diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 2d2f7bbf7092f..0eda80a78ee4f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -100,7 +100,6 @@
     is_list_like,
     is_named_tuple,
     is_object_dtype,
-    is_period_dtype,
     is_scalar,
     is_sequence,
     needs_i8_conversion,
@@ -8225,7 +8224,7 @@ def _reduce(
 
         dtype_is_dt = np.array(
             [
-                is_datetime64_any_dtype(values.dtype) or is_period_dtype(values.dtype)
+                is_datetime64_any_dtype(values.dtype)
                 for values in self._iter_column_arrays()
             ],
             dtype=bool,
@@ -8233,7 +8232,7 @@ def _reduce(
         if numeric_only is None and name in ["mean", "median"] and dtype_is_dt.any():
             warnings.warn(
                 "DataFrame.mean and DataFrame.median with numeric_only=None "
-                "will include datetime64, datetime64tz, and PeriodDtype columns in a "
+                "will include datetime64 and datetime64tz columns in a "
                 "future version.",
                 FutureWarning,
                 stacklevel=3,
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index b9ff0a5959c01..32b05872ded3f 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -7,7 +7,7 @@
 
 from pandas._config import get_option
 
-from pandas._libs import NaT, Period, Timedelta, Timestamp, iNaT, lib
+from pandas._libs import NaT, Timedelta, Timestamp, iNaT, lib
 from pandas._typing import ArrayLike, Dtype, Scalar
 from pandas.compat._optional import import_optional_dependency
 
@@ -353,14 +353,6 @@ def _wrap_results(result, dtype: Dtype, fill_value=None):
         else:
             result = result.astype("m8[ns]").view(dtype)
 
-    elif isinstance(dtype, PeriodDtype):
-        if is_float(result) and result.is_integer():
-            result = int(result)
-        if is_integer(result):
-            result = Period._from_ordinal(result, freq=dtype.freq)
-        else:
-            raise NotImplementedError(type(result), result)
-
     return result
 
 
@@ -516,6 +508,7 @@ def nansum(
     return _wrap_results(the_sum, dtype)
 
 
+@disallow(PeriodDtype)
 @bottleneck_switch()
 def nanmean(values, axis=None, skipna=True, mask=None):
     """
@@ -547,7 +540,12 @@ def nanmean(values, axis=None, skipna=True, mask=None):
     )
     dtype_sum = dtype_max
     dtype_count = np.float64
-    if is_integer_dtype(dtype) or needs_i8_conversion(dtype):
+    # not using needs_i8_conversion because that includes period
+    if (
+        is_integer_dtype(dtype)
+        or is_datetime64_any_dtype(dtype)
+        or is_timedelta64_dtype(dtype)
+    ):
         dtype_sum = np.float64
     elif is_float_dtype(dtype):
         dtype_sum = dtype
diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index 0255759513e28..75afc59382a75 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -885,16 +885,20 @@ def test_mean_datetimelike_numeric_only_false(self):
                 "A": np.arange(3),
                 "B": pd.date_range("2016-01-01", periods=3),
                 "C": pd.timedelta_range("1D", periods=3),
-                "D": pd.period_range("2016", periods=3, freq="A"),
             }
         )
 
+        # datetime(tz) and timedelta work
         result = df.mean(numeric_only=False)
-        expected = pd.Series(
-            {"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"], "D": df.loc[1, "D"]}
-        )
+        expected = pd.Series({"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"]})
         tm.assert_series_equal(result, expected)
 
+        # mean of period is not allowed
+        df["D"] = pd.period_range("2016", periods=3, freq="A")
+
+        with pytest.raises(TypeError, match="reduction operation 'mean' not allowed"):
+            df.mean(numeric_only=False)
+
     def test_stats_mixed_type(self, float_string_frame):
         # don't blow up
         float_string_frame.std(1)