DOC: add section on groupby().rolling/expanding/resample

doc/source/computation.rst

@@ -214,6 +214,10 @@ computing common *window* or *rolling* statistics. Among these are count, sum,
 mean, median, correlation, variance, covariance, standard deviation, skewness,
 and kurtosis.
 
+Now the ``rolling()`` and ``expanding()`` functions can be used directly from
+DataFrameGroupBy objects, see :ref:`whatsnew docs <whatsnew_0181.deferred_ops>` and :ref:`groupby transformation <groupby.transform.window_resample>`
+
+
 .. note::
 
    The API for window statistics is quite similar to the way one works with ``GroupBy`` objects, see the documentation :ref:`here <groupby>`

doc/source/groupby.rst

@@ -614,6 +614,73 @@ and that the transformed data contains no NAs.
 
       grouped.ffill()
 
+
+.. _groupby.transform.window_resample:
+
+New syntax to window and resample operations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. versionadded:: 0.18.1
+
+Working with the resample, expanding or rolling operations on the groupby
+level used to require the application of helper functions. However,
+now it is possible to use ``resample()``, ``expanding()`` and
+``rolling()`` as methods on groupbys. We will find below simple
+examples of the application of each of the refered methods:
+
+
+Example of the ``rolling()`` method applied to groupbys:
+
+.. ipython:: python
+
+   df = pd.DataFrame({'A': [1] * 20 + [2] * 12,
+	              'B': np.arange(32)})
+
+   df.groupby('A').rolling(4).B.mean()
+
+.. note::
+
+   The example above is grouping the values of the column A, creating
+   a rolling window of size 4 on the samples of columns B and applying
+   the average.
+
+
+Example of the ``resample()``:
+
+.. ipython:: python
+
+   df = pd.DataFrame({'date': pd.date_range(start='2016-01-01',
+	                                    periods=4,
+					    freq='W'),
+		      'group': [1, 1, 2, 2],
+		      'val': [5, 6, 7, 8]}).set_index('date')
+
+   df.groupby('group').resample('1D').ffill()
+
+.. note::
+
+   The example above is grouping the values of column ``group``,
+   applying a resample operation to a daily frequency and completing
+   the missing values with the ``ffill()`` method.
+
+
+
+Example of the ``expanding()``:
+
+.. ipython:: python
+
+   df = pd.DataFrame({'group': [1] * 5 + [2] * 5,
+	              'val': [5] * 5 + [1] * 5 })
+
+   df.groupby('group').expanding().sum()
+
+.. note::
+
+   The example above of the ``expanding`` method is grouping the
+   dataframe into the groups available at the columns groups. The
+   expanding operation will accumulate the defined operations (sum()
+   in this example) for all the members of the a particular group.
+
+
 .. _groupby.filter:
 
 Filtration

doc/source/timeseries.rst

@@ -1288,6 +1288,9 @@ limited to, financial applications.
 ``.resample()`` is a time-based groupby, followed by a reduction method on each of its groups.
 See some :ref:`cookbook examples <cookbook.resample>` for some advanced strategies
 
+Now the ``resample()`` function can be used directly from
+DataFrameGroupBy objects, see :ref:`whatsnew docs <whatsnew_0181.deferred_ops>` and :ref:`groupby transformation <groupby.transform.window_resample>`
+
 .. note::
 
    ``.resample()`` is similar to using a ``.rolling()`` operation with a time-based offset, see a discussion :ref:`here <stats.moments.ts-versus-resampling>`
@@ -1353,6 +1356,8 @@ retains the input representation.
 frequency periods.
 
 
+
+
 Up Sampling
 ~~~~~~~~~~~