diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index bb1b2ab8accbb..8fefa47c16bab 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -93,8 +93,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.Series.item \
         pandas.Series.pipe \
         pandas.Series.mode \
-        pandas.Series.sem \
-        pandas.Series.skew \
         pandas.Series.is_unique \
         pandas.Series.is_monotonic_increasing \
         pandas.Series.is_monotonic_decreasing \
@@ -542,8 +540,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.DataFrame.keys \
         pandas.DataFrame.iterrows \
         pandas.DataFrame.pipe \
-        pandas.DataFrame.sem \
-        pandas.DataFrame.skew \
         pandas.DataFrame.backfill \
         pandas.DataFrame.pad \
         pandas.DataFrame.swapaxes \
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 821e41db6b065..003e4cc5b8b23 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -11382,7 +11382,41 @@ def all(
             name2=name2,
             axis_descr=axis_descr,
             notes="",
-            examples="",
+            examples="""
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.sem().round(6)
+            0.57735
+
+            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.sem()
+            a   0.5
+            b   0.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.sem(axis=1)
+            tiger   0.5
+            zebra   0.5
+            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.sem(numeric_only=True)
+            a   0.5
+            dtype: float64""",
         )
         def sem(
             self,
@@ -11615,7 +11649,45 @@ def mean(
             axis_descr=axis_descr,
             min_count="",
             see_also="",
-            examples="",
+            examples="""
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.skew()
+            0.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
+            ...                  index=['tiger', 'zebra', 'cow'])
+            >>> df
+                    a   b   c
+            tiger   1   2   1
+            zebra   2   3   3
+            cow     3   4   5
+            >>> df.skew()
+            a   0.0
+            b   0.0
+            c   0.0
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.skew(axis=1)
+            tiger   1.732051
+            zebra  -1.732051
+            cow     0.000000
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True` to avoid
+            getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
+            ...                  index=['tiger', 'zebra', 'cow'])
+            >>> df.skew(numeric_only=True)
+            a   0.0
+            dtype: float64""",
         )
         def skew(
             self,