diff --git a/doc/source/release.rst b/doc/source/release.rst index 6c3e7f847b485..a3289b1144863 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -52,7 +52,7 @@ Highlights include: - Integration with `Apache Parquet `__, including a new top-level :func:`read_parquet` function and :meth:`DataFrame.to_parquet` method, see :ref:`here `. - New user-facing :class:`pandas.api.types.CategoricalDtype` for specifying categoricals independent of the data, see :ref:`here `. -- The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames is now consistent and no longer depends on whether `bottleneck `__ is installed, see :ref:`here `. +- The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames is now consistent and no longer depends on whether `bottleneck `__ is installed, and ``sum`` and ``prod`` on empty Series now return NaN instead of 0, see :ref:`here `. - Compatibility fixes for pypy, see :ref:`here `. - Additions to the ``drop``, ``reindex`` and ``rename`` API to make them more consistent, see :ref:`here `. - Addition of the new methods ``DataFrame.infer_objects`` (see :ref:`here `) and ``GroupBy.pipe`` (see :ref:`here `). diff --git a/doc/source/whatsnew/v0.21.0.txt b/doc/source/whatsnew/v0.21.0.txt index 4c460eeb85b82..89e2d3006696c 100644 --- a/doc/source/whatsnew/v0.21.0.txt +++ b/doc/source/whatsnew/v0.21.0.txt @@ -12,7 +12,7 @@ Highlights include: - Integration with `Apache Parquet `__, including a new top-level :func:`read_parquet` function and :meth:`DataFrame.to_parquet` method, see :ref:`here `. - New user-facing :class:`pandas.api.types.CategoricalDtype` for specifying categoricals independent of the data, see :ref:`here `. -- The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames is now consistent and no longer depends on whether `bottleneck `__ is installed, see :ref:`here `. +- The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames is now consistent and no longer depends on whether `bottleneck `__ is installed, and ``sum`` and ``prod`` on empty Series now return NaN instead of 0, see :ref:`here `. - Compatibility fixes for pypy, see :ref:`here `. - Additions to the ``drop``, ``reindex`` and ``rename`` API to make them more consistent, see :ref:`here `. - Addition of the new methods ``DataFrame.infer_objects`` (see :ref:`here `) and ``GroupBy.pipe`` (see :ref:`here `). @@ -369,11 +369,11 @@ Additionally, support has been dropped for Python 3.4 (:issue:`15251`). .. _whatsnew_0210.api_breaking.bottleneck: -Sum/Prod of all-NaN Series/DataFrames is now consistently NaN -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Sum/Prod of all-NaN or empty Series/DataFrames is now consistently NaN +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The behavior of ``sum`` and ``prod`` on all-NaN Series/DataFrames no longer depends on -whether `bottleneck `__ is installed. (:issue:`9422`, :issue:`15507`). +whether `bottleneck `__ is installed, and return value of ``sum`` and ``prod`` on an empty Series has changed (:issue:`9422`, :issue:`15507`). Calling ``sum`` or ``prod`` on an empty or all-``NaN`` ``Series``, or columns of a ``DataFrame``, will result in ``NaN``. See the :ref:`docs `. @@ -381,35 +381,35 @@ Calling ``sum`` or ``prod`` on an empty or all-``NaN`` ``Series``, or columns of s = Series([np.nan]) -Previously NO ``bottleneck`` +Previously WITHOUT ``bottleneck`` installed: .. code-block:: ipython In [2]: s.sum() Out[2]: np.nan -Previously WITH ``bottleneck`` +Previously WITH ``bottleneck``: .. code-block:: ipython In [2]: s.sum() Out[2]: 0.0 -New Behavior, without regard to the bottleneck installation. +New Behavior, without regard to the bottleneck installation: .. ipython:: python s.sum() -Note that this also changes the sum of an empty ``Series`` - -Previously regardless of ``bottlenck`` +Note that this also changes the sum of an empty ``Series``. Previously this always returned 0 regardless of a ``bottlenck`` installation: .. code-block:: ipython In [1]: pd.Series([]).sum() Out[1]: 0 +but for consistency with the all-NaN case, this was changed to return NaN as well: + .. ipython:: python pd.Series([]).sum() @@ -877,6 +877,28 @@ New Behavior: pd.interval_range(start=0, end=4) +.. _whatsnew_0210.api.mpl_converters: + +No Automatic Matplotlib Converters +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Pandas no longer registers our ``date``, ``time``, ``datetime``, +``datetime64``, and ``Period`` converters with matplotlib when pandas is +imported. Matplotlib plot methods (``plt.plot``, ``ax.plot``, ...), will not +nicely format the x-axis for ``DatetimeIndex`` or ``PeriodIndex`` values. You +must explicitly register these methods: + +.. ipython:: python + + from pandas.tseries import converter + converter.register() + + fig, ax = plt.subplots() + plt.plot(pd.date_range('2017', periods=6), range(6)) + +Pandas built-in ``Series.plot`` and ``DataFrame.plot`` *will* register these +converters on first-use (:issue:17710). + .. _whatsnew_0210.api: Other API Changes @@ -900,8 +922,6 @@ Other API Changes - Renamed non-functional ``index`` to ``index_col`` in :func:`read_stata` to improve API consistency (:issue:`16342`) - Bug in :func:`DataFrame.drop` caused boolean labels ``False`` and ``True`` to be treated as labels 0 and 1 respectively when dropping indices from a numeric index. This will now raise a ValueError (:issue:`16877`) - 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`). -- Pandas no longer registers matplotlib converters on import. The converters - will be registered and used when the first plot is draw (:issue:`17710`) .. _whatsnew_0210.deprecations: