`` (:issue:`21294`)
- Fixed ``DataFrame[np.nan]`` when columns are non-unique (:issue:`21428`)
- Bug when indexing :class:`DatetimeIndex` with nanosecond resolution dates and timezones (:issue:`11679`)
- Bug where indexing with a Numpy array containing negative values would mutate the indexer (:issue:`21867`)
- Bug where mixed indexes wouldn't allow integers for ``.at`` (:issue:`19860`)
- ``Float64Index.get_loc`` now raises ``KeyError`` when boolean key passed. (:issue:`19087`)
- Bug in :meth:`DataFrame.loc` when indexing with an :class:`IntervalIndex` (:issue:`19977`)
- :class:`Index` no longer mangles ``None``, ``NaN`` and ``NaT``, i.e. they are treated as three different keys. However, for numeric Index all three are still coerced to a ``NaN`` (:issue:`22332`)
- Bug in `scalar in Index` if scalar is a float while the ``Index`` is of integer dtype (:issue:`22085`)
- Bug in `MultiIndex.set_levels` when levels value is not subscriptable (:issue:`23273`)

Missing
^^^^^^^

- Bug in :func:`DataFrame.fillna` where a ``ValueError`` would raise when one column contained a ``datetime64[ns, tz]`` dtype (:issue:`15522`)
- Bug in :func:`Series.hasnans` that could be incorrectly cached and return incorrect answers if null elements are introduced after an initial call (:issue:`19700`)
- :func:`Series.isin` now treats all NaN-floats as equal also for `np.object`-dtype. This behavior is consistent with the behavior for float64 (:issue:`22119`)
- :func:`unique` no longer mangles NaN-floats and the ``NaT``-object for `np.object`-dtype, i.e. ``NaT`` is no longer coerced to a NaN-value and is treated as a different entity. (:issue:`22295`)


MultiIndex
^^^^^^^^^^

- Removed compatibility for :class:`MultiIndex` pickles prior to version 0.8.0; compatibility with :class:`MultiIndex` pickles from version 0.13 forward is maintained (:issue:`21654`)
- :meth:`MultiIndex.get_loc_level` (and as a consequence, ``.loc`` on a :class:`MultiIndex`ed object) will now raise a ``KeyError``, rather than returning an empty ``slice``, if asked a label which is present in the ``levels`` but is unused (:issue:`22221`)
- Fix ``TypeError`` in Python 3 when creating :class:`MultiIndex` in which some levels have mixed types, e.g. when some labels are tuples (:issue:`15457`)

I/O
^^^

.. _whatsnew_0240.bug_fixes.nan_with_str_dtype:

Proper handling of `np.NaN` in a string data-typed column with the Python engine
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

There was bug in :func:`read_excel` and :func:`read_csv` with the Python
engine, where missing values turned to ``'nan'`` with ``dtype=str`` and
``na_filter=True``. Now, these missing values are converted to the string
missing indicator, ``np.nan``. (:issue `20377`)

.. ipython:: python
   :suppress:

   from pandas.compat import StringIO

Previous Behavior:

.. code-block:: ipython

   In [5]: data = 'a,b,c\n1,,3\n4,5,6'
   In [6]: df = pd.read_csv(StringIO(data), engine='python', dtype=str, na_filter=True)
   In [7]: df.loc[0, 'b']
   Out[7]:
   'nan'

Current Behavior:

.. ipython:: python

   data = 'a,b,c\n1,,3\n4,5,6'
   df = pd.read_csv(StringIO(data), engine='python', dtype=str, na_filter=True)
   df.loc[0, 'b']

Notice how we now instead output ``np.nan`` itself instead of a stringified form of it.

- :func:`read_html()` no longer ignores all-whitespace ``<tr>`` within ``<thead>`` when considering the ``skiprows`` and ``header`` arguments. Previously, users had to decrease their ``header`` and ``skiprows`` values on such tables to work around the issue. (:issue:`21641`)
- :func:`read_excel()` will correctly show the deprecation warning for previously deprecated ``sheetname`` (:issue:`17994`)
- :func:`read_csv()` and func:`read_table()` will throw ``UnicodeError`` and not coredump on badly encoded strings (:issue:`22748`)
- :func:`read_csv()` will correctly parse timezone-aware datetimes (:issue:`22256`)
- :func:`read_sas()` will parse numbers in sas7bdat-files that have width less than 8 bytes correctly. (:issue:`21616`)
- :func:`read_sas()` will correctly parse sas7bdat files with many columns (:issue:`22628`)
- :func:`read_sas()` will correctly parse sas7bdat files with data page types having also bit 7 set (so page type is 128 + 256 = 384) (:issue:`16615`)
- Bug in :meth:`detect_client_encoding` where potential ``IOError`` goes unhandled when importing in a mod_wsgi process due to restricted access to stdout. (:issue:`21552`)
- Bug in :func:`to_string()` that broke column alignment when ``index=False`` and width of first column's values is greater than the width of first column's header (:issue:`16839`, :issue:`13032`)

Plotting
^^^^^^^^

- Bug in :func:`DataFrame.plot.scatter` and :func:`DataFrame.plot.hexbin` caused x-axis label and ticklabels to disappear when colorbar was on in IPython inline backend (:issue:`10611`, :issue:`10678`, and :issue:`20455`)
- Bug in plotting a Series with datetimes using :func:`matplotlib.axes.Axes.scatter` (:issue:`22039`)

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

- Bug in :func:`pandas.core.groupby.GroupBy.first` and :func:`pandas.core.groupby.GroupBy.last` with ``as_index=False`` leading to the loss of timezone information (:issue:`15884`)
- Bug in :meth:`DatetimeIndex.resample` when downsampling across a DST boundary (:issue:`8531`)
- Bug where ``ValueError`` is wrongly raised when calling :func:`~pandas.core.groupby.SeriesGroupBy.count` method of a
  ``SeriesGroupBy`` when the grouping variable only contains NaNs and numpy version < 1.13 (:issue:`21956`).
- Multiple bugs in :func:`pandas.core.Rolling.min` with ``closed='left'`` and a
  datetime-like index leading to incorrect results and also segfault. (:issue:`21704`)
- Bug in :meth:`Resampler.apply` when passing postiional arguments to applied func (:issue:`14615`).
- Bug in :meth:`Series.resample` when passing ``numpy.timedelta64`` to ``loffset`` kwarg (:issue:`7687`).
- Bug in :meth:`Resampler.asfreq` when frequency of ``TimedeltaIndex`` is a subperiod of a new frequency (:issue:`13022`).
- Bug in :meth:`SeriesGroupBy.mean` when values were integral but could not fit inside of int64, overflowing instead. (:issue:`22487`)
- :func:`RollingGroupby.agg` and :func:`ExpandingGroupby.agg` now support multiple aggregation functions as parameters (:issue:`15072`)
- Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` when resampling by a weekly offset (``'W'``) across a DST transition (:issue:`9119`, :issue:`21459`)

Reshaping
^^^^^^^^^

- Bug in :func:`pandas.concat` when joining resampled DataFrames with timezone aware index (:issue:`13783`)
- Bug in :meth:`Series.combine_first` with ``datetime64[ns, tz]`` dtype which would return tz-naive result (:issue:`21469`)
- Bug in :meth:`Series.where` and :meth:`DataFrame.where` with ``datetime64[ns, tz]`` dtype (:issue:`21546`)
- Bug in :meth:`Series.mask` and :meth:`DataFrame.mask` with ``list`` conditionals (:issue:`21891`)
- Bug in :meth:`DataFrame.replace` raises RecursionError when converting OutOfBounds ``datetime64[ns, tz]`` (:issue:`20380`)
- :func:`pandas.core.groupby.GroupBy.rank` now raises a ``ValueError`` when an invalid value is passed for argument ``na_option`` (:issue:`22124`)
- Bug in :func:`get_dummies` with Unicode attributes in Python 2 (:issue:`22084`)
- Bug in :meth:`DataFrame.replace` raises ``RecursionError`` when replacing empty lists (:issue:`22083`)
- Bug in :meth:`Series.replace` and meth:`DataFrame.replace` when dict is used as the ``to_replace`` value and one key in the dict is is another key's value, the results were inconsistent between using integer key and using string key (:issue:`20656`)
- Bug in :meth:`DataFrame.drop_duplicates` for empty ``DataFrame`` which incorrectly raises an error (:issue:`20516`)
- Bug in :func:`pandas.wide_to_long` when a string is passed to the stubnames argument and a column name is a substring of that stubname (:issue:`22468`)
- Bug in :func:`merge` when merging ``datetime64[ns, tz]`` data that contained a DST transition (:issue:`18885`)
- Bug in :func:`merge_asof` when merging on float values within defined tolerance (:issue:`22981`)
- Bug in :func:`pandas.concat` when concatenating a multicolumn DataFrame with tz-aware data against a DataFrame with a different number of columns (:issue`22796`)
- Bug in :func:`merge_asof` where confusing error message raised when attempting to merge with missing values (:issue:`23189`)
- Bug in :meth:`DataFrame.nsmallest` and :meth:`DataFrame.nlargest` for dataframes that have :class:`MultiIndex`ed columns (:issue:`23033`).

.. _whatsnew_0240.bug_fixes.sparse:

Sparse
^^^^^^

- Updating a boolean, datetime, or timedelta column to be Sparse now works (:issue:`22367`)
- Bug in :meth:`Series.to_sparse` with Series already holding sparse data not constructing properly (:issue:`22389`)
- Providing a ``sparse_index`` to the SparseArray constructor no longer defaults the na-value to ``np.nan`` for all dtypes. The correct na_value for ``data.dtype`` is now used.
- Bug in ``SparseArray.nbytes`` under-reporting its memory usage by not including the size of its sparse index.
- Improved performance of :meth:`Series.shift` for non-NA ``fill_value``, as values are no longer converted to a dense array.
- Bug in ``DataFrame.groupby`` not including ``fill_value`` in the groups for non-NA ``fill_value`` when grouping by a sparse column (:issue:`5078`)
- Bug in unary inversion operator (``~``) on a ``SparseSeries`` with boolean values. The performance of this has also been improved (:issue:`22835`)
- Bug in :meth:`SparseArary.unique` not returning the unique values (:issue:`19595`)

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

- Building pandas for development now requires ``cython >= 0.28.2`` (:issue:`21688`)
- Testing pandas now requires ``hypothesis>=3.58``.  You can find `the Hypothesis docs here <https://hypothesis.readthedocs.io/en/latest/index.html>`_, and a pandas-specific introduction :ref:`in the contributing guide <using-hypothesis>`. (:issue:`22280`)
-

Other
^^^^^

- :meth:`~pandas.io.formats.style.Styler.background_gradient` now takes a ``text_color_threshold`` parameter to automatically lighten the text color based on the luminance of the background color. This improves readability with dark background colors without the need to limit the background colormap range. (:issue:`21258`)
- Require at least 0.28.2 version of ``cython`` to support read-only memoryviews (:issue:`21688`)
- :meth:`~pandas.io.formats.style.Styler.background_gradient` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` (:issue:`15204`)
- :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
- :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
- Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
- Bug in :meth:`DataFrame.combine_first` in which column types were unexpectedly converted to float (:issue:`20699`)