diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 759e07018a190..c2ec22020bca2 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -92,8 +92,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.Series.keys \
         pandas.Series.item \
         pandas.Series.pipe \
-        pandas.Series.mean \
-        pandas.Series.median \
         pandas.Series.mode \
         pandas.Series.sem \
         pandas.Series.skew \
@@ -543,8 +541,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.DataFrame.keys \
         pandas.DataFrame.iterrows \
         pandas.DataFrame.pipe \
-        pandas.DataFrame.mean \
-        pandas.DataFrame.median \
         pandas.DataFrame.sem \
         pandas.DataFrame.skew \
         pandas.DataFrame.backfill \
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 6c47418545307..9bdfb8da0c7cb 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -11516,7 +11516,41 @@ def prod(
             axis_descr=axis_descr,
             min_count="",
             see_also="",
-            examples="",
+            examples="""
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.mean()
+            2.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.mean()
+            a   1.5
+            b   2.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.mean(axis=1)
+            tiger   1.5
+            zebra   2.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True` to avoid
+            getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.mean(numeric_only=True)
+            a   1.5
+            dtype: float64""",
         )
         def mean(
             self,
@@ -11624,7 +11658,41 @@ def kurt(
             axis_descr=axis_descr,
             min_count="",
             see_also="",
-            examples="",
+            examples="""
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.median()
+            2.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.median()
+            a   1.5
+            b   2.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.median(axis=1)
+            tiger   1.5
+            zebra   2.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True`
+            to avoid getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.median(numeric_only=True)
+            a   1.5
+            dtype: float64""",
         )
         def median(
             self,