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Author: rhshadrach

doc/source/whatsnew/v0.23.0.rst

@@ -194,195 +194,6 @@ levels <merging.merge_on_columns_and_levels>` documentation section.
 
 .. _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_023.enhancements.extension:
-
-Extending pandas with custom types (experimental)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-pandas now supports storing array-like objects that aren't necessarily 1-D NumPy
-arrays as columns in a DataFrame or values in a Series. This allows third-party
-libraries to implement extensions to NumPy's types, similar to how pandas
-implemented categoricals, datetimes with timezones, periods, and intervals.
-
-As a demonstration, we'll use cyberpandas_, which provides an ``IPArray`` type
-for storing ip addresses.
-
-.. code-block:: ipython
-
-   In [1]: from cyberpandas import IPArray
-
-   In [2]: values = IPArray([
-      ...:     0,
-      ...:     3232235777,
-      ...:     42540766452641154071740215577757643572
-      ...: ])
-      ...:
-      ...:
-
-``IPArray`` isn't a normal 1-D NumPy array, but because it's a pandas
-:class:`~pandas.api.extensions.ExtensionArray`, it can be stored properly inside pandas' containers.
-
-.. code-block:: ipython
-
-   In [3]: ser = pd.Series(values)
-
-   In [4]: ser
-   Out[4]:
-   0                         0.0.0.0
-   1                     192.168.1.1
-   2    2001:db8:85a3::8a2e:370:7334
-   dtype: ip
-
-Notice that the dtype is ``ip``. The missing value semantics of the underlying
-array are respected:
-
-.. code-block:: ipython
-
-   In [5]: ser.isna()
-   Out[5]:
-   0     True
-   1    False
-   2    False
-   dtype: bool
-
-For more, see the :ref:`extension types <extending.extension-types>`
-documentation. If you build an extension array, publicize it on `the ecosystem page <https://pandas.pydata.org/community/ecosystem.html>`_.
-
-.. _cyberpandas: https://cyberpandas.readthedocs.io/en/latest/
-
-
-.. _whatsnew_0230.enhancements.categorical_grouping:
-
-New ``observed`` keyword for excluding unobserved categories in ``GroupBy``
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Grouping by a categorical includes the unobserved categories in the output.
-When grouping by multiple categorical columns, this means you get the cartesian product of all the
-categories, including combinations where there are no observations, which can result in a large
-number of groups. We have added a keyword ``observed`` to control this behavior, it defaults to
-``observed=False`` for backward-compatibility. (:issue:`14942`, :issue:`8138`, :issue:`15217`, :issue:`17594`, :issue:`8669`, :issue:`20583`, :issue:`20902`)
-
-.. ipython:: python
-
-   cat1 = pd.Categorical(["a", "a", "b", "b"],
-                         categories=["a", "b", "z"], ordered=True)
-   cat2 = pd.Categorical(["c", "d", "c", "d"],
-                         categories=["c", "d", "y"], ordered=True)
-   df = pd.DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})
-   df['C'] = ['foo', 'bar'] * 2
-   df
-
-To show all values, the previous behavior:
-
-.. ipython:: python
-
-   df.groupby(['A', 'B', 'C'], observed=False).count()
-
-
-To show only observed values:
-
-.. ipython:: python
-
-   df.groupby(['A', 'B', 'C'], observed=True).count()
-
-For pivoting operations, this behavior is *already* controlled by the ``dropna`` keyword:
-
-.. ipython:: python
-
-   cat1 = pd.Categorical(["a", "a", "b", "b"],
-                         categories=["a", "b", "z"], ordered=True)
-   cat2 = pd.Categorical(["c", "d", "c", "d"],
-                         categories=["c", "d", "y"], ordered=True)
-   df = pd.DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})
-   df
-
-
-.. code-block:: ipython
-
-    In [1]: pd.pivot_table(df, values='values', index=['A', 'B'], dropna=True)
-
-    Out[1]:
-         values
-    A B
-    a c     1.0
-      d     2.0
-    b c     3.0
-      d     4.0
-
-    In [2]: pd.pivot_table(df, values='values', index=['A', 'B'], dropna=False)
-
-    Out[2]:
-         values
-    A B
-    a c     1.0
-      d     2.0
-      y     NaN
-    b c     3.0
-      d     4.0
-      y     NaN
-    z c     NaN
-      d     NaN
-      y     NaN
-
-
-.. _whatsnew_0230.enhancements.window_raw:
-
-Rolling/Expanding.apply() accepts ``raw=False`` to pass a ``Series`` to the function
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-:func:`Series.rolling().apply() <.Rolling.apply>`, :func:`DataFrame.rolling().apply() <.Rolling.apply>`,
-:func:`Series.expanding().apply() <.Expanding.apply>`, and :func:`DataFrame.expanding().apply() <.Expanding.apply>` have gained a ``raw=None`` parameter.
-This is similar to :func:`DataFame.apply`. This parameter, if ``True`` allows one to send a ``np.ndarray`` to the applied function. If ``False`` a ``Series`` will be passed. The
-default is ``None``, which preserves backward compatibility, so this will default to ``True``, sending an ``np.ndarray``.
-In a future version the default will be changed to ``False``, sending a ``Series``. (:issue:`5071`, :issue:`20584`)
-
-.. ipython:: python
-
-   s = pd.Series(np.arange(5), np.arange(5) + 1)
-   s
-
-Pass a ``Series``:
-
-.. ipython:: python
-
-   s.rolling(2, min_periods=1).apply(lambda x: x.iloc[-1], raw=False)
-
-Mimic the original behavior of passing a ndarray:
-
-.. ipython:: python
-
-   s.rolling(2, min_periods=1).apply(lambda x: x[-1], raw=True)
-
-
-.. _whatsnew_0210.enhancements.limit_area:
-
-
 .. _whatsnew_0.23.0.contributors:
 
 Contributors