DOC: groupby v0.14.0 doc changes

Author: jreback

doc/source/v0.14.0.txt

@@ -22,6 +22,8 @@ users upgrade to this version.
 
 - :ref:`API Changes <whatsnew_0140.api>`
 
+- :ref:`Groupby API Changes <whatsnew_0140.groupby>`
+
 - :ref:`Performance Improvements <whatsnew_0140.performance>`
 
 - :ref:`Prior Deprecations <whatsnew_0140.prior_deprecations>`
@@ -95,57 +97,6 @@ API changes
 
 - Add ``is_month_start``, ``is_month_end``, ``is_quarter_start``, ``is_quarter_end``, ``is_year_start``, ``is_year_end`` accessors for ``DateTimeIndex`` / ``Timestamp`` which return a boolean array of whether the timestamp(s) are at the start/end of the month/quarter/year defined by the frequency of the ``DateTimeIndex`` / ``Timestamp`` (:issue:`4565`, :issue:`6998`)
 
-- More consistent behaviour for some groupby methods:
-
-  groupby ``head`` and ``tail`` now act more like ``filter`` rather than an aggregation:
-
-  .. ipython:: python
-
-     df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
-     g = df.groupby('A')
-     g.head(1)  # filters DataFrame
-
-     g.apply(lambda x: x.head(1))  # used to simply fall-through
-
-  groupby head and tail respect column selection:
-
-  .. ipython:: python
-
-     g[['B']].head(1)
-
-  groupby ``nth`` now filters by default, with optional dropna argument to ignore
-  NaN (to replicate the previous behaviour.), See :ref:`the docs <groupby.nth>`.
-
-  .. ipython:: python
-
-     df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
-     g = df.groupby('A')
-     g.nth(0)  # can also use negative ints
-
-     g.nth(0, dropna='any')  # similar to old behaviour
-
-  groupby will now not return the grouped column for non-cython functions (:issue:`5610`, :issue:`5614`, :issue:`6732`),
-  as its already the index
-
-  .. ipython:: python
-
-     df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
-     g = df.groupby('A')
-     g.count()
-     g.describe()
-
-  passing ``as_index`` will leave the grouped column in-place (this is not change in 0.14.0)
-
-  .. ipython:: python
-
-     df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
-     g = df.groupby('A',as_index=False)
-     g.count()
-     g.describe()
-
-- Allow specification of a more complex groupby via ``pd.Grouper``, such as grouping
-  by a Time and a string field simultaneously. See :ref:`the docs <groupby.specify>`. (:issue:`3794`)
-
 - Local variable usage has changed in
   :func:`pandas.eval`/:meth:`DataFrame.eval`/:meth:`DataFrame.query`
   (:issue:`5987`). For the :class:`~pandas.DataFrame` methods, two things have
@@ -247,6 +198,62 @@ API changes
   from 0.13.1
 - Added ``factorize`` functions to ``Index`` and ``Series`` to get indexer and unique values (:issue:`7090`)
 
+.. _whatsnew_0140.groupby:
+
+Groupby API Changes
+~~~~~~~~~~~~~~~~~~~
+
+More consistent behaviour for some groupby methods:
+
+- groupby ``head`` and ``tail`` now act more like ``filter`` rather than an aggregation:
+
+  .. ipython:: python
+
+     df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
+     g = df.groupby('A')
+     g.head(1)  # filters DataFrame
+
+     g.apply(lambda x: x.head(1))  # used to simply fall-through
+
+- groupby head and tail respect column selection:
+
+  .. ipython:: python
+
+     g[['B']].head(1)
+
+- groupby ``nth`` now filters by default, with optional dropna argument to ignore
+  NaN (to replicate the previous behaviour.), See :ref:`the docs <groupby.nth>`.
+
+  .. ipython:: python
+
+     df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
+     g = df.groupby('A')
+     g.nth(0)  # can also use negative ints
+
+     g.nth(0, dropna='any')  # similar to old behaviour
+
+- groupby will now not return the grouped column for non-cython functions (:issue:`5610`, :issue:`5614`, :issue:`6732`),
+  as its already the index
+
+  .. ipython:: python
+
+     df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
+     g = df.groupby('A')
+     g.count()
+     g.describe()
+
+- passing ``as_index`` will leave the grouped column in-place (this is not change in 0.14.0)
+
+  .. ipython:: python
+
+     df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
+     g = df.groupby('A',as_index=False)
+     g.count()
+     g.describe()
+
+- Allow specification of a more complex groupby via ``pd.Grouper``, such as grouping
+  by a Time and a string field simultaneously. See :ref:`the docs <groupby.specify>`. (:issue:`3794`)
+
 .. _whatsnew_0140.sql:
 
 SQL