BUG: Add test for to_numpy() handling of NaT and NaN values

Author: fbourgey

pandas/tests/frame/methods/test_to_numpy.py

@@ -1,10 +1,7 @@
 import numpy as np
 import pytest
 
-from pandas import (
-    DataFrame,
-    Timestamp,
-)
+from pandas import DataFrame, Timestamp, date_range, NaT
 import pandas._testing as tm
 
 
@@ -41,3 +38,37 @@ 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)