Various docstring fixes

pandas/core/frame.py

@@ -2610,12 +2610,9 @@ def transpose(self, *args, **kwargs):
 
         Parameters
         ----------
-        copy : bool, default False
-            If True, the underlying data is copied. Otherwise (default), no
-            copy is made if possible.
         *args, **kwargs
-            Additional keywords have no effect but might be accepted for
-            compatibility with numpy.
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with numpy.
 
         Returns
         -------
@@ -3237,7 +3234,7 @@ def eval(self, expr, inplace=False, **kwargs):
             If the expression contains an assignment, whether to perform the
             operation inplace and mutate the existing DataFrame. Otherwise,
             a new DataFrame is returned.
-        kwargs : dict
+        **kwargs
             See the documentation for :func:`eval` for complete details
             on the keyword arguments accepted by
             :meth:`~pandas.DataFrame.query`.
@@ -5205,8 +5202,10 @@ def swaplevel(self, i=-2, j=-1, axis=0):
 
         Parameters
         ----------
-        i, j : int, str (can be mixed)
-            Level of index to be swapped. Can pass level name as string.
+        i : int or str
+            Level of first index to be swapped. Can pass level name as string.
+        j : int or str
+            Level of second index to be swapped. Can pass level name as string.
 
         Returns
         -------
@@ -6295,7 +6294,6 @@ def unstack(self, level=-1, fill_value=None):
     %(versionadded)s
     Parameters
     ----------
-    frame : DataFrame
     id_vars : tuple, list, or ndarray, optional
         Column(s) to use as identifier variables.
     value_vars : tuple, list, or ndarray, optional

pandas/core/series.py

@@ -2076,15 +2076,15 @@ def idxmin(self, axis=0, skipna=True, *args, **kwargs):
 
         Parameters
         ----------
-        skipna : bool, default True
-            Exclude NA/null values. If the entire Series is NA, the result
-            will be NA.
         axis : int, default 0
             For compatibility with DataFrame.idxmin. Redundant for application
             on Series.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
         *args, **kwargs
-            Additional keywords have no effect but might be accepted
-            for compatibility with NumPy.
+            Additional arguments and keywords have no effect but might be
+            accepted for compatability with NumPy.
 
         Returns
         -------
@@ -2146,15 +2146,15 @@ def idxmax(self, axis=0, skipna=True, *args, **kwargs):
 
         Parameters
         ----------
-        skipna : bool, default True
-            Exclude NA/null values. If the entire Series is NA, the result
-            will be NA.
         axis : int, default 0
             For compatibility with DataFrame.idxmax. Redundant for application
             on Series.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
         *args, **kwargs
-            Additional keywords have no effect but might be accepted
-            for compatibility with NumPy.
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
 
         Returns
         -------
@@ -3525,8 +3525,10 @@ def swaplevel(self, i=-2, j=-1, copy=True):
 
         Parameters
         ----------
-        i, j : int, str (can be mixed)
-            Level of index to be swapped. Can pass level name as string.
+        i : int, str
+            Level of first index to be swapped. Can pass level name as string.
+        j : int, str
+            Level of second index to be swapped. Can pass level name as string.
         copy : bool, default True
             Whether to copy underlying data.
 
@@ -4080,14 +4082,10 @@ def rename(self, index=None, **kwargs):
             the index.
             Scalar or hashable sequence-like will alter the ``Series.name``
             attribute.
-        copy : bool, default True
-            Whether to copy underlying data.
-        inplace : bool, default False
-            Whether to return a new Series. If True then value of copy is
-            ignored.
-        level : int or level name, default None
-            In case of a MultiIndex, only rename labels in the specified
-            level.
+
+        **kwargs
+            Additional keyword arguments passed to the function. Only the
+            "inplace" keyword is used.
 
         Returns
         -------
@@ -4165,6 +4163,10 @@ def drop(
         columns : single label or list-like
             No change is made to the Series; use 'index' or 'labels' instead.
 
+            .. versionadded:: 0.21.0
+        columns : None
+            Redundant for application on Series.
+
             .. versionadded:: 0.21.0
         level : int or level name, optional
             For MultiIndex, level for which the labels will be removed.

pandas/core/window/ewm.py

@@ -21,25 +21,6 @@
             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.
-        **kwargs
-           Keyword arguments to be passed into func.
-"""
-
 
 class EWM(_Rolling):
     r"""
@@ -275,8 +256,10 @@ 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 +300,26 @@ 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
@@ -348,10 +347,24 @@ 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

pandas/core/window/rolling.py

@@ -1392,7 +1392,7 @@ 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.
 

pandas/plotting/_misc.py

@@ -168,7 +168,7 @@ def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
     colormap : str or :class:`matplotlib.colors.Colormap`, default None
         Colormap to select colors from. If string, load colormap with that
         name from matplotlib.
-    kwds : optional
+    **kwds : optional
         Options to pass to matplotlib scatter plotting method.
 
     Returns
@@ -283,7 +283,7 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
         greater or equal than the length of the `series`.
     samples : int, default 500
         Number of times the bootstrap procedure is performed.
-    **kwds :
+    **kwds
         Options to pass to matplotlib plotting method.
 
     Returns
@@ -398,7 +398,8 @@ def lag_plot(series, lag=1, ax=None, **kwds):
     series : Time series
     lag : lag of the scatter plot, default 1
     ax : Matplotlib axis object, optional
-    kwds : Matplotlib scatter method keyword arguments, optional
+    **kwds
+        Matplotlib scatter method keyword arguments, optional
 
     Returns
     -------