Bug cov nat

pandas/core/frame.py

@@ -93,6 +93,7 @@
     is_array_like,
     is_bool_dtype,
     is_dataclass,
+    is_datetime64_any_dtype,
     is_dict_like,
     is_float,
     is_float_dtype,
@@ -103,6 +104,7 @@
     is_list_like,
     is_scalar,
     is_sequence,
+    is_timedelta64_dtype,
     needs_i8_conversion,
     pandas_dtype,
 )
@@ -11350,6 +11352,13 @@ def cov(
         c -0.150812  0.191417  0.895202
         """
         data = self._get_numeric_data() if numeric_only else self
+        dtypes = [blk.dtype for blk in self._mgr.blocks]
+        if any(is_datetime64_any_dtype(d) or is_timedelta64_dtype(d) for d in dtypes):
+            msg = (
+                "DataFrame contains columns with dtype datetime64 "
+                "or timedelta64, which are not supported for cov."
+            )
+            raise TypeError(msg)
         cols = data.columns
         idx = cols.copy()
         mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)

pandas/core/internals/managers.py

@@ -1800,6 +1800,8 @@ def as_array(
                 arr = np.asarray(blk.values, dtype=dtype)
             else:
                 arr = np.array(blk.values, dtype=dtype, copy=copy)
+            if passed_nan and blk.dtype.kind in ["m", "M"]:
+                arr[isna(blk.values)] = na_value
 
             if not copy:
                 arr = arr.view()
@@ -1865,6 +1867,8 @@ def _interleave(
             else:
                 arr = blk.get_values(dtype)
             result[rl.indexer] = arr
+            if na_value is not lib.no_default and blk.dtype.kind in ["m", "M"]:
+                result[rl.indexer][isna(arr)] = na_value
             itemmask[rl.indexer] = 1
 
         if not itemmask.all():

pandas/tests/frame/methods/test_to_numpy.py

@@ -3,7 +3,9 @@
 
 from pandas import (
     DataFrame,
+    NaT,
     Timestamp,
+    date_range,
 )
 import pandas._testing as tm
 
@@ -41,3 +43,37 @@ def test_to_numpy_mixed_dtype_to_str(self):
         result = df.to_numpy(dtype=str)
         expected = np.array([["2020-01-01 00:00:00", "100.0"]], dtype=str)
         tm.assert_numpy_array_equal(result, expected)
+
+    def test_to_numpy_datetime_with_na(self):
+        # GH #53115
+        dti = date_range("2016-01-01", periods=3)
+        df = DataFrame(dti)
+        df.iloc[0, 0] = NaT
+        expected = np.array([[np.nan], [1.45169280e18], [1.45177920e18]])
+        assert np.allclose(
+            df.to_numpy(float, na_value=np.nan), expected, equal_nan=True
+        )
+
+        df = DataFrame(
+            {
+                "a": [Timestamp("1970-01-01"), Timestamp("1970-01-02"), NaT],
+                "b": [
+                    Timestamp("1970-01-01"),
+                    np.nan,
+                    Timestamp("1970-01-02"),
+                ],
+                "c": [
+                    1,
+                    np.nan,
+                    2,
+                ],
+            }
+        )
+        arr = np.array(
+            [
+                [0.00e00, 0.00e00, 1.00e00],
+                [8.64e04, np.nan, np.nan],
+                [np.nan, 8.64e04, 2.00e00],
+            ]
+        )
+        assert np.allclose(df.to_numpy(float, na_value=np.nan), arr, equal_nan=True)

pandas/tests/frame/test_reductions.py

@@ -1917,6 +1917,39 @@ def test_df_empty_nullable_min_count_1(self, opname, dtype, exp_dtype):
         expected = Series([pd.NA, pd.NA], dtype=exp_dtype, index=Index([0, 1]))
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize(
+        "data",
+        [
+            {"a": [0, 1, 2], "b": [pd.NaT, pd.NaT, pd.NaT]},
+            {"a": [0, 1, 2], "b": [Timestamp("1990-01-01"), pd.NaT, pd.NaT]},
+            {
+                "a": [0, 1, 2],
+                "b": [
+                    Timestamp("1990-01-01"),
+                    Timestamp("1991-01-01"),
+                    Timestamp("1992-01-01"),
+                ],
+            },
+            {
+                "a": [0, 1, 2],
+                "b": [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.NaT],
+            },
+            {
+                "a": [0, 1, 2],
+                "b": [
+                    pd.Timedelta("1 days"),
+                    pd.Timedelta("2 days"),
+                    pd.Timedelta("3 days"),
+                ],
+            },
+        ],
+    )
+    def test_df_cov_pd_nat(self, data):
+        # GH #53115
+        df = DataFrame(data)
+        with pytest.raises(TypeError, match="not supported for cov"):
+            df.cov()
+
 
 def test_sum_timedelta64_skipna_false():
     # GH#17235