ENH: IntegerArray.astype(dt64)

pandas/_libs/tslib.pyx

@@ -14,7 +14,7 @@ PyDateTime_IMPORT
 
 
 cimport numpy as cnp
-from numpy cimport float64_t, int64_t, ndarray
+from numpy cimport float64_t, int64_t, ndarray, uint8_t
 import numpy as np
 cnp.import_array()
 
@@ -351,7 +351,6 @@ def format_array_from_datetime(
 
 def array_with_unit_to_datetime(
     ndarray values,
-    ndarray mask,
     object unit,
     str errors='coerce'
 ):
@@ -373,8 +372,6 @@ def array_with_unit_to_datetime(
     ----------
     values : ndarray of object
          Date-like objects to convert.
-    mask : boolean ndarray
-         Not-a-time mask for non-nullable integer types conversion, can be None.
     unit : object
          Time unit to use during conversion.
     errors : str, default 'raise'
@@ -395,6 +392,7 @@ def array_with_unit_to_datetime(
         bint need_to_iterate = True
         ndarray[int64_t] iresult
         ndarray[object] oresult
+        ndarray mask
         object tz = None
 
     assert is_ignore or is_coerce or is_raise
@@ -404,9 +402,6 @@ def array_with_unit_to_datetime(
             result = values.astype('M8[ns]')
         else:
             result, tz = array_to_datetime(values.astype(object), errors=errors)
-        if mask is not None:
-            iresult = result.view('i8')
-            iresult[mask] = NPY_NAT
         return result, tz
 
     m = cast_from_unit(None, unit)
@@ -419,9 +414,8 @@ def array_with_unit_to_datetime(
         if values.dtype.kind == "i":
             # Note: this condition makes the casting="same_kind" redundant
             iresult = values.astype('i8', casting='same_kind', copy=False)
-            # If no mask, fill mask by comparing to NPY_NAT constant
-            if mask is None:
-                mask = iresult == NPY_NAT
+            # fill by comparing to NPY_NAT constant
+            mask = iresult == NPY_NAT
             iresult[mask] = 0
             fvalues = iresult.astype('f8') * m
             need_to_iterate = False

pandas/core/arrays/integer.py

@@ -13,6 +13,7 @@
 from pandas.core.dtypes.cast import astype_nansafe
 from pandas.core.dtypes.common import (
     is_bool_dtype,
+    is_datetime64_dtype,
     is_float,
     is_float_dtype,
     is_integer,
@@ -469,6 +470,8 @@ def astype(self, dtype, copy: bool = True) -> ArrayLike:
         if is_float_dtype(dtype):
             # In astype, we consider dtype=float to also mean na_value=np.nan
             kwargs = dict(na_value=np.nan)
+        elif is_datetime64_dtype(dtype):
+            kwargs = dict(na_value=np.datetime64("NaT"))
         else:
             kwargs = {}
 

pandas/core/tools/datetimes.py

@@ -323,15 +323,13 @@ def _convert_listlike_datetimes(
         # GH 30050 pass an ndarray to tslib.array_with_unit_to_datetime
         # because it expects an ndarray argument
         if isinstance(arg, IntegerArray):
-            # Explicitly pass NaT mask to array_with_unit_to_datetime
-            mask = arg.isna()
-            arg = arg._ndarray_values
+            result = arg.astype(f"datetime64[{unit}]")
+            tz_parsed = None
         else:
-            mask = None
 
-        result, tz_parsed = tslib.array_with_unit_to_datetime(
-            arg, mask, unit, errors=errors
-        )
+            result, tz_parsed = tslib.array_with_unit_to_datetime(
+                arg, unit, errors=errors
+            )
 
         if errors == "ignore":
             from pandas import Index

pandas/tests/arrays/test_array.py

@@ -222,6 +222,8 @@ def test_array_copy():
         # integer
         ([1, 2], IntegerArray._from_sequence([1, 2])),
         ([1, None], IntegerArray._from_sequence([1, None])),
+        ([1, pd.NA], IntegerArray._from_sequence([1, pd.NA])),
+        ([1, np.nan], IntegerArray._from_sequence([1, np.nan])),
         # string
         (["a", "b"], StringArray._from_sequence(["a", "b"])),
         (["a", None], StringArray._from_sequence(["a", None])),

pandas/tests/arrays/test_integer.py

@@ -633,6 +633,15 @@ def test_astype_specific_casting(self, dtype):
         expected = pd.Series([1, 2, 3, None], dtype=dtype)
         tm.assert_series_equal(result, expected)
 
+    def test_astype_dt64(self):
+        # GH#32435
+        arr = pd.array([1, 2, 3, pd.NA]) * 10 ** 9
+
+        result = arr.astype("datetime64[ns]")
+
+        expected = np.array([1, 2, 3, "NaT"], dtype="M8[s]").astype("M8[ns]")
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_construct_cast_invalid(self, dtype):
 
         msg = "cannot safely"

pandas/tests/frame/test_dtypes.py

@@ -505,7 +505,7 @@ def test_df_where_change_dtype(self):
 
     @pytest.mark.parametrize("dtype", ["M8", "m8"])
     @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
-    def test_astype_from_datetimelike_to_objectt(self, dtype, unit):
+    def test_astype_from_datetimelike_to_object(self, dtype, unit):
         # tests astype to object dtype
         # gh-19223 / gh-12425
         dtype = f"{dtype}[{unit}]"