Update Rolling window operations documentation

* Add a longer description and examples for the `center` and
  `min_period` parameters to `DataArray.rolling()`

Author: kmsquire

doc/computation.rst

@@ -143,20 +143,34 @@ name of the dimension as a key (e.g. ``y``) and the window size as the value
 
     arr.rolling(y=3)
 
-The label position and minimum number of periods in the rolling window are
-controlled by the ``center`` and ``min_periods`` arguments:
+Aggregation and summary methods can be applied directly to the ``Rolling``
+object:
 
 .. ipython:: python
 
-    arr.rolling(y=3, min_periods=2, center=True)
+    r = arr.rolling(y=3)
+    r.reduce(np.std)
+    r.mean()
 
-Aggregation and summary methods can be applied directly to the ``Rolling`` object:
+Aggregation results are at the end of each window by default, but can be
+centered by passing ``center=True`` when constructing the ``Rolling`` object:
 
 .. ipython:: python
 
-    r = arr.rolling(y=3)
+    r = arr.rolling(y=3, center=True)
+    r.mean()
+
+As can be seen above, aggregations of windows which overlap the border of the
+array produce ``nan``s.  Setting ``min_periods`` in the call to ``rolling``
+changes the minimum number of observations within the window required to have
+a value when aggregating:
+
+.. ipython:: python
+
+    r = arr.rolling(y=3, min_periods=2)
+    r.mean()
+    r = arr.rolling(y=3, center=True, min_periods=2)
     r.mean()
-    r.reduce(np.std)
 
 Note that rolling window aggregations are faster when bottleneck_ is installed.