cleanup based on comments

doc/source/computation.rst

@@ -243,6 +243,7 @@ accept the following arguments:
 - ``window``: size of moving window
 - ``min_periods``: threshold of non-null data points to require (otherwise
   result is NA)
+- ``center``: boolean, whether to set the labels at the center (default is False)
 
 .. warning::
 
@@ -334,7 +335,7 @@ The following methods are available:
     :meth:`~Window.sum`, Sum of values
     :meth:`~Window.mean`, Mean of values
 
-The weights used in the window are specified by the ``win_type``keyword. The list of recognized types are:
+The weights used in the window are specified by the ``win_type`` keyword. The list of recognized types are:
 
 - ``boxcar``
 - ``triang``
@@ -370,27 +371,12 @@ For some windowing functions, additional parameters must be specified:
 
    ser.rolling(window=5, win_type='gaussian').mean(std=0.1)
 
-Centering Windows
-~~~~~~~~~~~~~~~~~
-
-By default the labels are set to the right edge of the window, but a
-``center`` keyword is available so the labels can be set at the center.
-This keyword is available in other rolling functions as well.
-
-.. ipython:: python
-
-   ser.rolling(window=5, win_type='boxcar').mean()
-
-   ser.rolling(window=5, win_type='boxcar', center=True).mean()
-
-   ser.rolling(window=5, center=True).mean()
-
 .. _stats.moments.normalization:
 
 .. note::
 
     For ``.sum()`` with a ``win_type``, there is no normalization done to the
-    weights. Passing custom weights of ``[1, 1, 1]`` will yield a different
+    weights for the window. Passing custom weights of ``[1, 1, 1]`` will yield a different
     result than passing weights of ``[2, 2, 2]``, for example. When passing a
     ``win_type`` instead of explicitly specifying the weights, the weights are
     already normalized so that the largest weight is 1.
@@ -399,6 +385,18 @@ This keyword is available in other rolling functions as well.
     such that the weights are normalized with respect to each other. Weights
     of ``[1, 1, 1]`` and ``[2, 2, 2]`` yield the same result.
 
+Centering Windows
+~~~~~~~~~~~~~~~~~
+
+By default the labels are set to the right edge of the window, but a
+``center`` keyword is available so the labels can be set at the center.
+This keyword is available in other rolling functions as well.
+
+.. ipython:: python
+
+   ser.rolling(window=5).mean()
+   ser.rolling(window=5, center=True).mean()
+
 .. _stats.moments.binary:
 
 Binary Window Functions
@@ -550,7 +548,7 @@ Furthermore you can pass a nested dict to indicate different aggregations on dif
 
 .. ipython:: python
 
-   r.agg({'A' : {'ra' : 'sum'}, 'B' : {'rb' : 'std' }})
+   r.agg({'A' : ['sum','std'], 'B' : ['mean','std'] })
 
 
 .. _stats.moments.expanding:
@@ -607,6 +605,7 @@ all accept are:
 - ``min_periods``: threshold of non-null data points to require. Defaults to
   minimum needed to compute statistic. No ``NaNs`` will be output once
   ``min_periods`` non-null data points have been seen.
+- ``center``: boolean, whether to set the labels at the center (default is False)
 
 .. note::
 

doc/source/whatsnew/v0.14.0.txt

@@ -170,11 +170,18 @@ API changes
   :ref:`Computing rolling pairwise covariances and correlations
   <stats.moments.corr_pairwise>` in the docs.
 
-  .. ipython:: python
+  .. code-block:: python
+
+     In [1]: df = DataFrame(np.random.randn(10,4),columns=list('ABCD'))
+
+     In [4]: covs = pd.rolling_cov(df[['A','B','C']], df[['B','C','D']], 5, pairwise=True)
 
-    df = DataFrame(np.random.randn(10,4),columns=list('ABCD'))
-    covs = rolling_cov(df[['A','B','C']], df[['B','C','D']], 5, pairwise=True)
-    covs[df.index[-1]]
+     In [5]: covs[df.index[-1]]
+     Out[5]:
+               B         C         D
+     A  0.035310  0.326593 -0.505430
+     B  0.137748 -0.006888 -0.005383
+     C -0.006888  0.861040  0.020762
 
 - ``Series.iteritems()`` is now lazy (returns an iterator rather than a list). This was the documented behavior prior to 0.14. (:issue:`6760`)
 

doc/source/whatsnew/v0.15.0.txt

@@ -68,7 +68,7 @@ For full docs, see the :ref:`categorical introduction <categorical>` and the
 
