Fix SS06 formatting error pandas-dev#29254 with Appender Functions

Author: Deepan Das

pandas/core/generic.py

@@ -5156,8 +5156,7 @@ def pipe(self, func, *args, **kwargs):
     _shared_docs[
         "transform"
     ] = """
-    Call ``func`` on self producing a %(klass)s with transformed values
-    and that has the same axis length as self.
+    Call ``func`` on self producing a %(klass)s with transformed values and that has the same axis length as self.
 
     Parameters
     ----------
@@ -8741,8 +8740,7 @@ def ranker(data):
     _shared_docs[
         "align"
     ] = """
-        Align two objects on their axes with the
-        specified join method for each axis Index.
+        Align two objects on their axes with the specified join method for each axis Index.
 
         Parameters
         ----------
@@ -9966,14 +9964,11 @@ def abs(self):
     def describe(self, percentiles=None, include=None, exclude=None):
         """
         Generate descriptive statistics that summarizes a dataset.
-        
-        Statistics summarizes the central tendency, dispersion and 
+        Statistics summarizes the central tendency, dispersion and
         shape of a dataset's distribution, excluding ``NaN`` values.
 
-        Analyzes both numeric and object series, as well
-        as ``DataFrame`` column sets of mixed data types. The output
-        will vary depending on what is provided. Refer to the notes
-        below for more detail.
+        Analyzes both numeric and object series, as well as ``DataFrame`` column sets of mixed data types.
+        The output will vary depending on what is provided. Refer to the notes below for more detail.
 
         Parameters
         ----------
@@ -10659,9 +10654,8 @@ def compound(self, axis=None, skipna=None, level=None):
             name,
             name2,
             axis_descr,
-            "Return unbiased kurtosis over requested axis using Fisher's "
-            "definition of kurtosis(kurtosis of normal == 0.0).\nNormalized "
-            "by N-1.",
+            "Return unbiased kurtosis over requested axis using Fisher's definition of kurtosis(kurtosis of normal == 0.0)."
+            "\n\nNormalized by N-1.",
             nanops.nankurt,
         )
         cls.kurtosis = cls.kurt