DOC: Fixed other PR02 docstring errors in pandas rolling and ewm files

Fixed methods mean, std, var, and corr in ewm.py. Fixed methods sum, apply, max, std, var, and quantile in rolling.py
pandas-dev · WillAyd · Nov 8, 2019 · Oct 1, 2019 · Oct 1, 2019 · Oct 2, 2019
commit 7fc0e356ecfb4a3f2553253b1e3a0e7d169b2561
diff --git a/pandas/core/window/ewm.py b/pandas/core/window/ewm.py
@@ -17,27 +17,10 @@
         ----------
         bias : bool, default False
             Use a standard estimation bias correction.
-        *args, **kwargs
-            Arguments and keyword arguments to be passed into func.
-"""
-
-_pairwise_template = """
-        Parameters
-        ----------
-        other : Series, DataFrame, or ndarray, optional
-            If not supplied then will default to self and produce pairwise
-            output.
-        pairwise : bool, default None
-            If False then only matching columns between self and other will be
-            used and the output will be a DataFrame.
-            If True then all pairwise combinations will be calculated and the
-            output will be a MultiIndex DataFrame in the case of DataFrame
-            inputs. In the case of missing elements, only complete pairwise
-            observations will be used.
-        bias : bool, default False
-           Use a standard estimation bias correction.
+        *args
+            Arguments to be passed into func.
         **kwargs
-           Keyword arguments to be passed into func.
+            Keyword arguments to be passed into func.
 """
 
 
@@ -275,8 +258,10 @@ def mean(self, *args, **kwargs):
 
         Parameters
         ----------
-        *args, **kwargs
-            Arguments and keyword arguments to be passed into func.
+        *args
+            Arguments to be passed into func.
+        **kwargs
+            Keyword arguments to be passed into func.
         """
         nv.validate_window_func("mean", args, kwargs)
         return self._apply("ewma", **kwargs)
@@ -317,10 +302,26 @@ def f(arg):
 
     @Substitution(name="ewm")
     @Appender(_doc_template)
-    @Appender(_pairwise_template)
     def cov(self, other=None, pairwise=None, bias=False, **kwargs):
         """
         Exponential weighted sample covariance.
+
+        Parameters
+        ----------
+        other : Series, DataFrame, or ndarray, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndex DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        bias : bool, default False
+            Use a standard estimation bias correction
+        **kwargs
+           Keyword arguments to be passed into func.
         """
         if other is None:
             other = self._selected_obj
@@ -348,11 +349,27 @@ def _get_cov(X, Y):
 
     @Substitution(name="ewm")
     @Appender(_doc_template)
-    @Appender(_pairwise_template)
     def corr(self, other=None, pairwise=None, **kwargs):
         """
         Exponential weighted sample correlation.
+
+
+        Parameters
+        ----------
+        other : Series, DataFrame, or ndarray, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndex DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        **kwargs
+           Keyword arguments to be passed into func.
         """
+
         if other is None:
             other = self._selected_obj
             # only default unset

diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
@@ -495,7 +495,10 @@ def aggregate(self, func, *args, **kwargs):
 
     Parameters
     ----------
-    *args, **kwargs
+    *args
+        For compatibility with other %(name)s methods. Has no effect
+        on the computed value.
+    **kwargs
         For compatibility with other %(name)s methods. Has no effect
         on the computed value.
 
@@ -1018,7 +1021,9 @@ def count(self):
         not passed. In the future `raw` will default to False.
    
         .. versionadded:: 0.23.0
-    *args, **kwargs
+    args
+        Arguments and keyword arguments to be passed into func.
+    kwargs
         Arguments and keyword arguments to be passed into func.
 
     Returns
@@ -1084,7 +1089,9 @@ def sum(self, *args, **kwargs):
 
     Parameters
     ----------
-    *args, **kwargs
+    *args
+        Arguments and keyword arguments to be passed into func.
+    **kwargs
         Arguments and keyword arguments to be passed into func.
     """
     )
@@ -1190,7 +1197,9 @@ def median(self, **kwargs):
     ddof : int, default 1
         Delta Degrees of Freedom.  The divisor used in calculations
         is ``N - ddof``, where ``N`` represents the number of elements.
-    *args, **kwargs
+    *args
+        For NumPy compatibility. No additional arguments are used.
+    **kwargs
         For NumPy compatibility. No additional arguments are used.
 
     Returns
@@ -1265,7 +1274,9 @@ def f(arg, *args, **kwargs):
     ddof : int, default 1
         Delta Degrees of Freedom.  The divisor used in calculations
         is ``N - ddof``, where ``N`` represents the number of elements.
-    *args, **kwargs
+    *args
+        For NumPy compatibility. No additional arguments are used.
+    **kwargs
         For NumPy compatibility. No additional arguments are used.
 
     Returns
@@ -1392,7 +1403,7 @@ def kurt(self, **kwargs):
             * higher: `j`.
             * nearest: `i` or `j` whichever is nearest.
             * midpoint: (`i` + `j`) / 2.
-    **kwargs:
+    **kwargs
         For compatibility with other %(name)s methods. Has no effect on
         the result.