 .. ipython:: python
     :okwarning:
-    
+
     df = DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
 
     df["grade"] = df["raw_grade"].astype("category")
@@ -353,9 +353,15 @@ Rolling/Expanding Moments improvements
 
   New behavior
 
-  .. ipython:: python
+  .. code-block:: python
 
-     rolling_min(s, window=10, min_periods=5)
+     In [4]: pd.rolling_min(s, window=10, min_periods=5)
+     Out[4]:
+     0   NaN
+     1   NaN
+     2   NaN
+     3   NaN
+     dtype: float64
 
 - :func:`rolling_max`, :func:`rolling_min`, :func:`rolling_sum`, :func:`rolling_mean`, :func:`rolling_median`,
   :func:`rolling_std`, :func:`rolling_var`, :func:`rolling_skew`, :func:`rolling_kurt`, :func:`rolling_quantile`,
@@ -381,9 +387,15 @@ Rolling/Expanding Moments improvements
 
   New behavior (note final value is ``5 = sum([2, 3, NaN])``):
 
-  .. ipython:: python
+  .. code-block:: python
 
-    rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
+     In [7]: rolling_sum(Series(range(4)), window=3, min_periods=0, center=True)
+     Out[7]:
+     0    1
+     1    3
+     2    6
+     3    5
+     dtype: float64
 
 - :func:`rolling_window` now normalizes the weights properly in rolling mean mode (`mean=True`) so that
   the calculated weighted means (e.g. 'triang', 'gaussian') are distributed about the same means as those
@@ -397,20 +409,27 @@ Rolling/Expanding Moments improvements
 
   .. code-block:: python
 
-    In [39]: rolling_window(s, window=3, win_type='triang', center=True)
-    Out[39]:
-    0         NaN
-    1    6.583333
-    2    6.883333
-    3    6.683333
-    4         NaN
-    dtype: float64
+     In [39]: rolling_window(s, window=3, win_type='triang', center=True)
+     Out[39]:
+     0         NaN
+     1    6.583333
+     2    6.883333
+     3    6.683333
+     4         NaN
+     dtype: float64
 
   New behavior
 
   .. ipython:: python
 
-    rolling_window(s, window=3, win_type='triang', center=True)
+     In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True)
+     Out[10]:
+     0       NaN
+     1     9.875
+     2    10.325
+     3    10.025
+     4       NaN
+     dtype: float64
 
 - Removed ``center`` argument from all :func:`expanding_ <expanding_apply>` functions (see :ref:`list <api.functions_expanding>`),
   as the results produced when ``center=True`` did not make much sense. (:issue:`7925`)
@@ -449,9 +468,17 @@ Rolling/Expanding Moments improvements
 
   New behavior (note values start at index ``4``, the location of the 2nd (since ``min_periods=2``) non-empty value):
 
-  .. ipython:: python
+  .. code-block:: python
 
-    ewma(s, com=3., min_periods=2)
+     In [2]: pd.ewma(s, com=3., min_periods=2)
+     Out[2]:
+     0         NaN
+     1         NaN
+     2         NaN
+     3         NaN
+     4    1.759644
+     5    2.383784
+     dtype: float64
 
 - :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, :func:`ewmcov`, and :func:`ewmcorr`
   now have an optional ``adjust`` argument, just like :func:`ewma` does,
@@ -465,11 +492,28 @@ Rolling/Expanding Moments improvements
   When ``ignore_na=True`` (which reproduces the pre-0.15.0 behavior), missing values are ignored in the weights calculation.
   (:issue:`7543`)
 
-  .. ipython:: python
+  .. code-block:: python
+
+     In [7]: pd.ewma(Series([None, 1., 8.]), com=2.)
+     Out[7]:
+     0    NaN
+     1    1.0
+     2    5.2
+     dtype: float64
+
+     In [8]: pd.ewma(Series([1., None, 8.]), com=2., ignore_na=True)  # pre-0.15.0 behavior
+     Out[8]:
+     0    1.0
+     1    1.0
+     2    5.2
+     dtype: float64
 
