DEPR: deprecate pd.rolling_*, pd.expanding_*, pd.ewm*

Author: jreback

doc/source/api.rst

@@ -1505,9 +1505,9 @@ Window
 ------
 .. currentmodule:: pandas.core.window
 
-Rolling objects are returned by rolling calls: :func:`pandas.DataFrame.rolling`, :func:`pandas.Series.rolling`, etc.
-Expanding objects are returned by rolling calls: :func:`pandas.DataFrame.expanding`, :func:`pandas.Series.expanding`, etc.
-EWM objects are returned by rolling calls: :func:`pandas.DataFrame.ewm`, :func:`pandas.Series.ewm`, etc.
+Rolling objects are returned by ``.rolling`` calls: :func:`pandas.DataFrame.rolling`, :func:`pandas.Series.rolling`, etc.
+Expanding objects are returned by ``.expanding`` calls: :func:`pandas.DataFrame.expanding`, :func:`pandas.Series.expanding`, etc.
+EWM objects are returned by ``.ewm`` calls: :func:`pandas.DataFrame.ewm`, :func:`pandas.Series.ewm`, etc.
 
 Standard moving window functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1526,13 +1526,13 @@ Standard moving window functions
    Rolling.min
    Rolling.max
    Rolling.corr
-   Rolling.corr_pairwise
    Rolling.cov
    Rolling.skew
    Rolling.kurt
    Rolling.apply
    Rolling.quantile
-   Rolling.window
+   Window.mean
+   Window.sum
 
 .. _api.functions_expanding:
 
@@ -1553,7 +1553,6 @@ Standard expanding window functions
    Expanding.min
    Expanding.max
    Expanding.corr
-   Expanding.corr_pairwise
    Expanding.cov
    Expanding.skew
    Expanding.kurt

doc/source/computation.rst

@@ -203,8 +203,12 @@ Window Functions
 
 .. warning::
 
-   Prior to version 0.18.0, these were module level functions that have been deprecated.
-   You can see the previous documentation
+   Prior to version 0.18.0, ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` were module level
+   functions and are now deprecated and replaced by the corresponding method call.
+
+   The deprecation warning will show the new syntax, see an example :ref:`here <whatsnew_0180.window_deprecations>`
+
+   You can view the previous documentation
    `here <http://pandas.pydata.org/pandas-docs/version/0.17.1/computation.html#moving-rolling-statistics-moments>`__
 
 For working with data, a number of windows functions are provided for
@@ -242,10 +246,6 @@ accept the following arguments:
   result is NA)
 - ``freq``: optionally specify a :ref:`frequency string <timeseries.alias>`
   or :ref:`DateOffset <timeseries.offsets>` to pre-conform the data to.
-- ``how``: optionally specify method for down or re-sampling.  Default is
-  is ``min`` for :meth:`~Rolling.min`, ``max`` for :meth:`~Rolling.max`, ``median`` for
-  :meth:`~Rolling.median`, and ``mean`` for all other rolling functions.  See
-  :meth:`DataFrame.resample`'s how argument for more information.
 
 We can then call functions on these ``rolling`` objects. Which return like-indexed objects:
 
