diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index f2606ce1b0d61..84b2d266426c0 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -310,10 +310,12 @@
 
 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.
+
+    Data Structure has 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.
+    This is the primary pandas data structure.
 
     Parameters
     ----------
@@ -4798,9 +4800,10 @@ 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 +4837,9 @@ 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 +7540,10 @@ 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
         ----------
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index e15332a5277b9..d87f4c03e7f36 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -5156,8 +5156,9 @@ 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.
+
+    %(klass)s has the same axis length as self.
 
     Parameters
     ----------
@@ -8741,8 +8742,7 @@ 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 for each axis Index.
 
         Parameters
         ----------
@@ -9965,14 +9965,14 @@ 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
-        will vary depending on what is provided. Refer to the notes
-        below for more detail.
+        Analyzes both numeric and object series.
+        In addition, it also analyses ``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
         ----------
@@ -10649,7 +10649,7 @@ 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(
@@ -10658,9 +10658,9 @@ def compound(self, axis=None, skipna=None, level=None):
             name,
             name2,
             axis_descr,
-            "Return unbiased kurtosis over requested axis using Fisher's "
-            "definition of\nkurtosis (kurtosis of normal == 0.0). Normalized "
-            "by N-1.",
+            "Return unbiased kurtosis over requested axis."
+            "\n\nFisher's definition of kurtosis is used(kurtosis of normal == 0.0)."
+            "\nNormalized by N-1.",
             nanops.nankurt,
         )
         cls.kurtosis = cls.kurt