-     ewma(Series([None, 1., 8.]), com=2.)
-     ewma(Series([1., None, 8.]), com=2., ignore_na=True)  # pre-0.15.0 behavior
-     ewma(Series([1., None, 8.]), com=2., ignore_na=False)  # new default
+     In [9]: pd.ewma(Series([1., None, 8.]), com=2., ignore_na=False)  # new default
+     Out[9]:
+     0    1.000000
+     1    1.000000
+     2    5.846154
+     dtype: float64
 
   .. warning::
 
@@ -525,10 +569,23 @@ Rolling/Expanding Moments improvements
   By comparison, the following 0.15.0 results have a ``NaN`` for entry ``0``,
   and the debiasing factors are decreasing (towards 1.25):
 
-  .. ipython:: python
+  .. code-block:: python
 
-     ewmvar(s, com=2., bias=False)
-     ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)
+     In [14]: pd.ewmvar(s, com=2., bias=False)
+     Out[14]:
+     0         NaN
+     1    0.500000
+     2    1.210526
+     3    4.089069
+     dtype: float64
+
+     In [15]: pd.ewmvar(s, com=2., bias=False) / pd.ewmvar(s, com=2., bias=True)
+     Out[15]:
+     0         NaN
+     1    2.083333
+     2    1.583333
+     3    1.425439
+     dtype: float64
 
   See :ref:`Exponentially weighted moment functions <stats.moments.exponentially_weighted>` for details. (:issue:`7912`)
 

doc/source/whatsnew/v0.18.0.txt

@@ -86,8 +86,8 @@ And multiple aggregations
 
 .. ipython:: python
 
-   r.agg({'A' : {'ra' : ['mean','std']},
-          'B' : {'rb' : ['mean','std']}})
+   r.agg({'A' : ['mean','std'],
+          'B' : ['mean','std']})
 
 .. _whatsnew_0180.enhancements.other:
 
@@ -239,15 +239,17 @@ Deprecations
              2    0.5
              dtype: float64
 
-- The the ``freq`` and ``how`` arguments to the ``.rolling``, ``.expanding``, and ``.ewm`` (new) functions are deprecated, and will be removed in a future version. (:issue:`11603`)
+- The the ``freq`` and ``how`` arguments to the ``.rolling``, ``.expanding``, and ``.ewm`` (new) functions are deprecated, and will be removed in a future version. You can simply resample the input prior to creating a window function. (:issue:`11603`).
+
+  For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D',how='max').rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window.
 
 .. _whatsnew_0180.prior_deprecations:
 
 Removal of prior version deprecations/changes
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-- Removal of ``rolling_corr_parwise`` in favor of ``.rolling().corr(pairwise=True)`` (:issue:`4950`)
-- Removal of ``expanding_corr_parwise`` in favor of ``.expanding().corr(pairwise=True)`` (:issue:`4950`)
+- Removal of ``rolling_corr_pairwise`` in favor of ``.rolling().corr(pairwise=True)`` (:issue:`4950`)
+- Removal of ``expanding_corr_pairwise`` in favor of ``.expanding().corr(pairwise=True)`` (:issue:`4950`)
 
 
 

pandas/core/base.py

@@ -358,11 +358,10 @@ def _gotitem(self, key, ndim, subset=None):
 """
 
     _see_also_template = """
-
 See also
 --------
-`pandas.Series.%(name)s`
-`pandas.DataFrame.%(name)s`
+pandas.Series.%(name)s
+pandas.DataFrame.%(name)s
 """
 
     def aggregate(self, func, *args, **kwargs):
@@ -422,7 +421,7 @@ def _aggregate(self, arg, *args, **kwargs):
             else:
                 for col, agg_how in compat.iteritems(arg):
                     colg = self._gotitem(col, ndim=1)
-                    result[col] = colg.aggregate(agg_how, _level=(_level or 0) + 1)
+                    result[col] = colg.aggregate(agg_how, _level=None)
                     keys.append(col)
 
             if isinstance(list(result.values())[0], com.ABCDataFrame):
@@ -451,12 +450,16 @@ def _aggregate_multiple_funcs(self, arg, _level):
         if self.axis != 0:
             raise NotImplementedError("axis other than 0 is not supported")
 
-        obj = self._obj_with_exclusions
+        if self._selected_obj.ndim == 1:
+            obj = self._selected_obj
+        else:
+            obj = self._obj_with_exclusions
+
         results = []
         keys = []
 
         # degenerate case
-        if obj.ndim == 1:
+        if obj.ndim==1:
             for a in arg:
                 try:
                     colg = self._gotitem(obj.name, ndim=1, subset=obj)