@@ -323,7 +323,7 @@ compute the mean absolute deviation on a rolling basis:
 Rolling Windows
 ~~~~~~~~~~~~~~~
 
-The :meth:`~Window.mean`, and :meth:`~Window.sum` functions performs a generic rolling window computation
+The :meth:`~Window.mean`, and :meth:`~Window.sum` functions perform a generic rolling window computation
 on the input data. The weights used in the window are specified by the ``win_type``
 keyword. The list of recognized types are:
 
@@ -361,6 +361,9 @@ 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.

doc/source/conf.py

@@ -224,16 +224,7 @@
     'pandas.io.pickle.read_pickle', 'pandas.io.pytables.HDFStore.append', 'pandas.io.pytables.HDFStore.get',
     'pandas.io.pytables.HDFStore.put', 'pandas.io.pytables.HDFStore.select', 'pandas.io.pytables.read_hdf',
     'pandas.io.sql.read_sql', 'pandas.io.sql.read_frame', 'pandas.io.sql.write_frame',
-    'pandas.io.stata.read_stata', 'pandas.stats.moments.ewma', 'pandas.stats.moments.ewmcorr',
-    'pandas.stats.moments.ewmcov', 'pandas.stats.moments.ewmstd', 'pandas.stats.moments.ewmvar',
-    'pandas.stats.moments.expanding_apply', 'pandas.stats.moments.expanding_corr', 'pandas.stats.moments.expanding_count',
-    'pandas.stats.moments.expanding_cov', 'pandas.stats.moments.expanding_kurt', 'pandas.stats.moments.expanding_mean',
-    'pandas.stats.moments.expanding_median', 'pandas.stats.moments.expanding_quantile', 'pandas.stats.moments.expanding_skew',
-    'pandas.stats.moments.expanding_std', 'pandas.stats.moments.expanding_sum', 'pandas.stats.moments.expanding_var',
-    'pandas.stats.moments.rolling_apply', 'pandas.stats.moments.rolling_corr', 'pandas.stats.moments.rolling_count',
-    'pandas.stats.moments.rolling_cov', 'pandas.stats.moments.rolling_kurt', 'pandas.stats.moments.rolling_mean',
-    'pandas.stats.moments.rolling_median', 'pandas.stats.moments.rolling_quantile', 'pandas.stats.moments.rolling_skew',
-    'pandas.stats.moments.rolling_std', 'pandas.stats.moments.rolling_sum', 'pandas.stats.moments.rolling_var']
+    'pandas.io.stata.read_stata']
 
 html_additional_pages = {'generated/' + page: 'api_redirect.html' for page in moved_api_pages}
 

doc/source/cookbook.rst

@@ -517,7 +517,7 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
    def Red(x):
       return functools.reduce(CumRet,x,1.0)
 
-   pd.expanding_apply(S, Red)
+   S.expanding().apply(Red)
 
 
 `Replacing some values with mean of the rest of a group
@@ -639,7 +639,7 @@ Create a list of dataframes, split using a delineation based on logic included i
    df = pd.DataFrame(data={'Case' : ['A','A','A','B','A','A','B','A','A'],
                            'Data' : np.random.randn(9)})
 
-   dfs = list(zip(*df.groupby(pd.rolling_median((1*(df['Case']=='B')).cumsum(),3,True))))[-1]
+   dfs = list(zip(*df.groupby((1*(df['Case']=='B')).cumsum().rolling(window=3,min_periods=1).median())))[-1]
 
    dfs[0]
    dfs[1]

doc/source/groupby.rst

@@ -519,7 +519,7 @@ to standardize the data within each group:
 
    index = pd.date_range('10/1/1999', periods=1100)
    ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
-   ts = pd.rolling_mean(ts, 100, 100).dropna()
+   ts = ts.rolling(window=100,min_periods=100).mean().dropna()
 
    ts.head()
    ts.tail()

doc/source/whatsnew/v0.18.0.txt

@@ -210,18 +210,40 @@ Other API Changes
 Deprecations
 ^^^^^^^^^^^^
 
+.. _whatsnew_0180.window_deprecations:
 
+- Function ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` are deprecated and replaced by the corresponding method call. Note that
+  the new suggested syntax includes all of the arguments (even if default) (:issue:`11603`)
 
+  .. code-block:: python
 
+     In [1]: s = Series(range(3))
 
+     In [2]: pd.rolling_mean(s,window=2,min_periods=1)
+             FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with
+                  Series.rolling(min_periods=1,window=2,center=False).mean()
+     Out[2]:
+             0    0.0
+             1    0.5
+             2    1.5
+             dtype: float64
+
+     In [3]: pd.rolling_cov(s, s, window=2)
+             FutureWarning: pd.rolling_cov is deprecated for Series and will be removed in a future version, replace with
+                  Series.rolling(window=2).cov(other=<Series>)
+     Out[3]:
+             0    NaN
+             1    0.5
+             2    0.5
+             dtype: float64
 
 .. _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_parwise`` in favor of ``.rolling().corr(pairwise=True)`` (:issue:`4950`)
+- Removal of ``expanding_corr_parwise`` in favor of ``.expanding().corr(pairwise=True)`` (:issue:`4950`)
 
 
 

pandas/core/generic.py

@@ -4741,26 +4741,25 @@ def _add_series_or_dataframe_operations(cls):
 
         @Appender(rwindow.rolling.__doc__)
         def rolling(self, window, min_periods=None, freq=None, center=False,
-                    how=None, win_type=None, axis=0):
+                    win_type=None, axis=0):
             axis = self._get_axis_number(axis)
             return rwindow.rolling(self, window=window, min_periods=min_periods, freq=freq, center=center,
-                                   how=how, win_type=win_type, axis=axis)
+                                   win_type=win_type, axis=axis)
         cls.rolling = rolling
 
         @Appender(rwindow.expanding.__doc__)
-        def expanding(self, min_periods=None, freq=None, center=False,
-                      how=None, axis=0):
+        def expanding(self, min_periods=1, freq=None, center=False, axis=0):
             axis = self._get_axis_number(axis)
             return rwindow.expanding(self, min_periods=min_periods, freq=freq, center=center,
-                                     how=how, axis=axis)
+                                     axis=axis)
         cls.expanding = expanding
 
         @Appender(rwindow.ewm.__doc__)
         def ewm(self, com=None, span=None, halflife=None, min_periods=0, freq=None,
-                adjust=True, how=None, ignore_na=False, axis=0):
+                adjust=True, ignore_na=False, axis=0):
             axis = self._get_axis_number(axis)
             return rwindow.ewm(self, com=com, span=span, halflife=halflife, min_periods=min_periods,
-                               freq=freq, adjust=adjust, how=how, ignore_na=ignore_na, axis=axis)
+                               freq=freq, adjust=adjust, ignore_na=ignore_na, axis=axis)
         cls.ewm = ewm
 
 def _doc_parms(cls):