diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 84a4cbbc0a447..7a6f28a86dee0 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -1457,15 +1457,16 @@ def mean(self, skipna=True):
         Parameters
         ----------
         skipna : bool, default True
-            Whether to ignore any NaT elements
+            Whether to ignore any NaT elements.
 
         Returns
         -------
-        scalar (Timestamp or Timedelta)
+        scalar
+            Timestamp or Timedelta.
 
         See Also
         --------
-        numpy.ndarray.mean
+        numpy.ndarray.mean : Returns the average of array elements along a given axis.
         Series.mean : Return the mean value in a Series.
 
         Notes
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 4c15e4b26ed46..79d97a13e37ca 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1169,20 +1169,22 @@ def to_flat_index(self):
 
     def to_series(self, index=None, name=None):
         """
-        Create a Series with both index and values equal to the index keys
-        useful with map for returning an indexer based on an index.
+        Create a Series with both index and values equal to the index keys.
+
+        Useful with map for returning an indexer based on an index.
 
         Parameters
         ----------
         index : Index, optional
-            index of resulting Series. If None, defaults to original index
+            Index of resulting Series. If None, defaults to original index.
         name : str, optional
-            name of resulting Series. If None, defaults to name of original
-            index
+            Dame of resulting Series. If None, defaults to name of original
+            index.
 
         Returns
         -------
-        Series : dtype will be based on the type of the Index values.
+        Series
+            The dtype will be based on the type of the Index values.
         """
 
         from pandas import Series