diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index f2606ce1b0d61..40efc4c65476a 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.
+
+    Data structure also contains 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
     ----------
@@ -4798,8 +4800,9 @@ 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
+        Return DataFrame with duplicate rows removed.
+
+        Considering certain columns is optional. 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.
+
+        Considering certain columns is optional.
 
         Parameters
         ----------
@@ -7536,9 +7540,12 @@ 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.
+
+        Pairwise correlation is computed 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.
 
         Parameters
         ----------
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index e15332a5277b9..f88c26c7bc782 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.
+
+    Produced %(klass)s will have same axis length as self.
 
     Parameters
     ----------
@@ -8741,8 +8742,9 @@ 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.
+
+        Join method is specified for each axis Index.
 
         Parameters
         ----------
@@ -9965,9 +9967,11 @@ 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.
+
+        Descriptive statistics include those that summarize 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 +10653,7 @@ 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.\n\nNormalized by N-1.",
             nanops.nanskew,
         )
         cls.kurt = _make_stat_function(
@@ -10658,8 +10662,9 @@ 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 "
+            "Return unbiased kurtosis over requested axis.\n\n"
+            "Kurtosis obtained using Fisher's definition of\n"
+            "kurtosis (kurtosis of normal == 0.0). Normalized "
             "by N-1.",
             nanops.nankurt,
         )