Fix SS06 formatting error pandas-dev#29254

Author: Deepan Das

pandas/core/frame.py

@@ -310,10 +310,11 @@
 
 class DataFrame(NDFrame):
     """
-    Two-dimensional size-mutable, potentially heterogeneous tabular data
-    structure with labeled axes (rows and columns). Arithmetic operations
-    align on both row and column labels. Can be thought of as a dict-like
-    container for Series objects. The primary pandas data structure.
+    Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). 
+    
+    Arithmetic operations align on both row and column labels. 
+    Can be thought of as a dict-like container for Series objects.
+    The primary pandas data structure.
 
     Parameters
     ----------
@@ -4798,9 +4799,10 @@ def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):
 
     def drop_duplicates(self, subset=None, keep="first", inplace=False):
         """
-        Return DataFrame with duplicate rows removed, optionally only
-        considering certain columns. Indexes, including time indexes
-        are ignored.
+        Return DataFrame with duplicate rows removed.
+        
+        Optionally only considers certain columns. 
+        Indexes, including time indexes are ignored.
 
         Parameters
         ----------
@@ -4834,8 +4836,9 @@ def drop_duplicates(self, subset=None, keep="first", inplace=False):
 
     def duplicated(self, subset=None, keep="first"):
         """
-        Return boolean Series denoting duplicate rows, optionally only
-        considering certain columns.
+        Return boolean Series denoting duplicate rows.
+        
+        Optionally only considers certain columns.
 
         Parameters
         ----------
@@ -7536,9 +7539,10 @@ def cov(self, min_periods=None):
 
     def corrwith(self, other, axis=0, drop=False, method="pearson"):
         """
-        Compute pairwise correlation between rows or columns of DataFrame
-        with rows or columns of Series or DataFrame.  DataFrames are first
-        aligned along both axes before computing the correlations.
+        Compute pairwise correlation between rows or columns of DataFrame.
+        
+        Correlation is computed with rows or columns of Series or DataFrame. 
+        DataFrames are first aligned along both axes before computing the correlations.
 
         Parameters
         ----------

pandas/core/generic.py

@@ -9965,9 +9965,10 @@ def abs(self):
 
     def describe(self, percentiles=None, include=None, exclude=None):
         """
-        Generate descriptive statistics that summarize the central tendency,
-        dispersion and shape of a dataset's distribution, excluding
-        ``NaN`` values.
+        Generate descriptive statistics that summarizes a dataset.
+        
+        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
@@ -10649,7 +10650,7 @@ def compound(self, axis=None, skipna=None, level=None):
             name,
             name2,
             axis_descr,
-            "Return unbiased skew over requested axis\nNormalized by N-1.",
+            "Return unbiased skew over requested axis normalized by N-1.",
             nanops.nanskew,
         )
         cls.kurt = _make_stat_function(
@@ -10659,7 +10660,7 @@ def compound(self, axis=None, skipna=None, level=None):
             name2,
             axis_descr,
             "Return unbiased kurtosis over requested axis using Fisher's "
-            "definition of\nkurtosis (kurtosis of normal == 0.0). Normalized "
+            "definition of kurtosis(kurtosis of normal == 0.0).\nNormalized "
             "by N-1.",
             nanops.nankurt,
         )