diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index 1414d2dd3c8dc..d727424750be5 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -214,6 +214,11 @@ computing common *window* or *rolling* statistics. Among these are count, sum,
 mean, median, correlation, variance, covariance, standard deviation, skewness,
 and kurtosis.
 
+Starting in version 0.18.1, the ``rolling()`` and ``expanding()``
+functions can be used directly from DataFrameGroupBy objects,
+see the :ref:`groupby docs <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>`
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index c5a77770085d6..ff97775afc2e2 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -614,6 +614,54 @@ 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.
+
+The example below will apply the ``rolling()`` method on the samples of
+the column B based on the groups of column A.
+
+.. ipython:: python
+
+   df = pd.DataFrame({'A': [1] * 10 + [5] * 10,
+	              'B': np.arange(20)})
+   df
+   
+   df.groupby('A').rolling(4).B.mean()
+
+
+The ``expanding()`` method will accumulate a given operation
+(``sum()`` in the example) for all the members of each particular
+group.
+
+.. ipython:: python
+
+   df.groupby('A').expanding().sum()
+
+
+Suppose you want to use the ``resample()`` method to get a daily
+frequency in each group of your dataframe and wish to complete the
+missing values with the ``ffill()`` method.
+
+.. 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
+
+   df.groupby('group').resample('1D').ffill()
+
 .. _groupby.filter:
 
 Filtration
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
index 854de443ac5ee..9253124f7e8b2 100644
--- a/doc/source/timeseries.rst
+++ b/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
 
+Starting in version 0.18.1, the ``resample()`` function can be used directly from
+DataFrameGroupBy objects, see the :ref:`groupby docs <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>`