Remove pd.rolling_*, pd.expanding_* and pd.ewm*

Author: tp

asv_bench/benchmarks/gil.py

@@ -287,7 +287,11 @@ def setup(self):
     def time_nogil_rolling_median(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_median(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).median()
         run(self.arr, self.win)
 
 
@@ -304,43 +308,71 @@ def setup(self):
     def time_nogil_rolling_mean(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_mean(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).mean()
         run(self.arr, self.win)
 
     def time_nogil_rolling_min(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_min(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).min()
         run(self.arr, self.win)
 
     def time_nogil_rolling_max(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_max(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).max()
         run(self.arr, self.win)
 
     def time_nogil_rolling_var(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_var(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).var()
         run(self.arr, self.win)
 
     def time_nogil_rolling_skew(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_skew(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).skew()
         run(self.arr, self.win)
 
     def time_nogil_rolling_kurt(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_kurt(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).kurt()
         run(self.arr, self.win)
 
     def time_nogil_rolling_std(self):
         @test_parallel(num_threads=2)
         def run(arr, win):
-            rolling_std(arr, win)
+            if len(arr.shape) == 1:
+                obj = pd.Series(arr)
+            else:
+                obj = pd.DataFrame(arr)
+            obj.rolling(win).std()
         run(self.arr, self.win)
 
 

doc/source/computation.rst

@@ -200,19 +200,12 @@ Window Functions
 
 .. currentmodule:: pandas.core.window
 
-.. warning::
-
-   Prior to version 0.18.0, ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` were module level
-   functions and are now deprecated. These are replaced by using the :class:`~pandas.core.window.Rolling`, :class:`~pandas.core.window.Expanding` and :class:`~pandas.core.window.EWM`. objects and a corresponding method call.
-
-   The deprecation warning will show the new syntax, see an example :ref:`here <whatsnew_0180.window_deprecations>`
-
 For working with data, a number of windows functions are provided for
 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()``
+The ``rolling()`` and ``expanding()``
 functions can be used directly from DataFrameGroupBy objects,
 see the :ref:`groupby docs <groupby.transform.window_resample>`.
 

doc/source/whatsnew/v0.22.0.txt

@@ -216,7 +216,9 @@ Removal of prior version deprecations/changes
 - The ``pandas.io.wb`` and ``pandas.io.data`` stub modules have been removed (:issue:`13735`)
 - ``Categorical.from_array`` has been removed (:issue:`13854`)
 - The ``freq`` and ``how`` parameters have been removed from the ``rolling``/``expanding``/``ewm`` methods of DataFrame
-  and Series (deprecated since v0.18). Instead, resample before calling the methods. (:issue:18601)
+  and Series (deprecated since v0.18). Instead, resample before calling the methods. (:issue:`18601`)
+- The top-level functions ``pd.rolling_*``, ``pd.expanding_*`` and ``pd.ewm*`` have been removed (deprecated since v0.18).
+  Instead use the corresponding method on Groupby objects. (:issue:`18723`)
 - ``DatetimeIndex.to_datetime``, ``Timestamp.to_datetime``, ``PeriodIndex.to_datetime``, and ``Index.to_datetime`` have been removed (:issue:`8254`, :issue:`14096`, :issue:`14113`)
 
 .. _whatsnew_0220.performance:

pandas/__init__.py

@@ -41,7 +41,6 @@
 
 from pandas.core.api import *
 from pandas.core.sparse.api import *
-from pandas.stats.api import *
 from pandas.tseries.api import *
 from pandas.core.computation.api import *
 from pandas.core.reshape.api import *